One week after DeepSeek’s disruptive introduction, we are back with more news and insights about the lasting impact of this open AI model.
Join Paul Roetzer and Mike Kaput as they explore the latest advancements in AI, including OpenAI’s mini-o3 and deep research developments, the U.S. Copyright Office’s pivotal report on AI-generated works, Meta’s response to DeepSeek, funding news, and more.
Listen or watch below—and see below for show notes and the transcript.
00:07:37 — DeepSeek’s Fallout
00:17:04 — OpenAI Releases o3-mini and deep research
00:40:37 — US Copyright Office Hints AI-Influenced Work Is Protected
00:47:02 — OpenAI Seeks Silicon Valley’s Largest-Ever Funding Round
00:51:43 — How Meta Is Responding to DeepSeek
00:54:36 — Which AI Should You Use? An Answer
01:00:23 — a16z AI Voice Agent Market Analysis
01:03:19 — Listener Questions
01:07:30 — AI Use Cases
01:12:12 — AI Funding and Product Updates
DeepSeek’s Fallout
The fallout effects from DeepSeek are still rippling through Silicon Valley and the wider AI ecosystem. DeepSeek is a Chinese AI lab that has created open weight models that are allegedly as powerful as OpenAI products (and others) for a fraction of the cost.
The impact of DeepSeek’s latest releases, V3 (a competitor to GPT-4o) and R-1, a competitor to reasoning models, was immediate and dramatic.
Last week, Nvidia saw its stock plunge nearly 17% in a single day, erasing roughly $600 billion in market value—reportedly the largest single-day loss for any company in U.S. stock market history. The broader tech sector also saw steep declines as investors questioned the massive AI investments being made by companies like Microsoft, Meta, and Google.
OpenAI and Microsoft are also now investigating whether DeepSeek may have used data “distilled” from OpenAI's systems. David Sacks, President Trump's AI czar, claims there is "substantial evidence" that DeepSeek used OpenAI's models to train its own—an allegation DeepSeek has not directly addressed.
The situation has also triggered security concerns. Hundreds of companies and government agencies are now blocking access to DeepSeek's services over fears about data security and Chinese government access.
The Irish and Italian data protection authorities have launched investigations into how DeepSeek handles European users' data.
OpenAI o3 & Deep Research
OpenAI has just released o3-mini, a new reasoning model that is designed to excel at STEM tasks like coding, mathematics, and science, while being more efficient and cost-effective than its predecessors.
o3-mini can search the web and will eventually show its thinking while it goes about accomplishing tasks.
OpenAI is rolling out o3-mini across its entire product line—it's now available in ChatGPT for both free and paid users, as well as through their API for developers. Enterprise customers will gain access next week. ChatGPT Plus and Pro users also get access to a model called o3-mini-high, which thinks harder and gives better answers.
The company also released a new capability in ChatGPT called “deep research.” Deep research is designed to function as an autonomous research agent that can spend anywhere from 5 to 30 minutes investigating complex questions and delivering comprehensive answers.
Deep research shows you exactly how it arrived at its conclusions through a detailed sidebar that tracks its research process and cites its sources. Think of it as having a research analyst working alongside you, methodically gathering and synthesizing information while keeping detailed notes of their process.
Users can input questions through text, images, or even upload entire documents like PDFs and spreadsheets for context. The system then works independently to navigate through information, adjusting its approach based on what it finds.
OpenAI acknowledges that the system can occasionally hallucinate facts or struggle to distinguish between authoritative information and rumors. Access is initially limited to Pro subscribers, who'll get up to 100 queries per month.
US Copyright Office Hints AI-Influenced Work Is Protected
The US Copyright Office has issued a landmark report that provides updated guidance on how copyright law applies to AI-generated works.
The report, titled "Copyright and Artificial Intelligence, Part 2: Copyrightability," comes after extensive consultation with over 10,000 commenters from all 50 states and 67 countries.
Some of the Office's core findings are as follows:
While the report offers more clarity on several key questions, it also acknowledges that standards may need to evolve as AI technology advances.
The Office plans to continue monitoring developments and will provide ongoing guidance through registration practices and updates to its official manual.
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 POD100 to save $100 on a membership.
This episode is also brought to you by our AI for Writers Summit:
Join us and learn how to build strategies that future-proof your career or content team, transform your storytelling, and enhance productivity without sacrificing creativity.
The Summit takes place virtually from 12:00pm - 5:00pm ET on Thursday, March 6. There is a free registration option, as well as paid ticket options that also give you on-demand access after the event.
To register, go to www.aiwritersummit.com
Disclaimer: This transcription was written by AI, thanks to Descript, and has not been edited for content.
[00:00:00] Paul Roetzer: I expect that people are going to get used to the Chinese labs, putting out advancements and models, doing things more efficiently, pushing the American labs to maybe release stuff before they would normally be ready. And I think we're just going to kind of in a week or two, this will be the new world and we'll be used to it.
[00:00:16] Paul Roetzer: Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Roetzer. I'm the founder and CEO of Marketing AI Institute, and I'm your host. Each week, I'm joined by my co-host and Marketing AI Institute 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.
[00:00:45] Paul Roetzer: Join us as we accelerate AI literacy for all.
[00:00:52] Paul Roetzer: Welcome to episode 134 of the Artificial Intelligence Show. I'm your host, Paul Roetzer, along with my co-host, Mike Kaput. [00:01:00] We are. Recording this on Monday morning, February 3rd, 11 a.m. Eastern Time, approximately, I don't know, 15 hours after OpenAI dropped a Sunday night AI model on us. It was a unique end to the weekend, as we'll talk plenty about the new Deep Research model, same name as Google uses.
[00:01:22] Paul Roetzer: So it was a busy Sunday night, busy Monday morning getting ready for this one. So we've got a lot to cover as always. a couple quick notes before we get started. we have dedicated artificial intelligence show social media accounts if you're not following them. So on X or Twitter, it is at AIShowPod.
[00:01:41] Paul Roetzer: And on YouTube, it is at AIShowPod. So you can find us on YouTube and Twitter. And we also have, we post on TikTok and Instagram and all those things, but those are the two main dedicated accounts if you want to follow along on those channels. Also, just a quick note and thanks to everyone for the response on the AI [00:02:00] Literacy Project.
[00:02:00] Paul Roetzer: I have my inbox, email inbox, LinkedIn inbox, and everything in between has been flooded with, amazing notes and, people volunteering to get involved in the Literacy Project. And if I haven't gotten back to you I apologize, I'm trying to work through all of those, outreach communications, and we're grateful for everyone for your time.
[00:02:21] Paul Roetzer: You know, wanting to kind of explore that if you're not sure what I'm talking about, you can go back and listen to, was it 133, Mike, when we talked about the Literacy Project, or 132, I don't know, one of the shows last week.
[00:02:30] Mike Kaput: It was 132, right?
[00:02:32] Paul Roetzer: Okay, yeah, so, or just go to literacyproject. ai and you can learn more about the AI Literacy Project.
[00:02:38] Paul Roetzer: And then, just as always, we're very grateful for our audience. We had, Pretty remarkable week last week in terms of the numbers, so we did two weekly shows, which is not the norm, and as a result, we broke 24 hour records for downloads, the 7 day record for downloads, and the 30 day trendline actually saw a 30 percent jump from our previous high, so [00:03:00] we're, like I said, always grateful for our growing audience and the people who listen regularly and support us.
[00:03:05] Paul Roetzer: Thank you. And the people that reach out to us and kind of let us know, you know, how the podcast is impacting them. So, we really appreciate all that. And we'll keep doing our best to keep bringing you this news every week and trying to condense it into the hour, hour 20 minutes or so that we do each week.
[00:03:20] Paul Roetzer: All right. So, this week is, episode is brought to us by the AI Mastery Membership Program. We've been talking a lot about this lately. Incredible response to this program as well. We've seen some huge jumps in number of members that are joining. Again, if you go back to episode 132, I sort of explained kind of the vision and roadmap for the iMastery membership program.
[00:03:42] Paul Roetzer: We made some major changes on January 24th that took the existing mastery membership and enhanced it by bundling in our piloting and scaling AI. Certification courses are now part of that membership for the same fee. And then we announced phase two, which is coming in spring of 2025, [00:04:00] just a few short months away.
[00:04:01] Paul Roetzer: We are very aggressively working on this. We're going to be launching a new AI Academy platform and user experience. We're going to be dramatically expanding the courses and professional certificates and building a turnkey AI Academy solution for businesses. So we've had a ton of people outreach about the business accounts.
[00:04:18] Paul Roetzer: We've had some amazing conversations just in the last week with enterprises that are looking to build their AI literacy into their organizations, and that's going to be, you know, things like our AI Fundamentals course series, our Gen AI app, AI app course series, which can be like weekly reviews of, of products and tools and platforms, AI for Industries course series, AI for Departments.
[00:04:39] Paul Roetzer: Those are all coming this spring, so you can go to smarterx. ai slash, AI. Dash mastery or just go to smarterx. ai and click on education and you'll see I have mastery right there. And for our podcast listeners, we have a promo code of pod 100 pod 100 and that gets you 100 off of the membership. [00:05:00] also the AI for writers summit, we've been talking a lot about that.
[00:05:02] Paul Roetzer: This is our third one, Mike, I think. Yeah. Third annual summit. We had, 4, 500 last year, I think, from 90 countries, so this has been a huge event for us the first two years. I think a lot of writers and creators are really trying to understand the moment we find ourselves in and figure out what all these advanced models mean to the writing profession and copywriters within enterprises.
