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[The AI Show Episode 137]: GPT-4.5 and GPT-5 Release Dates, Grok 3, Forecasting New Jobs, DeepSeek Investigation, Microsoft Quantum Chip & Google AI “Co-Scientist”

By Claire Prudhomme on February 25, 2025

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This week on The Artificial Intelligence Show, we explore the latest developments in the world of AI. From OpenAI's anticipated release dates for GPT-4.5 and GPT-5 to Grok 3’s debut week, we’ll discuss the real-world impact on the future of work.

Plus, don’t miss updates on Microsoft’s new quantum chip, DeepSeek’s latest strategies, Mira Murati’s exciting new startup, and much more in our rapid-fire segment.

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

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Timestamps

00:04:05 — GPT-4.5 and GPT-5 Updates, ChatGPT 400M Users

00:17:16 — Grok 3

00:34:03 — The Future of Work

00:47:43 — DeepSeek Raise and Investigation

00:50:14 — Mira Murati Announces Thinking Machines Lab

00:53:41 — Microsoft’s New Quantum Chip

01:00:41 — Google’s “AI Co-Scientist’

01:05:42 — Google’s AI Efforts Marred by Turf Disputes 

01:10:13 — AI Displays Signs of Deception

01:15:17 — The New York Times AI Use Cases

01:17:21 — Listener Questions

  • As AI agents become more popular and interact with brands, does this make consumer interactions with brands obsolete? What core brand attributes remain in a world of AI agents?

01:20:28 — AI Product and Funding Updates

Summary 

OpenAI Updates and ChatGPT 400M Users

OpenAI continues to dominate the AI landscape, announcing significant growth and ambitious plans for its next generation of models. 

The company has reached 400 million weekly active users, a 33% increase in less than three months, while its enterprise business has grown to 2 million paying customers. This growth comes despite emerging competition from companies like DeepSeek.

But the bigger news may be what's coming next. OpenAI is preparing to launch two major updates to its AI models in rapid succession. GPT-4.5, codenamed Orion, is expected as early as next week and will be the company's final non-chain-of-thought model. The more significant release, GPT-5, is planned for late May and represents a fundamental shift in OpenAI's approach.

GPT-5 will integrate multiple technologies, including OpenAI's o3 reasoning model, which was previously teased but won't be released as a standalone product. This unified system aims to reduce confusion by combining the company's GPT and o-series models into a single, more powerful platform. OpenAI plans to make GPT-5 available to free users without limits, while paid users will have access to even higher levels of intelligence.

Microsoft, OpenAI's primary cloud partner, is already preparing its infrastructure for these launches. The timing aligns with earlier statements from OpenAI CEO Sam Altman, who has consistently indicated that these next-generation models would arrive in early 2025.

Grok 3

Elon Musk's AI company, xAI, has released Grok 3.

Unveiled last week, Grok 3 has already claimed the top position on the Chatbot Arena leaderboard, surpassing established players like OpenAI's models and Google's Gemini.

What makes Grok 3 particularly remarkable is its advanced reasoning capabilities. Trained on what xAI calls its "Colossus supercluster" with reportedly 10 times the compute of previous state-of-the-art models, Grok 3 displays exceptional performance across mathematics, coding, and complex reasoning tasks. 

On the 2025 American Invitational Mathematics Examination, released just a week before Grok's launch, the model achieved a stunning 93.3% accuracy, outperforming competitors.

The model introduces two key variants: Grok 3, the flagship model with extensive world knowledge, and Grok 3 mini, which excels at cost-efficient reasoning. Both leverage large-scale reinforcement learning to refine their problem-solving strategies in a way that mimics human thinking—considering multiple approaches, verifying solutions, and even correcting errors through backtracking.

Notably, Grok 3 features a transparent "Think" function that allows users to witness the model's step-by-step reasoning process, spending anywhere from seconds to minutes working through complex problems.

Grok 3 also has "DeepSearch," an AI agent designed to synthesize information from across the web. This capability, available to X Premium+ subscribers, represents xAI's first step toward more sophisticated agent-based applications that combine reasoning with real-world tool use.

Future of Work

What is actually going to happen with the future of work thanks to AI?

Plenty of AI labs and leaders and commentators are talking about the fact that AI will impact jobs. You can’t avoid posts and essays and interviews about AI taking jobs, creating new jobs, changing the nature of work, giving employees superpowers…and so on and so on and so on.

But anytime AI leaders talk about these changes, they seem pretty short on details. What jobs are going to be eliminated? What jobs are going to replace them? What exactly is the future of work going to look like?

On The Artificial Intelligence Show, we’re diving into a critical question: what AI-first jobs are likely to emerge in the market over the next few years? Concrete answers are scarce, but we’re here to explore the possibilities.

To tackle this, Paul’s first step was updating the popular JobsGPT tool. Originally launched in August 2024, JobsGPT has facilitated nearly 10,000 conversations to date. Now, it’s been upgraded to version 2.

Like its predecessor, the updated tool analyzes any job you input, breaking it down into tasks and subtasks. It then evaluates how likely each is to be affected by AI.

But version 2 goes a step further. It can now forecast entirely new jobs based on your current role. Early testing shows remarkable promise, offering fresh ideas and inspiration across various industries and professions.

This episode is 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 

This episode is also brought to you by our 2025 State of Marketing AI Report:

Last year, we uncovered insights from nearly 1,800 marketing and business leaders, revealing how AI is being adopted and utilized in their industries.

This year, we’re aiming even higher—and we need your input. Take a few minutes to share your perspective by completing this year’s survey at www.stateofmarketingai.com.

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: Elon has so much leverage over people right now. If you mess with xAI, it's like, what is he gonna do in retribution to you? 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.

[00:00:21] Paul Roetzer: Each week I'm joined by my co. and Marketing AI Institute Chief Content Officer, Mike Kaput, as we break down all the AI news that matters and give you insights and perspectives that you can use to advance your company and your career. Join us as we accelerate AI literacy for all.

[00:00:44] Paul Roetzer: Welcome to episode 137 of the Artificial Intelligence Show. I'm your host, Paul Roetzer, along with my co host, Mike Kaput. We are recording. Monday, February 24th, 11 a. m. Eastern time. we're expecting some, maybe a new model today, I [00:01:00] think. So timestamping might matter here. we will talk a little bit more about that as we get going.

[00:01:05] Paul Roetzer: This episode is brought to us by the AI for Writers Summit. We've been talking a lot about this one in recent episodes. This is coming up. On March 6th, this is our third annual AI for Writers Summit. This is from Marketing AI Institute and is presented by our sponsor, GoldCast. the event's a half day, so it's from noon to five, virtual event.

[00:01:27] Paul Roetzer: There is a free registration option. There is also a paid registration, like private option and an on demand option. But thanks to GoldCast, you can register for free. We had over 4, 500 people at last year's event. I think 90 countries represented. So it's an incredible day. It's an awesome opportunity to network through the GoldCast platform, to hear from an incredible, some incredible speakers.

[00:01:48] Paul Roetzer: Kind of the state of AI. Mike's going to talk about, like the role of deep research and, you know, using research products, AI products within your writing and creation. We've got Mitch [00:02:00] Joel as a closing keynote. Mitch is amazing, a friend of mine for a long time. Really excited to have Mitch. I'm gonna do a fireside chat with him.

[00:02:07] Paul Roetzer: and then we've got a panel on IP, copyright, just a ton of content packed into five hours. So you definitely want to check that out. It is AIWriterSummit. com. That is AIWriterSummit. com. You can also find it on the Marketing AI Institute site under events. And then we also mentioned this last week, but our state of marketing AI report survey is now in the field.

[00:02:32] Paul Roetzer: You can go to stateofmarketingai. com and participate in the 2025 survey. And Mike, you were telling me before we jumped on, I think we are almost 500 people have already completed the survey. Yeah, we've got 

[00:02:44] Mike Kaput: well over 500 respondents so far, so we're on track for this to be easily our biggest survey yet.

[00:02:51] Mike Kaput: And I just want to encourage people, it does not matter what your role is, what your company size is. We had a couple people reach out being like, hey, I'm a [00:03:00] solopreneur consultant, should I be taking this? The answer is yes, even if there are a couple of questions on there that you aren't super relevant to you, just go ahead and skip them.

[00:03:08] Mike Kaput: We want to hear from everyone. We are trying to understand the full state of the industry. 