[00:05:25] Paul Roetzer: There's a lot of uncertainty ahead, and we're trying to kind of piece it together for everyone. This event is going to be Thursday, March 6th. It is a virtual summit from noon to five Eastern time. There is a free registration option, so you can check that out. the agenda is now live. we're going to have, my opening keynote is going to be the Move 37 moment for writers and creators.
[00:05:45] Paul Roetzer: Kind of like telling the story of AlphaGo and what we learned from that and how it's going to impact writers and creators. we have a session on AI, copyright, and IP, which is extremely critical. We're going to talk about some updates to U. S. copyright in today's episode. [00:06:00] Got Mastering AI Prompting. Is that you, Mike?
[00:06:02] Paul Roetzer: Or no, that's Andy Crestodino. He's done, I think, Mastering AI Prompting. Mike's doing AI powered research. How to transform, transforming how writers discover and create, which the new open AI model, Mike, certainly great timing on that. Yeah. And then, we still have a yet to be announced keynote. And then, and ask us anything at the end.
[00:06:21] Paul Roetzer: So, AIWriterSummit. com. Again, that is AIWriterSummit. com. you can also find this on the Marketing AI Institute site if you're bouncing around on there, just click on events. And so that again is coming up March 6th. There is a free option. Check that out. And then final reminder, the submission to speak at MAICON, our 6th Annual Marketing AI Conference, October 14th to the 16th in Cleveland.
[00:06:46] Paul Roetzer: Registration is open for that event now, but we are also accepting speaker applications. And this is like kind of on a rolling basis through February 28th, you have to get in, but I would get in as quick as possible because we've been getting a lot of submissions and we're going to be kind of [00:07:00] filling the agenda.
[00:07:01] Paul Roetzer: As we go. So check that out At Macon ai, that's M-A-I-C-O-N ai. On the homepage there's a button to submit your speaker application, so if you're interested in speaking, check that out. If you're interested in interested in attending, you can get registered now at the lowest pricing. I think the price goes up at the end of each month, so, take advantage of that.
[00:07:21] Paul Roetzer: Okay, so, we'll get into deep seek a little bit 'cause it continued to dominate the news at least until Sunday night when OpenAI dropped. A model that, you know, no one was really expecting until that day. So, Mike, kick us off with what's the latest on DeepSeek and the implications.
[00:07:37] Mike Kaput: Sure thing, Paul. So, the fallout effects from DeepSeek are still rippling through Silicon Valley and the wider AI ecosystem.
[00:07:46] Mike Kaput: We talked a bunch about DeepSeek last week. This is an update. Chinese AI lab that has created open weight models that are allegedly as powerful as open AI products and the products of other [00:08:00] model providers for what they believe is a fraction of the cost of training those other closed lab products.
[00:08:07] Mike Kaput: Now the impact of DeepSeq's latest releases, which V3, which is a competitor to GPT 4. 0 and R1, a competitor to reasoning models. The impact of these was pretty immediate and dramatic. So, as we were reporting on this last week, on Monday when we recorded, NVIDIA was seeing its stock plunge. It ended up down nearly 17 percent in a single day, erasing roughly 600 billion in market value.
[00:08:37] Mike Kaput: This is reportedly the largest single day loss for any company in U. S. stock market history. The broader tech sector saw steep declines as investors questioned the massive AI investments being made by these companies to train AI models when apparently DeepSeek is able to do it on the cheap. So companies like Microsoft, Meta, and Google are facing a lot of [00:09:00] investor scrutiny.
[00:09:01] Mike Kaput: OpenAI and Microsoft are also now investigating whether DeepSeek may have used data distilled from OpenAI's systems. David Sachs, which is, who is President Trump's AI czar, claims that there is, quote, substantial evidence that DeepSeek used OpenAI's models to train its own, an allegation DeepSeek has not directly addressed.
[00:09:23] Mike Kaput: The idea here, which David Sacks is not the only person to question, is did they actually train their, their models for as cheap as they say and as quickly as they did without any other help? And it sounds like there are a lot of questions if that's actually the case. Now, DeepSeek has also triggered security concern.
[00:09:42] Mike Kaput: Hundreds of companies and government agencies are now blocking access to it over fears about data security and Chinese government access. The Irish and Italian data protection authorities have also launched investigations into how DeepSeek handles European security. [00:10:00] So, Paul, first, let's talk about the fallout in the markets.
[00:10:04] Mike Kaput: Like, this is something a lot of listeners, I would imagine, will immediately be seeing and or feeling. NVIDIA is still kind of way down. Like, is DeepSea going to change how investors think about the economics of these big AI companies?
[00:10:19] Paul Roetzer: I don't think so. I think it was mostly an overreaction because people didn't really understand what it was or what the implications were and it's kind of how the market works.
[00:10:28] Paul Roetzer: the thing I'd kind of, again, I'd very, very careful not, I'm not providing investing advice. I say this every, every single time, but I had a lot of friends reach out to me asking what in the world is going on that day by text. And what I generally Guided people is, you know, if, if you're worried about short term, yeah, there, you could see these drops and they may actually sustain for a little bit.
[00:10:52] Paul Roetzer: But if you're thinking long term, there's no fundamental change. If anything, this creates greater demand for GPUs from [00:11:00] NVIDIA because it just proves out the ability to build intelligence more efficiently. It doesn't mean you're going to build less of it or like require less computing power. So I just, I just really saw it as, as a.
[00:11:11] Paul Roetzer: Significant overreaction, not surprising. I do think that had it been an American lab that had done it, there would have been no reaction. If not, NVIDIA might've actually been up that day, which is part of kind of how I assess this. So I think the key is it opens up these kind of unknowns and, and Wall Street just doesn't like uncertainty and unknowns.
[00:11:32] Paul Roetzer: And so they had to kind of let the dust settle a little bit. and I think it was pretty apparent right away that there, there was Everything wasn't as it appeared, like it wasn't the, I think we talked about this on last week's show, like the training run probably could have been in the six million range, but that doesn't mean that's what they spent to build this thing.
[00:11:52] Paul Roetzer: It's more likely they spent over a billion dollars on the infrastructure and the chips and everything that they built in to enable it, [00:12:00] but it still makes it, you know, Significant, like Zuckerberg said, you know, he acknowledged it, NVIDIA released a statement acknowledging it, Sam Altman tweeted about it saying it was an impressive model.
[00:12:11] Paul Roetzer: So people took notice and it was like a note, it was certainly a noteworthy event in the timeline of advancements of AI. I think the fact that they show the chain of thought so clearly, which is still wild. Like if you look on Twitter and you see some of the examples, I haven't personally used the model, but if you Look at some of the examples where it's showing you what it's thinking as it's like answering.
[00:12:36] Paul Roetzer: It's wild. Like it's kind of how the human mind works. So you can see it kind of bouncing around and and OpenAI has, to date as well as Google and Anthropocene. They have all avoided showing that level of chain of thought, I believe, for security purposes like I think they see that as somewhat dangerous to Be able to understand that deeply what the [00:13:00] model is doing and thinking.
[00:13:01] Paul Roetzer: so you're seeing kind of some other stuff where now these other model companies are starting to trickle out more advanced stuff. So, I don't know. We'll, we'll see what happens this week. It's going to be hard to get a read on the markets this week, obviously, because of the tariff war that was started over the weekend and, you know, now the U.
[00:13:17] Paul Roetzer: S. is imposing tariffs on Mexico, although I just saw that got delayed a month now. We'll see if the one in Canada goes through. It's going to be 25 percent on Canada, but. So, you know, they were having phone calls today to try and negotiate that down. So the markets are running haywire right now because of tariff wars, not because of deep seek and other stuff.
[00:13:35] Paul Roetzer: So you can't read into like NVIDIA stock this week and figure anything out. So the last note I'll make here is Andrew O who we've talked about many times, you know, one of the founders of the Google Brain team. Creator of Coursera, he has deep learning ai, he, he heavily, involved in, in the current and past of ai.
[00:13:54] Paul Roetzer: So he tweeted, the buzz over deep seek this week crystallized for many people. A few important trends that have been [00:14:00] happening in plain sight one, and these are important notes. China's catching up to the US generative AI with implications for the AI supply chain. Two open weight models are commoditizing the foundation model layer.
[00:14:12] Paul Roetzer: which creates opportunities for application builders. Now what that means there is like, if you think about OpenAI and Google, how they have kind of these, you know, more closed proprietary models. What they're saying is like, these open weight models are catching up to the biggest models out there and it's going to commoditize like the value of these things.
[00:14:31] Paul Roetzer: And then the third was scaling up isn't the only path to high progress. Despite the massive focus and hype around processing power, algorithmic innovations are rapidly pushing training costs down. And then the final note I'll make here is related. Sam Altman actually did a Reddit conversation. This was on like Saturday or Friday.
[00:14:49] Paul Roetzer: I don't remember what day this was. and Sam said. OpenAI has, quote, been on the wrong side of history and need to figure out a new open source strategy. [00:15:00] He added that not everyone at OpenAI shares this view and it's also not our highest priority. So, again, the deep seek moment may have been the trigger for Sam to finally step up and say, We're not, we're not doing everything I think we should be doing with open source, which is what, you know, Elon Musk's beef has been in part with Sam all these years.
[00:15:21] Paul Roetzer: So, yeah, just, you know, it's a continuing story. I think it's gonna probably fade pretty quick. I expect that people are gonna get used to the Chinese labs putting out, you know, advancements in models, doing things more efficiently, pushing the American labs to maybe release stuff before they would normally be ready.
[00:15:39] Paul Roetzer: And I think we're just going to kind of in a week or two, this will be the new world and we'll be used to it.
[00:15:45] Mike Kaput: And just to kind of wrap this up with a bow. So it's clear there is a moment here. This is deeply important, but it sounds like also just contextually, You know, people, a lot of people [00:16:00] were under that false impression that, oh, they did for six million dollars what OpenAI does.
[00:16:04] Mike Kaput: That's just not true.
[00:16:05] Paul Roetzer: I think it's pretty safe to say that that those media headlines were misrepresenting what was actually going on. And I think you're right, like, you know, if you look back five years from now, I would imagine if you were making a timeline of significant milestones in AI development over, you know, that five year period, the deep seek moment was probably on that timeline.
[00:16:26] Paul Roetzer: Like, that's how I kind of think about why we're talking about this for a second week in a row, is I think, When we look back, it'll have been a very significant moment, and I, and one of the things that may trigger that I don't think is necessarily a good thing is I think OpenAI and others are going to accelerate the release of more advanced models as a result of this to stay ahead when they have yet to solve how to keep those models safe.
[00:16:53] Paul Roetzer: And I think that's going to be a major problem as we move throughout this year.
[00:16:58] Mike Kaput: As we have said many times, it's [00:17:00] not a good year for the AI doomers. No.
[00:17:04] Mike Kaput: Alright, so speaking of kind of effects of DeepSeek on the market, so the next big topic, we're going to talk about both OpenAI's O3 Mini and Deep Research.
[00:17:16] Mike Kaput: So we're first going to tee up O3 a bit, Paul, and talk about that, and then discuss what's going on with this. Pretty cool new feature called Deep Research that they have released. So first up, literally just days after all the deep seat drama, OpenAI has released Open, has released O3 Mini, which is a new reasoning model that is designed to excel at STEM tasks like coding, math, and science, while being more efficient and cost effective than its predecessors.
[00:17:45] Mike Kaput: O3 Mini can search the web and will eventually show its thinking while it Now, in a sign of perhaps democratization of access, OpenAI is actually making O3 Mini available to [00:18:00] free ChatGPT users. OpenAI is also rolling out O3 Mini across its entire product line. It is available not only in just free ChatGPT, but with higher limits in the paid versions.
[00:18:11] Mike Kaput: As well as through the API, apparently enterprise customers will soon gain access as well. And technical evaluations show that this model matches or exceeds the performance of previous models on certain STEM benchmarks while delivering responses 24 percent faster. ChatGPT Plus and Pro users also get access to a model called O3 Mini High, which thinks harder and gives better answers.
[00:18:39] Mike Kaput: So Sam Altman posted on X about O3 Mini, quote, A lot of people prefer this to O1, and it's just the mini model. Now we work on the big brother. So Paul, let's first talk a little bit about O3. Like, this is, seems like an obvious response to DeepSeek in terms of moving this release forward, offering [00:19:00] significantly more intelligence for far less cost at much greater speed.
[00:19:04] Mike Kaput: Seems like no matter how quickly these models come out, that's the trend to bet on, right? Is that we're going to see more things like O3 Mini?
[00:19:11] Paul Roetzer: Yeah, it's going to be harder to keep up with the model releases. It's, it, I mean, it's seriously getting out of control. so the first thing I note here is like, I would imagine like some other people in our audience, my initial reaction was, Oh my God, the name.
[00:19:26] Paul Roetzer: Like, it's just, so if you go into ChatGPT right now. You can choose depending on whether you're in your personal account or business account or pro account. Like now you have differences in the accounts. In the standard paid account, there's seven models to pick from and very little guidance as to like, which one is like, okay, fast responses, better responses, like, well, I don't, how am I supposed to choose?
[00:19:50] Paul Roetzer: I want better responses, but I guess I want the faster response. Like, I don't know. And it leaves the user to actually like be testing the outputs of these different things, trying to figure out, and then you can't even tell the [00:20:00] difference. Yeah. So. You know, I think that they're really starting to get in their own way here in terms of the average user adoption, the non developer user adoption.
[00:20:09] Paul Roetzer: Because I really think developers are, and maybe like extreme power users, non developer power users, they're the only people who care about all these model choices. As I've said many times, like people just want to use ChatGPT. They don't want to have to pick between seven models. So, That being said, I did my first test with 03 mini high was actually on a related topic.
[00:20:31] Paul Roetzer: I said, how could OpenAI simplify model choice for non developers using ChatGPT? Right now, when a user creates a new chat, there are seven models to choose from. Most ChatGPT users would struggle to understand the difference between these models and when to use which one. What would be a better way to handle this?
[00:20:46] Paul Roetzer: And it actually had like two decent, like actually pretty obvious answers. One is smart selection. So that the interface just picks it for you. You put in your prompt and it picks it. And the other is to like hide all of these other options under advanced options, which is like I'm an advanced one. I'm a developer.
[00:20:59] Paul Roetzer: I want my [00:21:00] choices. The average user doesn't want those things. also worth note that Sam has teased there's something else coming for O3 Mini. This week, and the deep research model from last night wasn't it, he said on Twitter. So there's something else coming, there. But then the really interesting thing happened last night.
[00:21:19] Paul Roetzer: So this, again, this is, we're talking on Monday morning. This is Sunday night. They were in Japan, I think, is where he released this. They must, they had like an event. They were live streaming from Japan. But they released Deep Research, which, yes, if you're following along at home, is the same name as Google, but they didn't uppercase it, I'm guessing, so they didn't run into, like, legal trademark issues, maybe?
[00:21:41] Paul Roetzer: So they just, like, lowercased Deep Research? So, when they finally name something that isn't like 03 mini high, they use somebody else's name. so Mike, I know you dove into a little bit of the background on like what deep research is, so why don't you give us the rundown there and then I'll offer a [00:22:00] little perspective on it.
[00:22:02] Mike Kaput: Alright, so this is a new capability in ChatGPT called Deep Research. It's like an option you can select. When you are actually chatting with ChatGPT. So not like a separate standalone product, but what this is, is it's designed to function as an autonomous research agent that can go spend a bunch of time investigating complex questions and delivering comprehensive answers.
[00:22:27] Mike Kaput: So deep research will show you exactly how it arrived there. at its conclusions through a detailed sidebar that tracks its research progress and cites its sources. So think of this basically like a research analyst working alongside you, gathering and synthesizing information while keeping detailed notes about their process.
[00:22:47] Mike Kaput: So you can input questions through text, images, or even upload documents like PDFs and spreadsheets for context. Then the system works independently to navigate through all that [00:23:00] information, adjusting its approach based on what it finds. In the coming weeks, OpenAI plans to enhance this Capabilities output with embedded images and data visualizations.
[00:23:11] Mike Kaput: Now, right now, the access is initially limited to pro subscribers who get up to 100 queries per month because it's apparently very computationally intensive. Apparently though, plus team and enterprise users will gain access later once there's a more efficient version. that they are using for those accounts.
[00:23:30] Mike Kaput: So, Paul, I guess, like, you know, obviously his name is infuriating at this point. It is triggering for me that, like, we couldn't call it anything else. Even we could use research still. Just pick another. At least they didn't do, like,
[00:23:42] Paul Roetzer: Deep Seeker. Like, I could have seen
[00:23:44] Mike Kaput: it. Yeah. Oh my god, that would be a nightmare.
[00:23:49] Mike Kaput: but. Basically, the kind of difference, as far as I can tell right now, is that both this and Google's deep research aim to kind of do the same thing, which is go [00:24:00] autonomously do research on topics for you. But I think Ethan Mollick posted a really good kind of breakdown about how they're different. So he says, OpenAI's deep research is very good, unlike Google's version, which is a summarizer of many sources.
[00:24:13] Mike Kaput: OpenAI is more like engaging and opinionated, often almost PhD level researcher, who follows the lead. So, this. It basically seems like this is a much more fluid, autonomous kind of research partner versus something that's going and creating like a whole research brief for you, though they are both producing briefs.
[00:24:34] Mike Kaput: It seems like this one might be more useful to more converse with in some ways, but we'll time will tell as we test it out.
[00:24:41] Paul Roetzer: Yeah, I don't know that the average user is going to like immediately be able to tell the difference. I, yeah, I did test it and I don't know that I would agree with Ethan's analysis.
[00:24:50] Paul Roetzer: Now, Ethan's had early access to this and done way more tests, but in my very limited test, I don't know, very similar outputs, like it wasn't a dramatic difference between one or the [00:25:00] other. so yeah, all right. So I have a few thoughts here. I kind of like wander on this one for a minute. My initial reaction to this was that the AI timeline is accelerating.
[00:25:11] Paul Roetzer: So our path to AGI or whatever we want to call it, the delta between what these models are capable of. And society's understanding and preparedness grew again last night. Like, I think this is a advancement in capabilities. It is our first, interaction outside of OpenAI with the full O3 model. So the, this is not O3 mini high, this is the full O3.
[00:25:38] Paul Roetzer: Now there's an O3 pro coming also that's going to be even more powerful. But this is your only chance to currently use the full O3 model is to actually use this product. So you can kind of use the most advanced reasoning model they have. I think perplexity is cooked. Like, I've said this the last couple months that I was starting to become Less [00:26:00] bullish on perplexity as a sustainable company.