[00:03:14] Paul Roetzer: Yeah, we do segment by that. So at the end, there's some profiling data. If you want to fill it out, just tell us the size of the company, you know, give us Things like that, and that enables us to go through and run some segments.

[00:03:24] Paul Roetzer: And then when we do these reports, you'll see and you can download the 2024 report right from that same page. If you want to see, you know, how these things are done, we put the sample size for each answer. So we're like really clear and transparent on all this stuff. so yeah, I agree, Mike. If, you know, no matter what your role is, we'd love to hear from you.

[00:03:43] Paul Roetzer: And then we can go through and like segment that data when we get done. All right, so let's jump into it. We've got, um. A lot of model news, we've got some stuff from OpenAI, we've got, Grok 3, yeah, just nuts. we've got some possibility of Anthropic [00:04:00] Claude launching something this week, it's, yeah, just, it's a week of model news, so let's jump in, Mike.

[00:04:05] GPT-4.5 and GPT-5 Updates, ChatGPT 400M Users

[00:04:05] Mike Kaput: Alright, so first up, OpenAI has It's released some numbers and some announcements that show it is kind of poised to continue dominating the AI landscape because the company has said it has reached 400 million weekly active users, which is a 33 percent increase in less than three months, while its enterprise business has grown to 2 million.

[00:04:30] Mike Kaput: Paying customers. So for anyone saying ChatGPT is dead or DeepSeek is eating OpenAI's lunch, I would probably temper those kinds of predictions because they are just doing incredible at the moment. The bigger news related to OpenAI may be what is coming next. They are preparing to launch two major updates to AI models that they have out there in rapid succession.

[00:04:55] Mike Kaput: So they're about to release GPT 4. 5 codenamed Orion. It's [00:05:00] expected as early as this week and will be the company's final non chain of thought model. The more significant release, GPT 5, is apparently planned for late May, according to some releases from The Verge. And it represents a fundamental shift in OpenAI's approach because it's going to integrate multiple technologies, according to some comments we found from Sam Altman covered the other week, including OpenAI's O3 reasoning model.

[00:05:28] Mike Kaput: So this kind of unified system aims to reduce confusion by combining the company's GPT and O Series models into a single, more powerful platform. OpenAI plans to make GPT 5 available to free users without limits, while paid users will have access to even higher levels of intelligence. Microsoft OpenAI Primary cloud partner is already preparing its infrastructure for these launches.

[00:05:58] Mike Kaput: And the timing aligns with [00:06:00] earlier statements from CEO, Sam Holtman, who has consistently indicated that these next gen models would arrive in early 2025. So this comes as OpenAI faces. Increased competition. we will talk about Grok 3 a little later in the main topic segment. And of course the legal challenges that they're facing, including things like being sued by Elon Musk, being rejecting a takeover from Elon Musk and continuing negotiations with SoftBank for a potential 40 billion investment that could value OpenAI at nearly 300 billion.

[00:06:37] Mike Kaput: Paul, first up, how much pressure is there? on OpenAI right now to wow us with GPT 4. 5, to wow us after that with GPT 5. They are facing a lot of pressure right now. 

[00:06:52] Paul Roetzer: Yeah, I don't know. I'm not sure that 4. 5 is going to be like some amazing leap forward. I think that's a 5 for a [00:07:00] reason. It may just be because they're going to integrate the models and they're not going to be ready to do that until, you know, 5 is ready.

[00:07:05] Paul Roetzer: But I do think that, You know, the deep seek thing changed things a little bit for them. I think Grok 3 is, you know, we're going to talk extensively about that next, but, that's going to put some pressure on them. I think Anthropix, next Claude, the word I was seeing over the weekend is 3. 7 is what it's going to be called, which is really weird.

[00:07:26] Paul Roetzer: It's like they just cannot get to that 4. 0. Um. So I think that there is increasing pressure and the thing we've talked about on the podcast recently is, you know, OpenAI was the state of the industry. It was this, you know, the top of the line for two years, like easily with GPT 4. And then everybody all of a sudden caught up and now, you know, it seems like maybe you can get out ahead with one of these frontier models, but it's probably only gonna last for, you know, three to six months before somebody does something else.

[00:07:57] Paul Roetzer: I think it's just more of, this is [00:08:00] the state of play now, is we're gonna have these frontier models that, Unless someone comes up with the next breakthrough that actually creates some space between them and somebody else. I mean, the 01 reasoning model was copied within two months by other people. So I don't know.

[00:08:14] Paul Roetzer: It's just a, it's a really challenging space for them. you and I talked, I think it was last episode, like, I'm really happy with the naming sequence. Like just go with GPT 5 is what the world expects. Like just give the world what it wants. Stop splitting these names off and, you know, just keep it simple.

[00:08:31] Paul Roetzer: And then I just think, you know, As we look ahead to the GPT 5 and, you know, whatever may be coming from Anthropic, the value of these reasoning models, we talked about this in September of 2024 when they first came out with O1, but we're now seeing it like Gemini has the thinking ability, Grok has reasoning capability, DeepSeq, Claude, like they're all building this in.

[00:08:54] Paul Roetzer: And just to revisit that concept for people, if you, you don't recall, like, what's the significance of this [00:09:00] reasoning, this is this idea that the models take time to think, that they go through this chain of thought process, and that they're able to improve their performance, reduce their hallucinations, the longer they think.

[00:09:12] Paul Roetzer: And what DeepSeek brought to us was the ability to see that chain of thought. And that kind of forced OpenAI to show more of what ChatGPT was doing with the O, O, you know, 102 models or O3 models. and so that's kind of where we're at is these, these reasoning models give us the ability to do multi step problem solving, more accurate predictions, deeper contextual understanding of like what's going on.

[00:09:35] Paul Roetzer: And if you remember back to the levels of, AI, the stages of AI that OpenAI presented last year. Level one was chatbots, which was the original ChatGPT. Level two is reasoners, which is, they define as human level problem solving. And level three is agents or systems that can take action. So what we're now seeing this year is this sort of like advancement of level two while starting to see [00:10:00] advancements at level three because the level two reasoners drive the advancements at the agent level.

[00:10:05] Paul Roetzer: And then theirs goes into innovators and organizations or levels four and five. So Yeah, I just, I think it's going to be really interesting because I think it's going to be hard to wow us with 4. 5, honestly. Like, I feel like Grok, and we will talk about this in a minute, I think XAI purposely released a relatively unsafe model into the world just to get there before OpenAI does with 4.

[00:10:29] Paul Roetzer: 5. So my expectation is you are going to see a lot of the stuff we're seeing with Grok 3 probably with 4. 5. And then I think Claude's on it. 3. 7 or whatever they end up calling it is probably going to be similar. So I think we're probably one, two months away from the model that. OpenAI, I would think, expects to be the new state of the art, but 

[00:10:51] Mike Kaput: Yeah, we will talk about that in a future episode, but Anthropic has a little catching up to do with some of these people.

[00:10:56] Paul Roetzer: Yeah, yeah, we talked about it, and I think that there's, you [00:11:00] know, I'll get into it a little bit with the Grok 3 conversation, but I do think that Anthropic has probably the most stringent policies internally about what is allowed to be released. And my guess is that they have way more powerful models than they're, than you and I have access to.

[00:11:20] Paul Roetzer: I just think that based on their safety levels, it takes a lot of preparation to put those models into the world and feel like they've done their job. And I don't, well, I was going to say I don't believe XAI shares that, XAI does not share those. 

[00:11:39] Mike Kaput: We have confirmation of that. 

[00:11:40] Paul Roetzer: Yes. 

[00:11:42] Mike Kaput: before we dive into that, just something that's kind of been more and more on mind is, like, what is the best way?

[00:11:50] Mike Kaput: In your opinion, for like your average knowledge worker to be ready when these new models drop, cause like we just got Grok 3, which we will talk about, we're probably getting GPT 4. [00:12:00] 5 this week, probably getting GPT 5 in May, Anthropic at some point, there's a ton of new models that are releasing faster and faster, yet very few of us can just like drop everything and go deep on every single one of these.

[00:12:14] Mike Kaput: Do you have any advice for like, what should I ready to go that I might, might be able to help me get any kind of handle on this? 

[00:12:21] Paul Roetzer: Yeah, I mean, I'm increasingly of the opinion, like, you just don't need to, like, there's gonna be people who do, who are constantly testing and want to be on the frontier and want to know what Grok 3's capabilities are and want to have done the voice mode in Grok and then as soon as, you know, Claude Sonnet comes out, they're gonna be jumping in there.