[00:26:02] Paul Roetzer: And I think they're done. Like they're there because Google already is better than them at this. OpenAI is obviously going to be better and they're only going to accelerate the development of this. Everybody else is going to build the same thing. And Anthropic's going to have a deep research product.
[00:26:17] Paul Roetzer: Grok's going to have, everybody's going to have a deep research product. And once those things, you know, all have access to the web, they all become external, like, what do you need perplexity for? So. I, I think if I was again on like putting odds on things, I do think perplexity at some point just gets folded into somebody this year.
[00:26:35] Paul Roetzer: I just don't understand what their market is going to be when everybody else can kind of do the same thing. Plus, you can build these things open. So, I saw somebody already like, Created an open source version of this on like Hugging Face where they like reverse engineered how they did it. So that's interesting.
[00:26:49] Paul Roetzer: The battle with Google is certainly noteworthy here. I think we are now in a race for these reasoning models and to productize those reasoning models. And maybe this is how they get away from the [00:27:00] model confusion is you start building independent products or agents for specific things. And so deep research is that idea that like, I don't even care what model they're using.
[00:27:10] Paul Roetzer: Does it do the analysis I need it to do? And so maybe productization is actually what eliminates the confusion with all these models. I haven't had time to think about this, but the impact on the future of search SEO. Like changes impact on education. You cannot give research projects to students and not accept the fact that they may be using these tools to do it.
[00:27:33] Paul Roetzer: Impact on the future of the work of work, which I'll, I'll talk about as kind of go on here for a moment. Um. Last week, we talked about humanity's last exam. I think that was 133, Mike, when we talked about that new exam. So basically an exam was created to try and accept the fact that the current evaluations weren't hard enough for these models and we needed something insanely hard.
[00:27:55] Paul Roetzer: So they went out and got over a thousand subject experts to contribute all of [00:28:00] these questions, like the hardest questions you could create. There's a data set of 3000 challenging questions. And we went from, let's see. Kevin Roos tweeted, When I wrote about Humanities last exam, the leading AI model got an 8.
[00:28:15] Paul Roetzer: 3%. Five models now surpass that, and the best gets 26. 6%. That was 10 days ago, in all caps. So, we went from 8. 3 percent to 26. 6%. In those 10 days, O3 mini high is 13%, now it's text only, because that's all O3 mini high does. O1, it was the highest multimodal model at 9. 1%, and now we have deep research at 26. 6%, all in two weeks.
[00:28:45] Paul Roetzer: So, Sam actually tweeted on Saturday, the day before they released deep research, A screenshot of humanity's last exam and said, we're going to need a new exam soon. Now keep in mind, they're training 04 already. Like, they already know [00:29:00] what the scaling laws are for the reasoning models. And they can look out ahead and say, by the time we get to 05, this, this exam's done.
[00:29:07] Paul Roetzer: We will have surpassed this too. okay, so, here's where we start getting into the interesting stuff. So, Sam tweets, this was I think last night, because then he said it in his presentation at this livestream event. He said, my very approximate vibe is that deep research can do a single digit percentage of all economically valuable tasks in the world, which is a wild milestone.
[00:29:35] Paul Roetzer: That's his, his words. So, So I thought, well, how can I test this model? So I upgraded to pro this morning and I was like, all right, let me, let me run my first test. So I took this idea from Sam that we are now at the point where single digit percentage, which who knows what that means, maybe 9%, all economically valuable tasks, keep in mind jobs or series of tasks.
[00:29:59] Paul Roetzer: [00:30:00] So I went into, deep research and I said, okay, This is the prompt I gave it. When presenting OpenAI's new deep research capability, Sam Altman said, quote, my very approximate vibe is that it could do a single digit percentage of all economically valuable tasks in the world. Research and analyze what jobs could be most impacted in the next one to two years, including rationale and estimated impact.
[00:30:23] Paul Roetzer: It went off for five minutes and started doing its thing. And again, if you haven't used one of these models, you need to experiment with them. Like watching its chain of thought is kind of wild. And it does share way more than it used to share about that. It finished it, and honestly, like, it looked really impressive.
[00:30:40] Paul Roetzer: You could see like a college student turning it in and saying, Hey, I did it. It's awesome. But the first time you start clicking on citations, you realize it's citing everything from the March 2023 GPT's or GPT's paper. And then articles about that paper. So, I went through, read, read this this is useless.
[00:30:58] Paul Roetzer: Like, this, this is [00:31:00] two, two year old data, basically. So, my second prompt was, you're citing studies and articles from 2023 that don't take into account all the advances in AI models since then. I'm specifically looking for an analysis of how OpenAI's new deep research model, and then I put C, and then I put the URL in to the announcement post, how this will impact jobs.
[00:31:20] Paul Roetzer: So now it got way better. Now it did still pull some of those 2023 studies, but it also started pulling in more direct research, and as you watch the chain of thought, you could see it saying, oh, but I need to connect it back to the current release. Let me go revisit that current release. information from OpenAI and you could like watch it going back and forth, which again, it's just fascinating to see.
[00:31:42] Paul Roetzer: So I'm gonna, I want to actually take a couple minutes here because I think this is really critical and I'm going to go through some of the highlights of that deep research output. so again, my prompt was all about trying to understand the impact. It starts with roles most susceptible to AI automation and augmentation.
[00:31:59] Paul Roetzer: [00:32:00] Not all knowledge jobs will be affected equally. The deep research models capabilities align closely with tasks that involve information processing, writing, and pattern recognition. Which means certain roles will see a greater impact. Jobs heavily centered on generating or analyzing text and data are the most susceptible, especially those with routine or formulaic components.
[00:32:20] Paul Roetzer: On the other hand, roles requiring a high degree of human judgment, interpersonal skills, or creativity may be less affected in the short term. Now the reason I'm reading this is because I agree with everything it wrote. So I am, I'm going to now provide the perspective of someone who thinks deeply about this a lot, and this is a really good output.
[00:32:38] Paul Roetzer: And I think it's important for people, if you haven't Come to the stage of acceptance of where we are, I think these are really important things to listen to. So, then it goes into examples. Writers, editors, and content creators. Professionals who produce written content. Technical writers, journalists, copywriters are highly exposed to this technology.
[00:32:57] Paul Roetzer: Researchers and analysts, knowledge workers who research and [00:33:00] synthesize information such as market research, policy analysts, academic research assistants, will find many of their tasks accelerated. Legal assistants and paralegals, legal research and document drafting involve scanning large volumes of text and producing written analyses.
[00:33:17] Paul Roetzer: This means law firms might handle the same caseload with fewer support staff or focus their staff on more strategic advisory rather than rote paperwork, which is the theme we keep stressing. It's just like it's going to change things real fast. Accountants and financial analysts. A large portion of work in finance involves analyzing numbers and writing reports.
[00:33:36] Paul Roetzer: For instance, summarizing quarterly results, auditing financial statements, or evaluating investment options. The deep research model can analyze financial data and produce well written analyses or summaries. it says while complex strategic decisions still rely on human judgment and domain expertise, the preparatory work, compiling data, option analysis, can significantly be automated.
[00:33:58] Paul Roetzer: This suggests a high level [00:34:00] of augmentation in finance roles and potential automation of junior level tasks. Software engineers we've talked a bunch about, there's a section on evolving skill requirements for knowledge workers. As the deep research model takes over certain tasks, the skills required for many jobs will evolve.
[00:34:15] Paul Roetzer: AI literacy will become as important as basic computer literacy. Knowledge workers will need to know how to work with AI tools effectively. This includes skills like prompt engineering, interpreting AI outputs, and verifying the information that AI provides. That's a really important paragraph. That, when you think about any job, any knowledge work profession, those are fundamental things.
[00:34:37] Paul Roetzer: Knowing how to prompt it, knowing when to give it a better prompt because the first prompt didn't get what you wanted out of it, or you knew the output wasn't the level of expertise you needed. interpreting those outputs, verifying the information is accurate. These are the things. So then it says critical thinking and oversight skills will be in high demand.
[00:34:53] Paul Roetzer: I agree. The ability to apply domain expertise to validating AI generated results becomes a core part of the job. That is what we're [00:35:00] doing in real time right now. This is me applying a domain expertise to assess the output of, the AI. The human only skills increase in importance. Creative thinking, strategic vision, interpersonal communication, emotional intelligence will differentiate employees in an era where routine analysis or writing is done by machines.
[00:35:18] Paul Roetzer: I'd, I'll stop there for a second, Mike. Is there anything I just went through there that you would disagree with or that, like, Nope.
[00:35:25] Mike Kaput: Not at all. This is perfect.
[00:35:27] Paul Roetzer: Yeah, it's a pretty good synopsis. okay, so then OpenAI says, they're going to keep doing this, they're going to build more agents, this is going to get better and better and better.
[00:35:36] Paul Roetzer: Then I wanted to pull one other thought in here. So I don't know if, Mike, we mentioned this later on. if we do, we can kind of gloss over it, but Y Combinator. So if you aren't familiar with Y Combinator, it is a startup incubator. they have, let's see, since 2005, they've funded over 3, 000 companies.
[00:35:56] Paul Roetzer: there are more than 60 Y Combinator [00:36:00] companies valued at over a billion dollars. So unicorns and the combined valuation of YC alumni is over 600 billion. Sam Altman was the president of yc, Y Combinator from 2014 to 2019. So this is a very important organization within the technology world. They put out a call for startups.
[00:36:21] Paul Roetzer: So this is last week. So if you don't think, like if you're still on the fence about whether or not we're trying to replace people, I'm going to read to you two of the areas that they have put out a call for startups. The first, AI personal staff for everyone. Quote, despite the explosion of software in the last decade, wealthy people still employ lots of human staff to provide personal services.
[00:36:44] Paul Roetzer: These are things like tax accountants, personal lawyers, and money managers, but also personal trainers, private tutors, and even personal doctors. The list goes on. Why can only the rich afford this? Because software hasn't been able to replace these types of [00:37:00] personalized knowledge work tasks until now.
[00:37:03] Paul Roetzer: Over the next few years, we expect AI to get good enough to do most of these jobs, not just tasks, jobs. So if you are working to bring a part of this personal AI staff to every human on the planet, we'd love to hear from you. Then one other one, there was like, I don't know, 10 or 12, so I'm just picking two of them.
[00:37:20] Paul Roetzer: The next one was vertical AI agents. What is a vertical AI agent? This is them, quote. It's software that's built on top of large language models that's been carefully tuned to be able to automate some kind of real important work. In recent batches, we've had YC companies build an AI tax accountant, an AI medical biller, an AI phone support agent, and an AI compliance agent, and an AI quality assurance tester.
[00:37:46] Paul Roetzer: The value proposition of B2B SaaS companies was to make human workers incrementally more efficient. The value prop of Vertical AI Agents is to automate the work entirely. Vertical AI Agents that reach human level [00:38:00] performance grow extremely quickly. Again, the reason we are spending so much time on this topic is, if you're not aware or accepting of where we find ourselves, this is it.
[00:38:13] Paul Roetzer: You have Y Combinator that drives the growth of startups, calling for startups to replace people. You have Sam Altman saying the current model that he knows is only going to get better in a few months. This is already capable of single digit tasks for all human work. This is, it's real. Like, and, and again, like the key here is the timeline is accelerating.
[00:38:38] Paul Roetzer: We're likely going to get a new model this week. I think from Google, it sounds like maybe, Anthropic supposedly is sitting on something as good or better than what Deep Research is doing and hasn't released it yet due to concerns. I It's only going to accelerate from here, and it's just really, really important that people, even if you don't want to believe it, that you try and [00:39:00] step back and be realistic about what is happening, because it's going to happen really fast.
[00:39:05] Mike Kaput: That is a really good kind of call to action and warning, and honestly, I just keep coming back to, in my own life, trying to think about how do I become less emotional about this, because I get it's scary, it's overwhelming, but like, You have to look at the reality of the writing on the wall. And also, how do I go even more all in in my time on AI for X, right?
[00:39:28] Mike Kaput: I think that's really the only sustainable strategy I'm seeing as a knowledge worker moving forward.
[00:39:33] Paul Roetzer: Yeah, and I think it's a great point, Mike, but this like, It's hard to emotionally disconnect ourselves from what's happening. Like, I was, someone was asking me over the weekend about, like, tariffs and, like, why would we do this?
[00:39:46] Paul Roetzer: Like, our allies, like, what is going on? And it's hard to not get emotional about it, but I said, like, just factually, like, the chaos and the pain is the point. Like, The strategy they're employing is to create [00:40:00] chaos and pain. And so they can leverage it to get what they want in the end. And that is not a right or left, like, opinion.
[00:40:07] Paul Roetzer: It is just, it's what's being done. And so you do, you have to kind of like try and be able to step back and the same thing applies in the AI world. Like, what is going on? Why are they doing it? And like, what does it actually mean? And to disconnect from the fact that, wait a second. I'm a writer. Wait a second.
[00:40:21] Paul Roetzer: We do analysis every week on the podcast. Like, this is our world. My wife is an artist. Like, we live in this world where this impacts us personally. But you do have to step back and try and remove that and be objective about what's actually going on and what does it really mean.
[00:40:37] Mike Kaput: Alright, our third big topic this week, the U. S. Copyright Office has issued a landmark report that provides updated guidance on how copyright law applies to AI generated works. This report is titled Copyright and Artificial Intelligence, Part 2, Copyrightability. And it comes after their extensive consultation with over 10, 000 commenters from all [00:41:00] 50 states and 60 countries.
[00:41:01] Mike Kaput: Seven countries. So they did an executive summary of their core findings. This is a 50 plus page report. I'm just going to quickly touch on the main points here. So first existing copyright law is adequate to handle AI generated works. They say that no legislative changes are needed. To move forward here right now, the use of AI tools to assist rather than stand in for human creativity does not affect the availability of copyright protection for the output.
[00:41:29] Mike Kaput: Copyright protects the original expression in a work created by a human author, even if the work also includes AI generated material. Copyright does not extend to purely AI generated material or material where there's insufficient human control over the expressive elements. Whether human contributions to AI generated outputs are sufficient to constitute authorship must be analyzed on a case by case basis.
[00:41:55] Mike Kaput: Based on the functioning of current generally available technology, they say that [00:42:00] prompts do not alone provide the what they call sufficient control and they say that human authors are entitled to copyright in their works of authorship that are perceptible in AI generated outputs as well as the creative selection coordination or arrangement of material in the outputs or creative modifications of the outputs so while the report offers Quite a bit of clarity actually on some key questions people have.
[00:42:24] Mike Kaput: It also acknowledges that standards may need to evolve as AI advances. So they're going to plan on continuing monitoring the developments going on in this space and providing ongoing guidance. So Paul, I guess like with the caveat, as always, we are not lawyers. You should check with your lawyers before taking any path forward here.
[00:42:43] Mike Kaput: But it seems like this is at least some of the guidance we've been waiting for, for a while, like. It kind of seems like decently big news that AI generated outputs can, in certain circumstances, get some type of protection. Like, is that how you're initially reading this?
[00:42:58] Paul Roetzer: Yeah, I mean, so you, you [00:43:00] know, you and I are in the midst of a pretty large content strategy that this affects, like, and we'll, Mike and I'll talk more about this in the future, but we're always assessing how AI can be used in outputs that then impacts our ability to hold a copyright to that output.
[00:43:18] Paul Roetzer: And so we were just on a call with our IP attorneys 10 days ago on this exact topic. And then this came out and the immediate email was get this to the IP attorneys and ask if this changes anything based on what we just discussed. So. I'll read two quick paragraphs from the release from the office. As the office confirms that the use of AI to assist in the process of creation or inclusion of AI generated material is a large, a larger human generated work in a larger human generated work does not bar copyrightability.
[00:43:49] Paul Roetzer: Now, that's way more clear, I believe, than what they previously stated. So, the previous thing we would always say is like, hey, if I generate, AI generates it, you can't copyright. Now, it's [00:44:00] moving more towards like, well, as long as you remix it enough, as long as you have enough human involvement, you can actually copyright the stuff, even the stuff that came from the AI, as long as You've made changes to it, basically.
[00:44:14] Paul Roetzer: so then there's a quote, it says, After considering the extensive public comments and the current state of technological development, our conclusions turn on the centrality of human creativity to copyright. Says Shira, Perlmutter, Register of Copyrights and Director of the U. S. Copyrights Office.
[00:44:30] Paul Roetzer: Where that creativity is expressed through the use of AI systems, it continues to enjoy protection, extending protection to material whose expressive elements are determined by a machine. I don't even know how many this means. However, would undermine rather than further the constitutional goals of the copyright.
[00:44:45] Paul Roetzer: That's a word salad to say that like they've kind of moved a little bit on this. so here's my overall take. Don't go changing your generative AI policies on your own without input from your attorneys. Go talk to your IP attorneys, share the information with them in case they [00:45:00] haven't seen it. but be proactive here because your team is doing things every day with generative AI that isn't considering these things and you do need to kind of quickly assess how this evolves.
[00:45:12] Paul Roetzer: I would then take the information from your IP attorneys, adjust policies internally as needed, as well as any policies you may have with outside contractors, agencies, freelancers, things like that, and train them how to do it. So if this now creates some level of freedom to use AI more, Make sure that they're trained how to properly deal with the outputs of the AI so that your copyright is protected.
[00:45:39] Paul Roetzer: It's not enough just to say, okay, you can now use generative AI, we can still get a copyright. That is not what the Copyright Office is saying. They're saying you have to have human elements within it. So Come to an agreement what that means within your company with the help of your IP attorneys and make those adjustments.
[00:45:54] Paul Roetzer: In a quick related note, the Authors Guild, one of the largest associations of writers in [00:46:00] America, is launching a new project to certify books that have been written by human rather than machine. The new human authored quote unquote certification will help authors distinguish their work and let readers know what they're This is what I think I said this last year, like, this was inevitable that we're going to have, Human authored, whatever you call it, human certified songs, books, articles, everything.
[00:46:21] Paul Roetzer: so I'm not surprised at all by this, Mike, but I think we'll see a lot more of this like human created stamp. I know with my exec AI newsletter I do on Sundays, I put at the bottom, this is 100 percent written by me. Right. yeah, I, maybe I need to get like a A Paul stamp on it or something, I don't
[00:46:37] Paul Roetzer: know.
[00:46:37] Mike Kaput: That would be great. Yeah, no, this is cool stuff to see and I certainly understand the motivation. I'm a little curious how they're going to actually do this because like, I don't know, if you figure it out, Authors Guild, go talk to every higher education institution in America as well because it seems like a huge lift to even figure this out.
[00:46:55] Paul Roetzer: Yeah, I don't know if you have to like, turn in your word docs and like, show the providence of it all, I [00:47:00] don't know.