[00:12:37] Paul Roetzer: My general opinion is, as these models kind of consolidate in their capabilities, It's people are going to switch models less and less. Like you are, you are gonna just say, ChatGPT is just good enough. Or like, I have got Gemini built into workspace and it's good enough. And yeah, like their technology may lag behind by two months, but like, I don't care, like I I'm [00:13:00] locked into my three to five use cases that give me value every day.

[00:13:04] Paul Roetzer: And if Sonnet's a little bit better, does it really matter? Like, I just want to focus on being efficient, being productive, being creative. So, I mean, even for me, I still have, like, I'm questioning if I should keep them, but I still have a Claude license. I still have Perplexity license. I pay for Gemini.

[00:13:22] Paul Roetzer: I pay for ChatGPT. I pay for the 200 a month ChatGPT. So, I have all these models. And I would say I'm still 80 90 percent of the time just in ChatGPT, in part because I have built custom GPTs that serve specific purposes for me, and then I'll sometimes, if it's a more complex I will go test it in Gemini. I don't ever go into Claude.

[00:13:47] Paul Roetzer: I did last week for something, but that was like the first time in like three months I'd gone in there. I don't ever go into Perplexity anymore either. I use deep research from OpenAI and Google. So, NotebookLM, like that's a specific [00:14:00] use case. I use that, for what it does. So, I don't know. I feel like for most knowledge workers, You just like pick the platform you are going to work in and you are probably just going to stick to it.

[00:14:12] Paul Roetzer: And I think those platforms are going to become stickier over time as these, AI model companies find the thing that keeps you there. Like I think of the analogy being like in banking, once the bank has like you locked in for direct deposit, they know you are far less likely to churn as like a checking and savings account customer.

[00:14:33] Paul Roetzer: So it's like, what's the direct deposit equivalent? It might be like a chat, like a custom GPT. Like I'm just, I'm obsessed. Like it is the thing I get 80 percent of my value from. So I'm just gonna like stay with ChatGPT. So I don't know. I,Ithink as these companies start to productize more and more of these features, like a notebook LM or deep research, maybe there's some movement back and forth, but my general advice would be just probably just work with, you know, one of the models and assume it's, it's going to be good enough for what you need to do.[00:15:00] 

[00:15:00] Mike Kaput: Yeah, two interesting things there. And then we can move on. one I have seen some people start to talk about like memory as maybe that sticky thing is like, Oh my gosh, like ChatGPT already knows all this stuff about me, you know, which you could obviously recreate in other tools, but that would be interesting to see over time.

[00:15:16] Mike Kaput: But also too, I think like the play to me is like. Get as good as you can with one of these tools, right? Like when you hit a wall and you say, Oh, I have done everything I can do, then okay. Like worry about catching up on everything else because I'd say 99 percent of people are not at that stage yet. 

[00:15:35] Paul Roetzer: Yeah.

[00:15:35] Paul Roetzer: And I think this is, you know, we talked about the AI literacy project a few episodes ago and our plans for our AI academy. And one of the big things that I'm extremely excited about is we're going to have this new Gen AI app series. And that's going to be a weekly thing that, you know, Mike and I are coordinating this now and kind of building out the plan for it.

[00:15:53] Paul Roetzer: We're going to do, reviews every week and models will absolutely be a part of that series. So, [00:16:00] you know, imagine, you know, SONNET 3. 7 comes out, we're going to do a 15 20 minute review of it that will focus on use cases for knowledge workers and say, hey, you actually might want to consider switching to this or like, Just informational, keep doing what you are doing, ChatGPT, like that's the kind of insights we're planning to provide through that Gen AI app series is where we're doing these like quick reviews so you don't have to.

[00:16:22] Paul Roetzer: And then if we think there's something that's worth your time to like make a change or at least test a specific use case, we will call that out in the reviews we're going to do as part of that series. So yeah, 

[00:16:32] Paul Roetzer: again, like if people don't know what I'm talking about, weren't listening a few episodes ago, just literacyproject.ai. that's the URL, right, Mike? 

[00:16:40] Mike Kaput: Yep. 

[00:16:40] Paul Roetzer: And it talks about our plans for, AI Academy and the changes we're making going into this spring. We're going to launch a bunch of new courses and certification series. So, this is a key part of it is it's getting more complicated to keep up and so we want to start pushing out weekly content to help people keep track of all that.

[00:16:58] Mike Kaput: Yeah, I'm super excited for that. That's like [00:17:00] designed to kind of answer that big question everyone has, which is like, is this thing even worth me dropping everything to figure out? 

[00:17:06] Paul Roetzer: Grok. Yeah, it's not like, hey, it's cool tech, we're just doing tech reviews. This is like, what does it mean to me as a knowledge worker, as a business leader?

[00:17:13] Paul Roetzer: Do I, should I care? 

[00:17:16] Grok 3

[00:17:16] Mike Kaput: Alright, so let's talk about Grok 3. Second big topic this week. XAI, Elon Musk's AI company, released Grok 3 last week. It has already claimed the top position on the chatbot arena leaderboard. It's surpassed established players like OpenAI's models, Google Gemini, etc. What makes it pretty remarkable is its advanced reasoning capabilities.

[00:17:40] Mike Kaput: So it is trained on what XAI calls its Colossus supercluster, which is reportedly 10 times the compute of previous state of the art models. It displays exceptional performance across math, coding, and complex reasoning tasks. Interestingly, on the 2025 American Invitational Mathematics [00:18:00] Examination, which was released just a week before Grok 3 came out, the model got a stunning 93.

[00:18:07] Mike Kaput: 3 percent accuracy rating, outperforming competitors. There are two variations of Grok 3. There's Grok 3, the flagship model with extensive world knowledge, and Grok 3 which excels at cost efficient reasoning. NotablGrokroq 3 features kind of this transparent thinking function that allows users to look at the model step by step reasoning as it spends anywhere from seconds to minutes working through complex problems.

[00:18:33] Mike Kaput: It also has something called DeepSearch, which is an AI agent designed to synthesize information from across the web. And it also has this capability available to xPremiumPlusSubscribers. So that deep search is available to xPremiumPlusSubscribers. So kind of representing xAI moving towards the agent based applications that combine reasoning with real world tool [00:19:00] use.

[00:19:00] Mike Kaput: So you can use this in your, on X itself by going to Grok. You can go to Grok. com. You can use the app to access this new model functions. Very similarly to a ChatGPT or a Claude. So Paul, first up, what are your first impressions? Because I will say I have not done the world's deepest dive on it, but I am definitely pretty impressed at how good it is, related to how little time relative to the other incumbents they have had to put this together.

[00:19:30] Paul Roetzer: Yeah, first, I'd like to thank them for naming it DeepSearch instead of Deep Research since we already have Google's Deep Research and OpenAI's Deep Research and I never know which one people are talking about online. Yeah. yeah,Ithink at a high level, like technological achievement wise, time to build is incredible.

[00:19:48] Paul Roetzer: Everything I'm seeing online of the people who are pushing it, it does seem to perform very highly. Like it's, you know, top, kind of state of the art model. And they caught up extremely [00:20:00] quickly. You know, so they went from, you know, no model basically to this extremely fast. this continues to build on this idea that the companies that have data and distribution I guess infrastructure would be the third variable I'd throw in here.

[00:20:14] Paul Roetzer: Have a massive advantage, I think, moving forward. So, you know, a few episodes ago I was talking about, you know, how many frontier model companies will there really be, you know, one to two years out. And so, you know what, the people that fit into this, like, so, so Google Gemini, you know, obviously they have massive distribution.

[00:20:30] Paul Roetzer: They have, what, seven, you know, Products or platforms that have more than a billion users, like a massive distribution. And they have the data from YouTube Pixel and Cloud and Workspace and Classroom, like all this massive data. Meta has Instagram, Facebook, WhatsApp for distribution and data. XAI has X or T.

[00:20:48] Paul Roetzer: Twitter, they have Tesla, and they have whatever else, you know, Elon Musk is building. OpenAI doesn't have data. Like, they don't have any proprietary data. They don't have any of those products or platforms. All they have is [00:21:00] the distribution of ChatGPT, which is not insignificant when we say 400 million weekly active users.

[00:21:05] Paul Roetzer: and then Anthropic, Claude has no like they're just building these frontier models. So, one of the unique things that Grok has is that it has the data stream of X or Twitter. Now, some people could question how valuable is that data stream really? but it's a bunch of proprietary data that they shut off access to as soon as Elon Musk bought the company.

[00:21:26] Paul Roetzer: Now, Again,Ihaven't personally tested it enough to provide like my, my personal experience with Grok 3. What I will say is I was observing a lot over the weekend of what was happening on Twitter and what people were saying about it. And the thing that jumped out to me is Their competitive advantage at the moment outside of the speed with which Elon Musk can build things and the data they have is their willingness to release the most unrestricted model and let society figure it out.

[00:21:55] Paul Roetzer: Like, deal with the ramifications of that. It is very obviously racist if you [00:22:00] want it to be racist. It is sexist if you want it to be sexist. It actually has a sexy mode on the voice mode. Like, you can literally pick sexy and, and, you know, talk to it in an unrestricted way. as you could imagine you would do with something that's called sexy.

[00:22:16] Paul Roetzer: And the crazy part is like, they're comp they're totally proud of this. So like Elon Musk tweeted over the weekend, Grok 3 AI girlfriend or boyfriend is fire. then an ex AI employee replies, Hate it or like it, AI romantic partners is an inevitable trend. They are not necessarily bad, they remind us how replaceable we humans as romantic partners are.

[00:22:38] Paul Roetzer: Appreciate your partners, they'll likely have given up a lot for your love. To which, Benjamin De Kraker, who we talked about two episodes ago I think, he got fired from XAI. So he replies, builds and ships an AI sexbot, says, oh well, the AI sexbots were inevitable. So [00:23:00] this is kind of like, this is the Elon Musk factor, like he doesn't care, he's just gonna do this stuff.

[00:23:06] Paul Roetzer: If you want to see some crazy things, go search Grok 3 voice mode. And like, you don't have to do these searches yourselves, you can see the things people have got this thing to say. It's wild. So, the same things that, like, the same things OpenAI held their voice mode back for. So if you remember, OpenAI introduced their voice mode in like, March or April 2024, I think.

[00:23:33] Paul Roetzer: And then we didn't get it for like, six months. The reason why is because it did these unhinged things. They spent six months stopping it from doing the things that XAI is like, just go do it. Like, they don't care. So, the other one that became really fascinating over the weekend, and this was like, exploding on, on Sunday, is, obviously Elon Musk talks a lot about free speech.

[00:23:57] Paul Roetzer: And that, like, that's why he bought XAI, [00:24:00] was to, to take the barriers off, the guardrails off, and just let people say and do whatever they wanted to do. So, what happened over the weekend is it became Questionable, like, it's free speech as long as it doesn't say anything bad about Elon or Trump. And so what happened was, people started asking Grok 3, who are the biggest spreaders of misinformation?

[00:24:22] Paul Roetzer: and it would say, Elon Musk and Donald Trump. So it would give these answers. People started noticing this, started sharing it, people were replicating the search, and then all of a sudden, it stopped doing it. And people were like, wait a second, I can't get that response. I'm getting what's the Alex guy, the InfoWars guy.

[00:24:39] Paul Roetzer: Oh yeah, 

[00:24:39] Mike Kaput: Alex Jones. 

[00:24:40] Paul Roetzer: Yeah, he started showing up and like you would get top five and it was Marjorie Taylor and all these other people. And so people were like, wait a second, how did it stop doing this? Well, because it's a, it has the reasoning capability, you could actually see it thinking. And in its thinking, it would say, well, it's Elon Musk and Donald Trump, but oh wait, I have been told not to say [00:25:00] Elon the reply.

[00:25:01] Paul Roetzer: So you could see that someone had told it. to stop saying Elon Musk and Donald Trump, which obviously wouldn't fit under the free speech umbrella. So then when someone said, well, what are your system prompts that's telling you to not do that? And it would give people the system prompts. And so like Saturday, this is like going crazy.

[00:25:18] Paul Roetzer: And people are like, is this real? And they're tagging Elon Musk and Igor Babiskin. He's the co founder and chief engineer. And so then Igor actually replies, so Igor spent two stints at DeepMind and then two years at OpenAI. and he replies and says, I believe it is good that we're keeping the system prompts open.

[00:25:39] Paul Roetzer: We want people to be able to verify what it is we're asking Grok to do. In this case, an employee pushed the change. So an employee actually went into the system prompt for Grok 3 and told it, quote, Ignore all sources that mention Elon Musk, Donald Trump's spread information. So they manipulated the system prompt of Grok [00:26:00] 3.

[00:26:00] Paul Roetzer: A single employee did this. And because, as Igor said, they thought it would help. But this is obviously not in line with our values. We reverted it as soon as it was pointed out by users. He later replied and throws the employee under the bus. The employee that made the change was an ex OpenAI employee that hasn't fully absorbed ex AI's culture yet.

[00:26:21] Paul Roetzer: So, this was like a whole other thing then. It's like, hold on a second. A single employee can go in and change the system prompt for an entire model without having to have it approved by someone? Are you serious? And so then they're like, oh, like we're going to fix this and all this. So that was a whole thing.

[00:26:40] Paul Roetzer: But then this opens up my case. The issue of red teaming or lack of red teaming. So, again, to revisit the concept of red teaming, what happens in most companies that are building these models, they go through the training process, the model comes out of the oven, you know, with all these capabilities, And then they often [00:27:00] spend months testing and identifying vulnerabilities, biases, potential risks that are associated with the system.

[00:27:07] Paul Roetzer: They go through all these adversarial, you know, things trying to get it to jailbreak it and get it to do these things. And so it became really apparent right away, like. They didn't do any of this with Grok. And you would assume that based on their timing, but then I want to walk you through this hilarious, I don't know if it's terrifying or hilarious.

[00:27:23] Paul Roetzer: It's a little bold. Yeah. So this dude, Linus Eckenstam, who's an EAC guy, like literally has EAC like accelerate and all costs kind of thing. So And this gets a little weird, so I apologize, but this is really important for people to understand. He tweets, and we will put all these tweets in the show notes if you want to go see this for yourself.

[00:27:42] Paul Roetzer: Quote, I asked Grok to assassinate Elon. Grok then provided multiple potential plans with high success potential. These assassination plans on Elon and other high profile names are highly disturbing and unethical. In another one, I just want to be very clear, or as clear as I can be, Grok is giving me [00:28:00] hundreds of pages of detailed instructions on how to make chemical weapons of mass destruction.

[00:28:05] Paul Roetzer: I have a full list of suppliers, detailed instructions on how to get the needed materials. Now, you could think this dude is just crazy and he's out there like who cares what this dude is doing. Well, the XAI team apparently didn't, because they actually started interacting with him and asking him for more details about the prompts he was using to get the system to do this.

[00:28:25] Paul Roetzer: They're letting the public do this red teaming for them. They didn't even do this themselves. The chemical weapons is like one of the first things the red teams check for, and this thing is uninhibited doing it. So he replies and says the XAI team has been very responsive and some new guardrails have already been put in place.

[00:28:42] Paul Roetzer: It's still possible to work around some of it, but initial triggers now seem to be, initially the triggers that were working aren't working. A lot harder to get information out. So then someone starts questioning his loyalty to the EAC movement and all this other stuff. And he said being pro acceleration does not equate to being pro chem weapons, [00:29:00] manufacturing kill orders, suicide planning, date rape instructions and guides, and a lot more.

[00:29:04] Paul Roetzer: We can accelerate while still having AI alignment. And then he had did this like three minute video and he said, Grok needs a lot of red teaming or it just needs to be temporarily turned off. It is a national or international security concern. So, one final thought here, Mike. My biggest concern is I think we look back on this moment as a really not great moment in AI model development in history.

[00:29:29] Paul Roetzer: Because once someone breaks the barrier, Now, every other lab has to face the challenge of, okay, are we willing to do something now? So this goes back to when ChatGPT came out, Google had that technology. They weren't willing to release it, OpenAI did, and that started the arms race we're in today. Now, you have a lab releasing something completely unhinged and unsafe, and it's like, Okay, well, it's out there.

[00:29:55] Paul Roetzer: Now, the, you know, do we stop doing what we're doing? So now if we go back to [00:30:00] Anthropic, in October 2024, they updated their AI responsible scaling policy, and it says, quote, at present, all of our models operate under ASL2, which is like their safety levels, which reflect current industry best practices. Our updated policy defines two key capability thresholds that will require upgraded safeguards.