[00:47:02] Mike Kaput: All right, let's dive into some rapid fire topics this week. So just right after DeepSeek has rattled the tech markets and investors are questioning, you know, do we need to be spending this much money on AI models?
[00:47:15] Mike Kaput: OpenAI is preparing to raise what could be the largest private funding round in Silicon Valley history. They are apparently in talks to secure up to 40 billion in new funding. that would value them around 300 billion. SoftBank is leading the charge, looking to invest between 15 and 25 billion. The valuation represents a pretty significant leap from OpenAI's previous 157 billion valuation just a short time ago in October of 2024.
[00:47:49] Mike Kaput: Now, OpenAI says that they plan to use the funds, or it's reported that they plan to use the funds, partly to fulfill their 18 billion commitment to Stargate, their recently [00:48:00] announced joint venture with SoftBank and Oracle, to build AI data centers across the U. S., They also need capital to fund operations.
[00:48:08] Mike Kaput: They reportedly lost about 5 3. 7 billion of revenue. Interestingly, this would make OpenAI the second most valuable private company in the world, trailing only SpaceX. And it also deepens, appears to be deepening the relationship between Sam Altman and SoftBank CEO Masayoshi Son, who appears to be making OpenAI his primary vehicle for betting on the AI industry.
[00:48:35] Mike Kaput: So Paul, people were already turning their head at 157 billion valuation. Now we may almost double that, raise an additional 40 billion. Like how much is enough here? Is there a danger the Stargate thing blows up? Like what, what's going on here?
[00:48:53] Paul Roetzer: I mean, there was the rumor last year that Sam was seeking trillions and I don't, I don't, I think there was something to those [00:49:00] rumors.
[00:49:00] Paul Roetzer: I don't know that it was 7 trillion like reported, but I do think that. When they look out, you know, over the next 10 years, they expect to spend trillions on building out the capabilities in this intelligence. they're big numbers. I did a little quick research just to see, I did not use deep research for this.
[00:49:16] Paul Roetzer: This was a traditional Google search, believe it or not. the most valuable companies in the world, not just private, but publicly traded. So just for context, at 300 billion, how big is that? IBM's market cap is 234 billion, so it's bigger than IBM. Samsung's 237 billion, T Mobile's 266, and Coca Cola's 273.
[00:49:38] Paul Roetzer: So, bigger than all those, and then right on the heels of, Salesforce at 319 billion, and SAP at 334 billion. So, 300 billion is no joke. It is, like, top 35 companies in the world in terms of value. So yeah, and it just keeps jumping. I would not be surprised at all if they aren't a trillion dollar company, [00:50:00] you know, by the end of 2026, if not sooner.
[00:50:03] Paul Roetzer: Did you see anything in those notes, Mike, about any updates on their move to change the structure of the company that could free them up to IPO?
[00:50:12] Mike Kaput: It's possible I missed something, but I was actually surprised at like the lack of talk around that. When I scanned it, I didn't see
[00:50:18] Paul Roetzer: anything mentioned. I was like, that's a pretty important part of the story.
[00:50:21] Mike Kaput: Especially because I think their last funding round, right, also had conditions around how the money was used based on that conversion, so I'm not sure.
[00:50:29] Paul Roetzer: Okay, well, we'll look into it a little bit more. We'll ask Deep Research, but I do think, like I assume, they are moving forward with those plans to restructure the company so that they can Eventually, IPO, that would be the most logical path.
[00:50:44] Mike Kaput: Yeah, I mean, it's interesting too, just as a final note here, you know, I think it is easy to say like, okay, this is a crazy valuation, you're charging 20 to 200 bucks a month for these licenses for ChatGPT or whatever, but in the context of what we just talked about [00:51:00] with Sam's comments around deep research.
[00:51:02] Mike Kaput: You have to start thinking of like the tam, the total addressable market is not other software licenses. It's jobs, it's intelligence, the serv, it's the services software concept we've talked about a few times, like when you start thinking, oh, not what's, what's the market for accounting software? What's the market for accountants?
[00:51:19] Mike Kaput: Yeah. That's a very different question in terms of the numbers involved.
[00:51:23] Paul Roetzer: Yes. And what, and they won't say that out loud, like they're not going to tell you they have a deck that has the total addressable market of all knowledge work, but. That is basically what we're talking about is what is the value in the U.
[00:51:35] Paul Roetzer: S. of 100 million knowledge workers and what they do and their contribution to GDP and yeah.
[00:51:43] Mike Kaput: All right, one other story this week about kind of the deep seek fallout. So Mark Zuckerberg actually just laid out a pretty ambitious and urgent vision for Meta's AI future and addressed it. Some of the turbulence caused by DeepSeek in a company all hands meeting.
[00:51:59] Mike Kaput: [00:52:00] He told employees to quote, buckle up for what he called a quote, intense year ahead. He thinks in 2025, it'll be the year that a quote, highly intelligent and personalized digital assistant reaches a billion users. He wants Meta to be the company that gets there first. A key part of this vision is Llama4, which is their next generation open AI model.
[00:52:21] Mike Kaput: Now, in a social media post, he revealed that Llama4 will be natively multimodal, which he calls omnimodal, with built in capabilities for autonomous. The smaller Llama 4 Mini has already completed pre training. He also predicted that this will be the year when companies can build AI engineering agents.
[00:52:40] Mike Kaput: He's talked about this a couple times, saying that we're going to have AI that can be at the level of a good mid level engineer. And he called this potentially one of the more important innovations in history. Now this roadmap obviously comes as Meta is navigating the turbulence caused by DeepSeek. He was pretty [00:53:00] noticeably positive when asked about DeepSeek during the all hands and said the company's quote, novel infrastructure optimization advancements could actually benefit Meta since they were publicly So, Paul, like, when I read this and hear this from Zuckerberg, like, is Meta's competitive advantage here really that competitive?
[00:53:20] Mike Kaput: Like I get that it matters that they're one of, if not the, main kind of open model provider among the big U. S. labs, but it also seems like DeepSeek kind of takes direct aim at that.
[00:53:31] Paul Roetzer: Yeah, I think that Zuckerberg's putting on a good face publicly and internally. But the reality is DeepSeek did what he was trying to do.
[00:53:39] Paul Roetzer: Like, they disrupted things with an open weight model, which is what he's been trying to do with Llama, and they got way more love for theirs than I think Meta's got for what they've done. So, he's a really competitive guy. I can't imagine he was I was too excited about that, and I know it created a lot of headaches internally, and we talked last week about the, you know, these [00:54:00] rooms they created to focus on what to do, and they're already using it in their training and everything.
[00:54:05] Paul Roetzer: I did have to laugh, though. I was trying to scan and see if I couldn't find it. Oh, here it is. I think, so the article about this, like, you know, tough year ahead, it was like, he, I think he led off the meeting with saying about how annoyed he is that everything he says at Meta leaks and like, he's frustrated with it.
[00:54:22] Paul Roetzer: And so the headline was like, in, in leaked article or recording Zuckerberg says how angry he is that everything leaks in the company. Yeah. So, yeah, I guess that's the nature of having a, High profile tech company.
[00:54:36] Mike Kaput: Our next topic this week is AI expert Ethan Mollick, who we talk about all the time, just published a really fantastic guide to AI models.
[00:54:44] Mike Kaput: It's titled, Which AI to Use Now? An Updated Opinionated Guide. Basically which he now
[00:54:50] Paul Roetzer: needs to update because this was on the 26th, there's been like four models since then. Yeah,
[00:54:54] Mike Kaput: exactly right. Yeah, that's the danger of having published these takes, right? [00:55:00] So the reason though, this is important is because this really does.
[00:55:03] Mike Kaput: Start to help at least people answer a big question, which is not always obvious, which is what the heck should you use? Which model should you pick for your particular use case? So he kind of outlines, look, there's three clear front runners, Anthropx, Claude, Google Gemini, and OpenAI's ChatGPT. Each of these bring something unique to the table.
[00:55:23] Mike Kaput: ChatGPT currently leads the pack with live mode, where you can like have a conversation with the AI while it is seeing what you're seeing in real time. Google has similar capabilities that they demonstrated with Gemini, but ChatGPT is the only one offering that feature to all paying customers. Then, obviously, the companies are having more and more reasoning models, so AI that can think about a problem before answering.
[00:55:49] Mike Kaput: The most capable reasoning models, he says, currently come from OpenAI, though, obviously, DeepSeek is the best. Possibly changing that, offering competitive models too. [00:56:00] Google has their own thinking models as well. Web access is another key differentiator. ChatGPT, Gemini, several others can access the internet for current information while Claude cannot.
[00:56:12] Mike Kaput: And then some of these, if not most of these AIs can process images remarkably well. So video analysis is still rare. but for documents as well, Gemini stands out with the ability to process up to 2 million words at once, which is way more than any of the others. So Paul, this advice I thought was useful to note to people just because it is a challenge everyone struggles with.
[00:56:34] Mike Kaput: Like, it does map to a lot of things we've seen, what we advise people to do. I mean, we've talked about this before, I think it's very hard to avoid. Having at least a ChatGPT Plus account these days. I mean, for the ROI you get, I'd even go as far as to say paid tools for the other two are a huge benefit.
[00:56:53] Mike Kaput: But that seems like generally the advice we're offering to people is like, you want to focus on those three while you're experimenting with everything else. [00:57:00]
[00:57:00] Paul Roetzer: Yeah, definitely. You know, I, and that's, yeah, that's what we always say is at least just get the ChatGPT account and figure out how to use it. But I think it's So much, like I haven't made a note here as you were talking, like, I would love to see what the usage rates are on live mode in voice.
[00:57:14] Paul Roetzer: I don't know that there have been as disruptive as maybe we would have expected. I know personally, like, I use the live mode where you can like, you know, show it what you're looking at and ask a question. I used it like five times in the first two days. I don't think I've used it since. It's not quite like Apple Vision Pro level of not being used, but it's probably in a similar category where I just don't use it that much.
[00:57:35] Paul Roetzer: And then voice, I used voice a lot, but I normally use it when I'm in my car. And some reason it always like drops the connection and it drives me nuts. And so I stopped even using the voice in my car because it kept like restarting my conversation. so anyway, like, I don't know, like, I think it's great Ethan did this summary.
[00:57:54] Paul Roetzer: I just find myself starting to think more broadly now about these like adoption rates. And it even leads to this, [00:58:00] like, the reasoning model and what we're willing to pay. So I'm now paying the 200 a month for the pro licensure. I think you do the same thing. Yeah. and the way I justify is like, I'm fairly confident I will get 2, 400 in value out of this.
[00:58:11] Paul Roetzer: Like one or two examples or use cases or projects will pay for itself for the year. So I don't mind if I pay the 200 bucks and then Forget to use it for 30 days, like it's okay, I'm going to get the value. But I know a lot of people probably aren't in that same boat of like willing to just, you know, spend the 200 bucks.
[00:58:28] Paul Roetzer: But I do wonder if like even these reasoning models, as amazing as they are and passing humanity's last exam or like, you know, keep rising the charts, the reasoning models are harder to figure out how to use than the chat interfaces. So, like, you and I had a pack a thon internally to try and figure out how to use reasoning models when O1 first came out.
[00:58:45] Paul Roetzer: Right. And, like, what are the prompts that we would use? What would be the use case for this? Like, what's something really hard we would be trying to solve where we could actually test this model to where it's actually better than just regular ChatGPT? So, I do think that, again, these, all these companies, from Google, [00:59:00] OpenAI, Anthropic, they're going to continue to have this issue of, like, People just aren't really sure what to do with these things.
[00:59:05] Paul Roetzer: And as they get more intelligent, it actually becomes harder to figure out what to do with these tools. So, I don't know, it's, it's a good reason why, you know, articles like Ethan's are helpful. But I do think that in an enterprise, like everything about business adoption, you really just have to be very proactive with helping people figure out how to use these in their specific career.
[00:59:25] Paul Roetzer: Because otherwise they just have no idea what to do with them.
[00:59:27] Mike Kaput: Yeah, it just strikes me, the more I talk about this with people or publish about it, like, I think that this is way bigger of an issue than a lot of people, perhaps very close to this, realize. Like, the moment you start talking about this, people are just like, raising their hand, being like, yeah, I have the same problem.
[00:59:42] Mike Kaput: I've had to start building GPTs and prompts to like, help me with this issue, which is fun, but also, what are we doing here? Like, the average person is not going to do
[00:59:51] Paul Roetzer: it. You're the one percent. Like, the rest of the people are just going to just going to not do
[00:59:56] Mike Kaput: it. Yeah.
[00:59:58] Paul Roetzer: Yeah, and that's where I think like the open AI [01:00:00] may be really bullish because they're seeing these like crazy adoption rates of the pro license and people are like paying the 200 a month and they're getting a ton of revenue and they're probably seeing like high usage.
[01:00:08] Paul Roetzer: That's like the very, very early adopters, the innovator stage. Those are your like less than 1 percent of users. I would imagine they're going to plateau real fast on people who actually know what to do with these models and how to use them in their daily lives or careers.
[01:00:23] Mike Kaput: So kind of related to this, venture firm Andreessen Horowitz actually just released a really interesting market map kind of detailing where the AI voice agent space stands today.
[01:00:35] Mike Kaput: so in this, A16Z partner, Olivia Moore writes, quote, Voice AI is now nearly at human standards, allowing tech to replace labor on the phone. This has huge implications for businesses who can answer or make calls 24 seven at low cost. So they go through all these interesting kinds of startups and use cases.
[01:00:54] Mike Kaput: It's well worth diving into. in B2B companies are developing specialized voice [01:01:00] agents for different industries. There's like home service companies like Rosie AI at Revin. are creating voice agents to handle customer service and scheduling. In the restaurant industry, companies like Flying AI and Loman are developing voice systems to manage reservations and orders.
[01:01:16] Mike Kaput: Some companies are building voice AI for research, to conduct and analyze voice based research. Interestingly, there's a couple use cases in companies in the legal sector, like CaseFlood and Legal27. Creating voice enabled legal assistance and some voice AI for banking and financial transactions with companies like Salient and Domu.
[01:01:36] Mike Kaput: There are also a couple of cool examples they cite in the B2C space. So there's a particular focus on education technology with companies like Speak AI and Practica English are developing voice AI tools to help adults learn languages. While startups like Synthesis School and Buddy are creating voice enabled educational experiences for adults.
[01:01:57] Mike Kaput: Children. So Paul, while we're kind of seeing, yeah, [01:02:00] shoot, like advanced voice mode is maybe not reaching its full potential. It sounds like maybe with more of the, at the app layer, there are some interesting things going on here, it looks like.
[01:02:09] Paul Roetzer: Yeah. And I don't know if you have this one in the, and the funding and product updates, but, remind me, let's talk about it now.
[01:02:15] Paul Roetzer: Yeah. So Google's got ask for me, which just came out in their, search labs. I don't have access to this. I tried to get access, but I don't know. It wasn't already in there. I thought, again, this is the complexity of like. I thought I already had access to these things and you have to join another waitlist, but anyway, in Google Search Labs, I think you have to go into your personal account.
[01:02:33] Paul Roetzer: I don't think you can do this through a workspace account. there's a new tool called Ask For Me. And so a Verge article says Google is trying out a new tool that lets AI call businesses to ask questions for you. The feature called Ask For Me. Collects information about the pricing and availability of a service, but it's only available for nail salons and auto shops right now.
[01:02:52] Paul Roetzer: So yeah, this whole like movement of trying to find the market fit for these voice technologies. Yeah. that's going to be a big [01:03:00] deal. Now again, like this is different from you and I using the voice in ChatGPT. This is like building products around these kinds of things.
[01:03:07] Mike Kaput: And the fun, dark side of this, I've already gotten noticeably AI spam calls already that aren't just robo calls.
[01:03:14] Mike Kaput: Like, so that's starting too. Lovely.
[01:03:19] Mike Kaput: So in this week, we wanted to actually kick off a bit of a new segment that we're hoping to do kind of consistently, which tentatively calling listener questions. So we get a ton of questions each and every week about AI, both through the podcast and through other stuff we're doing like webinars and classes.
[01:03:38] Mike Kaput: So we wanted to start kind of answering. Some of those questions to the best of our ability on the pod. So if you have a question for us, like just reach out to Paul or myself, you know, LinkedIn's a good place to do that or go to marketingainstitute. com, click contact us. We'd love to hear from you. We cannot guarantee we're going to get to every question, but there's definitely won't.[01:04:00]
[01:04:00] Mike Kaput: I can guarantee we won't. there is a chance we'll be able to answer yours live. If you send it in. So Paul, I'm just going to throw this week's question at you. And this question says, I do not understand the difference between a custom GPT and an agent or when to use one over the other. Can you clarify this?
[01:04:20] Paul Roetzer: Yeah, so, it's a really good question. I think these are, a lot of times people are like, too shy to like, ask these what seem like obvious answers and then you realize like, oh, you don't understand it because it's actually super confusing and everybody has different definitions of these things. So, an AI agent is basically like, it's become kind of convoluted because everybody's sort of defining different things as agents, but it's an AI system that can take actions to achieve a goal.
[01:04:47] Paul Roetzer: So, in some ways, the reason this one's kind of confusing is that a custom GPT can be a form of an agent, like a very simple agent. So it can go do things. It's using the language model, you know, that it's powered by [01:05:00] to actually complete a couple of tasks and deliver something for you. In this case, like an output.
[01:05:04] Paul Roetzer: It's different from a computer use agent that like takes over your browser and fills out forms and things like that, or from the deep research agent we just talked about from OpenAI and from Google. that builds its own like plan, goes and executes that plan. So you're not setting the rules for it.
[01:05:19] Paul Roetzer: You're just saying, Hey, I want you to help me with this thing. And it's capable of building a list of tasks, executing those tasks, and creating a deliverable that achieves your goal of doing an analysis of something. So there's different types of agents, different, you know, Levels of human involvement in how those agents work, you know, the data they use, what they're integrated into, what the process that follows is, but that's where the confusion comes in is, is that like technically a custom GPT can certainly be categorized as a basic form of an agent is kind of a way to think about it.
[01:05:53] Mike Kaput: Yeah, I actually ran into this issue last week. I was talking with a friend of mine who wanted some help, with his [01:06:00] executive team thinking about how to use AI agents in his particular domain. And he was showing me some documentation around one of the systems they use. And he's like, yeah, it looks like you build AI agents here.
[01:06:10] Mike Kaput: Like this seems really complicated and a big thing because I'm hearing so much about them. You go through the directions and it's very clear. It's just a wrapper over custom GPT. It's literally the same options, which is not a problem. But it's like, man, this is actually going to feel a lot easier for you to use this piece of the software than you might have originally thought, because everyone's rebranding this stuff.