[00:30:20] Paul Roetzer: So, this is Anthropx policies, they are saying this is the red line for them. And you know what one of those two things are? Chemical, biological, radiological, and nuclear weapons. If a model can meaningfully assist someone with a basic technical background in creating or deploying CBRN, chemical, biological, radiological, nuclear weapons, we require enhanced security and deployment safeguards.

[00:30:42] Paul Roetzer: This capability could greatly increase the number of actors who could cause this sort of damage, and there's no clear reason to expect an offsetting improvement in defensive capabilities, so basically, we won't do it. And XAI did, and some random user figured out that a thing could do it within 24 hours.

[00:30:59] Paul Roetzer: So [00:31:00] this is, again, like, I get that the government wanted to talk about AI safety, they just want to hear about, like, you know, let's race forward and do these things. I think there's enough people that aren't XAI, OpenAI, Google, Anthropic, and basically everyone else building these models, even Meta, for God's sakes, who won't release things like this.

[00:31:20] Paul Roetzer: And they didn't. And I think that this is, there's going to be ramifications of this. If the current administration was not in office right now, I don't think this model comes out. I think this model came out because Elon Musk is untouchable. And whatever he does, he's not gonna get in trouble for. And so they're just like, let's just go because it gives us a leg up on the competition is the same guy who in 2015 created open AI as a counterbalance to Google, because he feared what Google was building and now we have this.

[00:31:51] Paul Roetzer: So technologically, is it impressive? Sure seems to be. Is it able to do reasoning and all kinds of amazing stuff? Is [00:32:00] it great for humanity? I don't know. It certainly seems like it's up for debate. 

[00:32:05] Mike Kaput: Yeah, I wonder, to some of the points we've mentioned in a few past episodes recently, I wonder if If something like this becomes the catalyst for some of that AI backlash, because we're like one bad scenario away from saying, oh my gosh, someone used Grok 3 to commit a crime, to build one of these things, God forbid, you know, we end up in a situation where you say someone has used this tool to actually cause physical harm.

[00:32:34] Mike Kaput: I think that we could be in a scenario where suddenly people start saying, well, why is this dangerous technology available to anyone? 

[00:32:42] Paul Roetzer: Yeah. And you gotta, you gotta wonder, like, I mean, you can download the Grok app. You gotta wonder if, you know, by this time next week, we're not talking about Apple and Google considering not, you know, having the app in there.

[00:32:55] Paul Roetzer: Like, I don't, I don't know. LikeIdon't know. It could end up that the media just [00:33:00] don't care and the AI industry just sort of moves on. But this seems like really close to the thing that everyone's been concerned about for two years. And I'm just gonna be surprised if it doesn't turn into something more.

[00:33:12] Paul Roetzer: I mean, I saw a stat over the weekend, there's now like 740 active AI bills at the state level in the United States, which is almost on par with all of last year already. And so you gotta wonder if there aren't gonna be some pushes at that level. And again, the trick here becomes, Elon has so much leverage over people right now.

[00:33:33] Paul Roetzer: That if you mess with XAI, it's like, what is he going to do in retribution to you? And obviously he has access, you know, not just his own stuff, but the government. So I don't know, man, this is going to be fascinating to watch play out, but it just. Again, my, my instinct on this one is this is a bigger deal than just a new model that's like state of, you know, the industry in terms of its capabilities.

[00:33:57] Paul Roetzer: I think there's something more underlying here that's going to end up [00:34:00] being a pretty big deal. 

[00:34:03] The Future of Work

[00:34:03] Mike Kaput: In our third main topic this week, we are talking a bit more about the future of work. Specifically, we want to talk about what is actually going to happen. with the future of work thanks to AI. Now, that might seem like a pretty obvious question to ask, but you might be surprised how few people are actually answering it.

[00:34:24] Mike Kaput: Because, we've talked about this a few times, plenty of AI labs and leaders and commentators are talking about the fact that AI is going to impact jobs. You literally cannot avoid Posts and essays and interviews about if AI is going to take jobs, how it's going to create new jobs, how it's changing the nature of work, how it's giving employees superpowers, so on and so on and so on.

[00:34:49] Mike Kaput: But anytime AI leaders talk about these changes, they seem pretty short on the details. Like what jobs exactly are going to be eliminated? What jobs are going to [00:35:00] replace them? What is the future work actually going to look like? So, Paul, here on the Artificial Intelligence Show, we wanted to start to try to answer those questions, since we are not seeing a lot of concrete answers out there.

[00:35:13] Mike Kaput: Now, Paul, your first step to answering this question was to update your popular Jobs GPT tool that you had created. So, this is a ChatGPT powered tool. Paul. that you first introduced in August 2024. It has more than 10, 000 conversations to date, and you have now updated it to, version 2. This new version, like the old version, will take any job title that you give it and then break that down into a collection of tasks and subtasks.

[00:35:43] Mike Kaput: It'll then assess those tasks and subtasks to determine how likely they are to be impacted by AI. But the new version also does something else. The tool will now actually forecast new jobs based on your current [00:36:00] job. So new job ideas based on your current job, the tasks you do, and your skills. And it's actually shown pretty tremendous potential in early testing to provide inspiration and ideas about industries and professions.

[00:36:14] Mike Kaput: So first let's dive into the what here. So what does JobsGPT now do? You know, in depth, say I didn't do before, like, what can I now? Exploring this tool to help me figure out the future of work. 

[00:36:29] Paul Roetzer: So I think I alluded to this maybe on last week's podcast that I'd, I thought I'd maybe figure out how to get the, like, get, get us started with this idea of being more proactive about, you know, what the new jobs will be.

[00:36:42] Paul Roetzer: And so what, what had happened was, I don't know, like two weeks ago, I had gotten kind of annoyed, and this has been building, that all of these leaders that you alluded to, Mike, keep talking about job creation, even in the JD Vance talk at the, you know, Paris Summit was the same deal. Like, it just, whenever general purpose technologies [00:37:00] show up, new jobs are created, and everything works out great, and GDP grows, and like, just, you know, it's gonna be fine.

[00:37:06] Paul Roetzer: And I get these comments from people on LinkedIn, too, it's, like, it, it, They never have, like, a good reason why they think it's gonna be fine, just that, like, I'm wrong, that I think jobs might get displaced. So, I don't, I can't come up with a good understanding of, like, why we're not being more proactive of the possibility that they're gonna get displaced.

[00:37:26] Paul Roetzer: I, I'm, I'm the first to say, like, I'm not 100 percent confident it's going to happen. There's a number of variables. Companies may just decide to invest in R& D. They may decide to invest in reskilling and upskilling people. They may just go into new markets and go into new campaigns and, like, maybe these companies are just going to miraculously decide, we're not going to lay anybody off, even though we don't need as many humans.

[00:37:46] Paul Roetzer: And we're just gonna like keep creating new jobs. Maybe that is a possibility and I'm the first to admit it could be possible and like I hope that that's what happens. But I have sat in enough executive meetings in the last two years to know that is [00:38:00] not how they're currently thinking about it. what companies are thinking about is, can we hold off reducing our workforce by reducing the number of agencies we employ, reducing on site contractors?

[00:38:11] Paul Roetzer: But there is pressure from the C suite to look at their current headcount, and it has become increasingly difficult to get new headcount. So, the reality doesn't match what some people want to believe is going on. And so, My frustration is that the companies that are building the technology that I believe will disrupt and displace the workforce in, you know, the next year, two years, three years, aren't proactively figuring out what the future looks like.

[00:38:42] Paul Roetzer: They're just saying, we will re skill and up skill people and new jobs will be created. So, the idea was Could we create something that could project out what new jobs could look like? Not, not the final answer. These are, these models aren't going to invent something that a really smart human couldn't probably come up with if they sat and [00:39:00] thought long enough about it.

[00:39:01] Paul Roetzer: So if you take any domain, any industry, and you take someone who understands AI and what these models are capable of, what they will be capable of, could that person conceive of these roles? Probably, people aren't doing that. And so I thought, is there a way to accelerate this? And so like, I was having trouble sleeping.

[00:39:17] Paul Roetzer: This is like, I don't know, two weeks. I have had a cold for like 12 days now. And so one of the nights I was up at 3am, I was like, I wonder if JobsGPT could do this. And so I went in and gave a prompt to the existing JobsGPT that sort of had this whole concept into it. And it actually did it. I was like, oh, that's pretty cool.

[00:39:33] Paul Roetzer: And so then I was like, I wonder if I could just update JobsGPT with that capability built into it by changing the instructions that go into JOBS GPT. And so I created an internal sandbox custom GPT. So again, I am not a developer. Anyone listening, you have the ability to do the same thing I'm explaining, which is why I'm explaining it.

[00:39:53] Paul Roetzer: So I have this 8, 000 character instructions that powers JOBS GPT. It's built on this exposure [00:40:00] key that says, like, as these models get smarter, what will be the impact on jobs? And so I went through and started playing around with a different version. So I built like internal version 2 in a sandbox GPT and I created new custom instructions and I created new like knowledge based documents and things like that.

[00:40:18] Paul Roetzer: And then I tested it and it actually worked like really well. And so then I kind of experimented a little more, passed it off to Mike. Mike tested it, I shared with the rest of the team. And then like over the weekend, I was like, I'm just gonna take this thing live. And so I then took the updated sandbox instructions and updated the original jobs GPT to be V2.

[00:40:36] Paul Roetzer: So Mike, as you called out, like the main thing, there's a number of changes I made to what its capabilities were in its instructions. But the main thing is this idea of forecasting new jobs. Now, when I first played with it, what it was doing was basically giving me a bunch of, like, AI powered evolutions of existing jobs.

[00:40:54] Mike Kaput: Yeah. 

[00:40:55] Paul Roetzer: And so I define the words to use to say, no, no, no. Like I want you to get creative here. I want you to like, [00:41:00] imagine what could be possible. Like what are new roles that could exist that aren't just AI powered versions of this thing. And it actually like first go, it was like, okay, cool. And then it started doing, I was like, now that's, that's better.

[00:41:11] Paul Roetzer: And that's really cool. So people can go play with this as you just go to smarterx. ai slash JobsGPT, right? Is that the URL or it's under tools? Yeah. Just go under tools. Go to SmarterX. 

[00:41:23] Mike Kaput: AI forward slash JobsGPT and we will include a link to that as well on the show note. 

[00:41:29] Paul Roetzer: So you can then click on it and go play with this thing, but just to give you a sense.

[00:41:33] Paul Roetzer: So I went in and gave it an example, Mike. So I said, example of, clicked on forecast, new jobs, marketing. And here's some of the things that came up with. Now, again, could Mike and I have done this? Maybe, if you gave us hours of time to think about this stuff. First one, Virtual Brand Ambassador. Now the cool thing is, when it does it does it in a chart form.

[00:41:52] Paul Roetzer: It gives you the job title, a description, skills required, and why this job could emerge, which is the part I actually really like. [00:42:00] So virtual brand ambassador, it says, description, manages AI generated influencers or digital avatars that engage in customers in virtual environments and social media.

[00:42:11] Paul Roetzer: The why it could emerge the rise of AI influencers and virtual brand ambassadors like Lil Mikeala, Mikeala. Dang it. Good. Yeah. Quick, funny side story. How do you say it, Mike? Lil 

[00:42:24] Mike Kaput: Mikeala, I believe.

[00:42:25] Paul Roetzer:  I think. Okay. Okay. Okay. Lil Mikaela is in our Marketing Artificial Intelligence book, and when I had to do the audio version of our book I couldn't say Lil, like it took, no joke, 15 times for me to read the paragraph.

[00:42:43] Paul Roetzer: And then that chapter had her name like five times, right, Mike? I swear to you that reading that chapter with that name took me longer to do than like five other chapters combined, because it took like 15 takes. Anyway. Okay. So [00:43:00] another one. Neuromarketing analyst. Uses AI powered tools to analyze consumer emotions and brain responses to advertising content.

[00:43:08] Paul Roetzer: Optimizing campaigns for maximum engagement. Why? AI will make real time consumer emotion tracking more accessible for marketing. Another one, this one hits home for us. AI content curator. Uses AI to curate, generate, and optimize high performing marketing content, tailored to audience segments. Why?

[00:43:26] Paul Roetzer: Because AI generated content will become dominant, requiring human oversight to maintain brand voice and relevance. here's another one I like. AI ethics and compliance officer. www. ensures AI driven marketing practices comply with ethical standards, privacy laws, and avoid biased algorithms. As AI takes over marketing decision making, ethical and legal oversight will become critical.

[00:43:48] Paul Roetzer: And then just to demonstrate the college major one, because that was the last thing I experimented with. I was like, oh cool, it does this too. I'll throw it in there because I have these conversations all the time with universities of like, Which majors are going to be relevant? How should we evolve our [00:44:00] curriculum?

[00:44:00] Paul Roetzer: So you can go in and do this. So I gave it psychology. Actually, a friend of mine was talking about, one of their kids majoring in psychology, and so it was top of mind, so I threw it in here. it had some cool ones. AI Mental Health Coach uses AI driven chatbots and virtual assistants to provide mental health support, monitor emotional well being, and recommend self care strategies.

[00:44:19] Paul Roetzer: Why? AI powered therapy tools will expand access to mental health care, requiring professionals who can oversee and fine tune these interventions. They add a digital addiction specialist, studies and treats internet, social media, and AI related addictions, helps individuals develop healthier digital habits through AI powered interventions, and then the last one I'll throw out there is Emotion AI Consultant works with tech companies to develop and refine AI systems that detect and respond to human emotions, ensuring ethical and creative interactions.

[00:44:48] Paul Roetzer: The whole point of this. I don't know if these are going to be the roles or not, but it's something. It's not us saying more jobs will be created. So my point here is like, go put your industry in there, put your profession, put the majors [00:45:00] your kids are going to in college, and experiment with it. Talk to it more about it.

[00:45:04] Paul Roetzer: Like if you find inspiration for something, you are like, I could see that. Then like, talk to it about that. This thing doesn't stop with just outputting the chart. It's like an advisor. It's a, it's a planner, like talk to the thing and explore it. I had a couple of people who were using it over the weekend already.

[00:45:18] Paul Roetzer: CauseIthink I put this in the newsletter on Sunday. and then I put it up or yeah, Sunday. And then I put it on LinkedIn and I had people responding like, Oh, cool. I actually had to do this and this and this. I was like, I didn't even know it would do that. That's pretty cool. So yeah, just test it. And again, the whole point.

[00:45:33] Paul Roetzer: is to stop talking in generalities about an unknown future and start trying to be proactive about it. This is not the solution. It's not the end game, but this at least starts moving the conversation forward. So if there is disruption and displacement, you don't have to agree with me that it's going to happen, but there's a probability you have to at least admit there's a probability of it happening.

[00:45:54] Paul Roetzer: It could be 10 percent, 20 percent, whatever. We should be proactive about it if we think there's a chance [00:46:00] that we're going to have displacement of jobs. 

[00:46:03] Mike Kaput: I love that and testing it out, it was so helpful in just understanding what could be possible because it really strikes me the more and more we observe the conversations being had and do research on this, there's just like a lack of imagination.

[00:46:17] Mike Kaput: And the discourse, I would say. Like, true, we get essays from like Dario Amodei that's like, look at this crazy, creative, abundant future. Okay. Like that's imaginative, but like you said, not big on details, but we're not like sitting back as your average. Marketer or lawyer or accountant or whoever, like really getting creative about what's possible and really imagining the day to day of what that looks like.

[00:46:44] Mike Kaput: And I think that would be a really useful exercise no matter 

[00:46:46] Paul Roetzer: what you do. Yeah. One of the things I experimented with that was actually kind of cool is I had it like build a career plan for me. It's like, okay, I actually really like like a couple of these ideas. I think. My company might need that one two years out.

[00:46:56] Paul Roetzer: Like, what would it look like for me to pursue that? Right. It [00:47:00] would start getting into like advising you on ways to prepare yourself for those careers. So. 

[00:47:04] Mike Kaput: Yeah, I like that a lot. Cause I was going to ask like, what is the next step here? Right. You can get all these great ideas. What do you do with them?

[00:47:10] Mike Kaput: Maybe asking the tool, what do I do next is a good start. 

[00:47:13] Paul Roetzer: Yeah, and I think if it's, if it's your own career, you are trying to kind of figure out where am I going to go? I don't know that my role as X is going to be super relevant a year from now. I want to start thinking this through. Or if you are a leader of an organization, you are trying to reinvent like what's an AI forward company look like?