[01:06:29] Paul Roetzer: Yeah, and I think this, I don't know if this is overly technical, but the only real difference from like a traditional automation or what we used to call like a bot or an automation, you know, people would use like Zapier and set up these zaps to do things. Yeah. In a traditional automation, the human, wrote all the rules.
[01:06:44] Paul Roetzer: And the AI just did the 10 things that the human told it to do. It wasn't thinking, it wasn't reasoning, it wasn't like creating anything, you know, on its own. It was just following a set of rules or tasks. Now there's a level of autonomy, a level, [01:07:00] not full autonomy, there's a level of autonomy where The AI actually determines some of its actions.
[01:07:06] Paul Roetzer: It does things outside of what the human told it. So when you give it a prompt, you're not saying, do these 10 things in the prompt. It's sort of like taking some ownership of trying to figure out what it's supposed to do and how to do it, and so now, within this These steps is some level of thinking, reasoning, you know, true automation and creativity from the AI, and that's what makes them different than traditional automation.
[01:07:30] Mike Kaput: So this week, we're also going to highlight a couple of quick practical use cases for AI that we're finding useful or interesting or worth discussing at the moment, which is also something we're going to try to aim to start doing perhaps in a recurring fashion. So I'm going to kick off one, Paul, that I was, Exploring this week, just quickly describe it, and I also know you had been doing something with ChatGPT's tasks that I actually wanted to, double click on a little more with you.
[01:07:56] Mike Kaput: So, this past week, I actually created a pretty extensive [01:08:00] prompt, and we'll link to a post that describes it, that turns AI into your personal writing critic. So, it doesn't just check grammar, it doesn't just look for errors, it analyzes data. Seven different areas of your writing, like clarity, logical flow, how engaging it is, precision, persuasiveness, tone, and writing mechanics.
[01:08:17] Mike Kaput: Also provides a bunch of actionable scores and specific improvements. So recently I used it for a number of very long form content pieces, think literally thousands of words long and got really good success with it. I was trying to kind of fill a gap of like, I needed something more than Just you're out of the box, here's some tone, here's some style, here's some clarity.
[01:08:37] Mike Kaput: I needed to be like, hey, does this actually make sense? And found it pretty useful. So you can always kind of be thinking, I think, about whether it's writing or anything else, what are the limitations you're running to and into in your own tools? And then create custom prompts to address those. So, that's one.
[01:08:54] Mike Kaput: And then two, Paul, you had posted this week about some experiments that you were doing with ChatGPT's [01:09:00] tasks function. And like, can you maybe share with us, like, what you were working on, what was going on with that?
[01:09:06] Paul Roetzer: Yeah, so when Tasks first came out a few weeks ago, I set up three of them. One, I just thought was like to test its real time nature, I put summarized Cleveland Cavs games for me.
[01:09:15] Paul Roetzer: And daily at 11pm, like, send me a summary of what's going on. Those have gotten better. Like, sometimes they'll send me a summary of a game from three days ago. But, yeah. You know, interesting, because I was actually trying to test, like, impact on sports journalism, that kind of thing, like, where maybe I don't even need to go to ESPN anymore, I can just get real time updates.
[01:09:31] Paul Roetzer: I did one for mentions of my name, so anybody in the audience who remembers Google News Alerts, back when we were running our agency, Mike, there was, We had Google News Alerts set up for every client, every executive at the client companies, the board members, like you just set up all these alerts.
[01:09:47] Paul Roetzer: and so I thought, let's see what ChatGPT can do with that. So I get a daily email with any mentions of my name basically online. But then the interesting one I wanted to experiment with was send AI news summary. So [01:10:00] again, the way we, Curate the information for the podcast each week is probably 99 percent my Twitter feed.
[01:10:06] Paul Roetzer: So I have notifications from about 150 different brands and people. And if anything about AI is talked about throughout the day, I will see it within that feed. And I grab those links and I read them and I put them into Zoom. That's kind of how we curate what we talk about. So I thought, well, let me see if like, maybe I'm missing some stuff.
[01:10:25] Paul Roetzer: Let me test the new AI News Summary. So this one's pretty good. Fine, like honestly, it sends me like five or ten things. I didn't go in and say like, here's examples. I didn't like build out how it should really function. I just wanted to see how it functions kind of out of the box. So relatively unimpressive.
[01:10:38] Paul Roetzer: But then, what day was this? This was, February 2nd. So this was Sunday. so I get an alert from ChatGPT that I have news and the headline was OpenAI GPT 5 beta released. And I was like, Oh, that's news. So I click on the alert. Now, keep in mind, this is from [01:11:00] OpenAI in my inbox. So I click on it and it takes me there.
[01:11:03] Paul Roetzer: And in my news summary, it says, OpenAI launches GPT 5 beta for enterprise testing. It has started beta testing GPT 5 with select enterprise customers. Focusing on improved contextual understanding and multimodal capabilities. I was like, wow, I can't believe I missed that. So I click on the read more and it takes me to an OpenAI news page that pops up with a 404 error.
[01:11:24] Paul Roetzer: But in the URL is GPT 5, so it's possible that OpenAI's own task thing preempted their announcement of GPT 5 and maybe it's actually coming this week. Well, we will see. But yeah, so either their tasks thing doesn't work super well and does hallucinate or they actually had a news page that announced GPT 5 and it wasn't public but their task tool found it.
[01:11:52] Paul Roetzer: That's wild.
[01:11:53] Mike Kaput: Yeah, I'll be honest, I need to put out a call to listeners to send me tips on using tasks because I'm sure [01:12:00] this is just me needing to spend more time on it, but man, I've had some really, like, Lackluster out of the gate. Like, I can't get it to work in ways that are actually useful for me. I think it's super buggy right now.
[01:12:12] Mike Kaput: That's so interesting. It'll be really funny to see if it did actually preempt. Yeah. When this news comes out. It'll be hilarious. All right, to wrap up this week's episode, we have a few AI funding and product updates. I'm just going to run through these rapid fire. First up, Eleven Labs, an AI audio generation platform, has secured 180 million in Series C funding, which values them at 3.
[01:12:40] Mike Kaput: 3 billion. They are a major player in synthetic voice technology. Their tools are used across media, gaming, and tech. Their technology already powers voice features for prominent names like ESPN, Chess. com, and The Atlantic. Next up, Google has rolled out Gemini 2. [01:13:00] 0 Flash formally across web and mobile applications.
[01:13:03] Mike Kaput: This is the latest version of their AI model. That promises faster responses and improves performance across key benchmarks, particularly for everyday tasks like brainstorming, learning, and writing. And someone that's been playing around with this a little bit, I would validate that and say, you should definitely try out the new model.
[01:13:22] Mike Kaput: Now, alongside that, Google has also upgraded its image generation capabilities with the latest version of ImageN3. This new iteration promises to deliver richer details and textures with improved accuracy and following user instructions for creative projects. Now, to use this, you just ask Gemini to create you an image of whatever you want, and it will default to ImageN3.
[01:13:45] Mike Kaput: And generate your image. I will say like just very initial tests of this recently, like this blows DALL E out of the water for me. It's really cool. I would go recommend you test it out.
[01:13:55] Paul Roetzer: Does anybody use DALL E still? It's so bad.
[01:13:58] Mike Kaput: Yeah. It's gotten to the point where [01:14:00] it's, I mean, we're so spoiled, but it does look dated at this point.
[01:14:04] Paul Roetzer: And they all look the same. Yeah.
[01:14:07] Mike Kaput: So the last update we've got this week is Meta has announced an update to its AI assistant. The company's chatbot will now be able to access and use information from your Facebook and Instagram accounts to provide more personalized responses. There's two kind of new capabilities here.
[01:14:25] Mike Kaput: First, Meta. AI can now remember details from conversations across Facebook, Messenger, and WhatsApp. So you can explicitly tell its AI to remember certain preferences or things you like. And it will factor these into future interactions. The more substantial change is that MetaAI will now also automatically tap into the user's broader social media activity.
[01:14:47] Mike Kaput: So the assistant can access information like home locations from Facebook profiles or recently viewed Instagram videos to shape content. Paul, it has been a wild week [01:15:00] in AI. I think we're probably getting more news this week that's going to be big and relevant, but we'll see. Appreciate you breaking it all down for us.
[01:15:08] Paul Roetzer: Yes, there is more model news coming. I think we'll have multiple launches in February for sure. Maybe multiple launches this week. We will do our best to keep up with all of them for you. I put, I put this, GIF on Twitter earlier this week with like the cats, like heads moving back and forth, back and forth, like as like model releases be like, it's just like, well, I think that day, like Gemini 2.
[01:15:30] Paul Roetzer: o Flash had come out and then o3 Mini came out and I was just like, Oh my gosh. It's crazy. Well, good luck everyone. don't get caught up in the madness. Focus on use cases that actually matter to your job, and just like, stick to those. Just keep nailing those and stack those, but do not get overwhelmed by the fire hose of AI model news like Mike and I have to do every Monday morning.
[01:15:54] Paul Roetzer: And, we appreciate you being with us, and we will talk to you again next week. Thanks for [01:16:00] listening to The AI Show. Visit MarketingAIInstitute. com to continue your AI learning journey and join more than 60, 000 professionals and business leaders who have subscribed to the weekly newsletter, downloaded the AI blueprints, attended virtual and in person events, taken our online AI courses, and engaged in the Slack community.
[01:16:21] Paul Roetzer: Until next time, stay curious and explore AI.