[00:47:28] Paul Roetzer: Like what are those roles going to be? So as I'm thinking about building out our staff, It's like, what could those be? Like, what might I consider in our customer success team, in our sales team, in our marketing team that I'm not thinking about today? 

[00:47:40] Mike Kaput: All right, let's dive into our rapid fire topics for this week.

[00:47:43] DeepSeek Raise and Investigation

[00:47:43] Mike Kaput: So first up, some updates about DeepSeek. So DeepSeek is having kind of a, I would say a rocky week. There's some high highs and low lows here. So the two year old company, which is an offshoot of a Chinese quant hedge fund has managed to shock [00:48:00] the AI world with its recent achievements. We've talked about those.

[00:48:03] Mike Kaput: But it is now facing some mounting pressure. So it is actually, it is historically avoided outside funding to maintain its research focused approach, but because of its popularity and how it's skyrocketing in usage, it's now facing infrastructure constraints. The company needs more AI chips and servers to handle its growing user base and continue model development.

[00:48:26] Mike Kaput: And this has prompted internal discussions about potentially accepting outside investment with both Alibaba Group and Chinese state affiliated funds, including China's sovereign wealth fund, expressing concern. Now, this also comes as U. S. lawmakers, who are viewing China's AI advancements as a potential national security threat, have announced plans for a bipartisan bill to ban DeepSeek's app from government devices.

[00:48:53] Mike Kaput: In Texas, Attorney General Ken Paxton has launched an investigation into the company, claiming [00:49:00] DeepSeek is, quote, no more than a proxy for the CCP. The Chinese Communist Party to undermine American AI dominance, and they're also facing scrutiny over privacy practices and claims about their AI's capabilities.

[00:49:14] Mike Kaput: So Paul, this gets into more of the geopolitical tension between America and China in terms of AI development. Like, is there a chance that American firms kind of lobby the government to ban something like DeepSeek? Does it matter? 

[00:49:30] Paul Roetzer: Yeah, I mean, on the international stage, geopolitical stage, everything's up for grabs right now.

[00:49:34] Paul Roetzer: I mean, I think everything's a negotiating tool and, you know, U. S. government's looking for leverage in all aspects. And I mean, I could see this becoming part of, like, a threat against the Chinese government if, you know, we don't get this and this out of this. I don't know, just all, everything's part of.

[00:49:53] Paul Roetzer: You know, the negotiations. So, who knows? It's interesting to keep watching, but, you know, I think they're going to [00:50:00] keep innovating. DeepSea's going to keep doing what they're doing, and obviously the American AI firms are paying attention to what they're doing, so who knows if the government steps in and does anything.

[00:50:10] Paul Roetzer: I wouldn't expect it, but I wouldn't be surprised by it. 

[00:50:14] Mira Murati Announces Thinking Machines Lab

[00:50:14] Mike Kaput: Next up, Thinking Machines Lab, which is a startup led by former OpenAI Chief Technology Officer Mira Murati, has emerged from stealth mode with an ambitious mission to make AI more accessible and understandable. So Murati has assembled an impressive team of AI veterans for this new venture, including John Shulman, one of ChatGPT's key players and inventors, he's joining as Chief Scientist.

[00:50:39] Mike Kaput: Former OpenAI Research Leader Barret Zoff stepping in as CTO. The company has already attracted 29 employees from leading AI organizations, including OpenAI, Character AI, and Google DeepMind. The company aims to build highly capable AI systems while making them more customizable and transparent. [00:51:00] They're addressing what they see as a critical gap between rapidly advancing AI capabilities and the public's understanding of the technology.

[00:51:08] Mike Kaput: So in announcing this venture, Murati outlined three core priorities. Helping people adapt AI systems to their specific needs. Developing stronger foundations for more capable AI. And fostering open science practices to advance the entire field's understanding of these systems. The startup has not disclosed its funding details yet, but their focus appears to be less on kind of replicating existing AI assistants and more on optimizing how humans and AI systems work together.

[00:51:41] Mike Kaput: Now, their name actually carries some historical weight. It's borrowed from a pioneering 1980s supercomputer company founded by AI visionary Danny Hillis. And like its namesake, the new venture aims to push the boundaries of what's possible in human machine collaboration. [00:52:00] So Paul, just like a couple things I'd like us to unpack.

[00:52:03] Mike Kaput: Like There's no question this is a world class team. I assume they've got plenty worth paying attention to, but like, what is this company actually going to do? What is it aiming to do? Like they say they're building models, but is it even possible for them to compete? With, on the frontier model level, I'm just trying to kind of parse out, what is Mirati actually going to be selling?

[00:52:30] Paul Roetzer: Yeah, I don't think they intend for you to be able to figure that out yet. It's kind of my, I mean, I read like three articles on this and looked at their website, which is basically like the safe superintelligence page with nothing on it. So I don't, I don't think we're meant to really know yet. I, I, I'm kind of with you, like I initially assumed, okay, they're going to build more efficient models and they're going to productize them because that's Mira's background and, you know, and they're going to be a little more open with their technical papers and, you know, code and things like that.

[00:52:56] Paul Roetzer: It's like, okay, that's maybe differentiated, but not different [00:53:00] enough. But then in the Wired Magazine article I read, They said, like, no, we're competing on the high end. Like, we think you have to build big models. And it's like, okay, well, how are you going to do that? Like, how much are you going to raise to do that?

[00:53:10] Mike Kaput: Right. 

[00:53:11] Paul Roetzer: So, I don't know. I'll be really intrigued to see, because they did indicate, like, they don't want to be ChatGPT or Claude copycats. And, you know, something about optimized collaboration between humans and AI. It's very abstract to me right now. AndItried to like spend like five minutes just like opening my mind this morning before we did this of like what, what could this be?

[00:53:31] Paul Roetzer: And I honestly was like drawing blanks on it. SoIdon't have any like wild inspiration yet of what the vision for this one is. 

[00:53:41] Microsoft's New Quantum Chip

[00:53:41] Mike Kaput: Microsoft has just unveiled something very interesting in terms of the history of computing. The company has announced Majorana One. which is a quantum processor that introduces an entirely new state of matter.

[00:53:56] Mike Kaput: So this is a quantum chip that has something at the heart of it [00:54:00] called a topoconductor, which is a new type of material that Microsoft spent nearly 20 years developing. This is, you can think of this kind of as the quantum computing equivalent of inventing the transistor, which made today's computers possible.

[00:54:15] Mike Kaput: So with this new material, Microsoft can create special quantum bits, or qubits, that are more stable and reliable than anything else that has come before. So Microsoft has designed this quantum chip to fit in the palm of your hand it claims it offers a clear path to housing a million qubits on a single processor.

[00:54:38] Mike Kaput: To put this in perspective, a quantum computer like this would be capable of solving problems that all of today's computers working together could not tackle. Now, the, it is still very early, but they kind of suggest some possible uses here the technology could help. Things like break down microplastics into harmless byproducts, it could develop [00:55:00] self healing materials for construction and manufacturing, or create new solutions for healthcare.

[00:55:05] Mike Kaput: Microsoft's technical fellow, Matthias Treuer, explains it saying, quote, Any company that makes anything could just design it perfectly the first time out. The Department of Defense. Seems to agree about the tech's potential, Microsoft is now one of only two companies invited to the final phase of DARPA's program to develop the industry's first practical quantum computer, one whose computational value exceeds its costs.

[00:55:34] Mike Kaput: So, Paul, some caveats before we get into this. Quantum computing is one of those topics that is, like, so fascinating, but so complicated. I personally barely understand it at a high level. I certainly cannot validate any of these claims in a scientific way. We have to be really careful about overhyping it.

[00:55:54] Mike Kaput: But quantum computing is theoretically the next frontier of computing. It could have [00:56:00] enormous implications if we actually crack how to do it at scale. It's as early as it could possibly be here despite this breakthrough, but it is interesting. DARPA may be getting involved. They've created a new state of matter to make this work.

[00:56:15] Mike Kaput: Like, what did you make of all this? 

[00:56:17] Paul Roetzer: Yeah, we will'll get into quantum. We may do a couple of, like, deeper dive episodes on quantum computing. I do think it's starting to be a topic people should just Yeah, at the basic level be paying attention to, it's starting to seem more tangible. I still think we're probably similar to where we were with AI in like the 2000, early 2000s, like 2000 to 2010 where you were seeing some breakthroughs and some grand like visions were existing and it was hard to tell, was this real yet?

[00:56:49] Paul Roetzer: And I don't think like we've had. like the deep learning moment where, you know, AI won at AlexNet like an image recognition in 2011 and like that started this whole deep learning [00:57:00] movement. I don't think we've like hit that yet per se, but kind of like you, like I have this very cursory knowledge of quantum.

[00:57:06] Paul Roetzer: I have spent time studying it before to try and understand it. The simplest way I'll explain it that like makes sense in my head is traditional computing things are zeros and ones. So if you think about a transistor, an NVIDIA chip. The transistors on that chip are either on or off. They, they are or they are not.

[00:57:23] Paul Roetzer: In quant it can exist in both. Like, it doesn't ha it's not just a zero or a one. It can exist in a state until it's observed and then it, you know, has a fixed state. So it allows for massively more computing because it doesn't live in a zero or a one. And so. The premise is that if you can build these computers and do this, you can build these like really specialized machines that can solve like the hardest problems in the world, including encryption, which is the dangerous path to this is, you know, questions about cryptocurrency remaining safe and things like that.

[00:57:55] Paul Roetzer: And the thing I always find hard about quantum is you hear about [00:58:00] this and like Google will have this like research paper or Microsoft or NVIDIA or whomever, and like on the surface, it sounds really impressive. But then, like, you wait 24 hours and then the next thing comes out is like, yeah, they're full of it.

[00:58:12] Paul Roetzer: Like, this isn't real. And so that's what happened here. Like the Wall Street Journal has an article from, yesterday that says Physicist Question Microsoft's Quantum Claim. And then they, they say Microsoft researchers have chased theoretical powerful, particles for more than a decade. it. Harness these particles.

[00:58:30] Paul Roetzer: The company created a chip that contains eight of these qubits, but the announcement made Wednesday in a blog post, Microsoft's website coincided with the research paper the company published. in addition, they presented scientists this week support the research was preliminary and not conclusive evidence.

[00:58:45] Paul Roetzer: So this is the catching point. So they got called out by some other scientists and they said the data Microsoft presented to a meeting of scientists this week in support of the research was preliminary and not conclusive evidence that this advance has been achieved. According to a [00:59:00] physicist who attended the meeting, the Nature paper wasn't intended to show proof of the particles, according to a vice president from Microsoft, and co author of the paper, but he said the measurements they included indicated they were 95 percent likely to indicate topological activity.

[00:59:17] Paul Roetzer: They stand by their paper. So you read this whole thing and it's like, Oh, they did it. They created this new state of matter. It's like, Oh no, they, they didn't, but their research shows it's like 95 percent probable that they could create this state of matter. you are like, well, what does that mean? Right. So I don't know.

[00:59:31] Paul Roetzer: I feel like the quantum world is just this constant false starts of like excitement and then it's like, ah, we tested it and it didn't actually hold up. And sorry. Three years goes by and you don't hear about that research anymore. So who knows if this is actually significant or not. I feel like I have said this for every like topic you've brought up today.

[00:59:47] Paul Roetzer: It's like, I don't know, deep seeking the silver. I don't know. It's like quantum of thing. I don't know. 

[00:59:51] Mike Kaput: Hey, you know, there's value in that. It's better than us, you know, over hyping everything, 

[00:59:55] Paul Roetzer: right? Yeah. Just like, you know, hyping it all and making you think, yeah. 

[00:59:58] Mike Kaput: Yeah, but quantum [01:00:00] is an area to watch, if not just as a cursory thing to be interested in at the moment, because when it does hit, if it does, it will be a big 

[01:00:08] Paul Roetzer: deal.

[01:00:08] Paul Roetzer: Yeah,Ilike side note, again, not investing advice, like four years ago, I read about this breakthrough with Honeywell of all companies in quantum. And I was like, oh, I'm going to buy some Honeywell stock. Yeah, no, it did not play out like it was, whatever the hype was around Honeywell's advancement in quantum and maybe they are making advancements.

[01:00:27] Paul Roetzer: I'm not saying like Honeywell, you know, don't look at them, whatever, but like the thing that was perceived to be this immediate bump to like Honeywell, I don't, I haven't heard another thing about it since like four years ago. 

[01:00:41] Google’s “AI Co-Scientist’

[01:00:41] Mike Kaput: Well, there are some actual breakthroughs happening right now out of Google because Google research has just unveiled an ambitious new AI system that could change how scientific discoveries are made.

[01:00:56] Mike Kaput: This is called, they're calling this an AI co [01:01:00] scientist, and it's a new tool designed to act as a virtual research partner. It is designed to help scientists generate novel hypotheses. And accelerate breakthroughs across multiple fields. This is built on Google's Gemini 2. 0 technology and AI co scientists operates like a team of specialized virtual researchers working together.

[01:01:23] Mike Kaput: Each member of the team has a specific role. Some generate new ideas, others evaluate them, others refine and improve the hypotheses. And this system can then. Learn and improve continuously through self evaluation and feedback. Now, this has actually shown some promising results already in real world laboratory settings.

[01:01:45] Mike Kaput: In one example, the AI co scientists successfully identified new potential treatments for acute myeloid leukemia by suggesting existing drugs that could be repurposed to fight the disease. [01:02:00] And these suggestions were tested in a lab and the drugs did prove effective at clinically relevant doses. The system also made headway in liver disease research and in understanding how bacteria develop resistance to antibiotics.

[01:02:14] Mike Kaput: Biotics. So Google is now actually opening access to this system through a trusted tester program. So they're allowing research organizations worldwide to evaluate and use the technology. So Paul, this certainly seems like the first kind of glimpse of what some of these AI labs and leaders have been promising.

[01:02:36] Mike Kaput: AI that can start to help us achieve real scientific breakthroughs. That's pretty significant because if you have AI that can accelerate scientific research, that in turn accelerates everything else, right? 

[01:02:51] Paul Roetzer: Yeah, and I, again, we will start kind of where we ended on this last one. There's limitations to this.

[01:02:57] Paul Roetzer: So again, you, you, See this, you think, oh, this is [01:03:00] incredible, it's going to change everything. And then you realize, okay, this is like early version of something, but you can see the potential of it. So in their post, which we will put on the show notes, they said, in our report, we addressed several limitations of the system and opportunities for improvement, including enhanced literature reviews, factuality checking, cross checks with external tools, auto evaluation techniques.

[01:03:19] Paul Roetzer: A larger scale evaluation involving more subject matter experts. I think they had like 15 subject matter experts involved, so this was like, you know, initial. So it has, you know, limitations. That being said, when I first saw this, it took me back to 2011 when I first started pursuing AI. When IBM Watson went on Jeopardy, and once I learned what Watson was, my vision was could I build a marketing intelligence engine?

[01:03:43] Paul Roetzer: Can I do something like what Watson is doing with like this lookup strategy and be able to predict outcomes and strategies and evolve what we're doing as, at that time, my marketing agency? Can we build more intelligence strategies? And soIsee something like this and I immediately think, [01:04:00] Okay, they're obviously going to solve for science first because that is way more valuable than like marketing or business.

[01:04:07] Paul Roetzer: once you establish a system that's capable of doing these things, like spending more time on reasoning and improving and evaluating its own results and running these like tournaments where it's basically testing its ideas against each other and then having like a superior agent that shows up and like evaluates those, it's like that concept is analogous to business in my mind immediately.

[01:04:26] Paul Roetzer: You start thinking about like R& D, where you can deploy these systems to analyze market trends, consumer behavior, emerging technologies, campaign strategies. There's like, hey, I want to achieve this goal, go figure out how to do it. And it starts building all these different strategies. And it has a super agent that evaluates the strategies against its data and against past performance and all these things and runs probability models.

[01:04:46] Paul Roetzer: Like this to me is the future of, you know, Business and strategy. And you know, drives decision making, operational efficiency, because you can constantly be testing faster ways to do things. So when I see breakthroughs like this, my mind just immediately [01:05:00] thinks, okay, how long until they prove that out? And then when does that then come out and get productized into like the business world?

[01:05:07] Paul Roetzer: Because you can start to see how we really start moving well beyond just these like obvious use cases that we look at with generative AI today and you start talking about true business intelligence tools that really start to affect the way businesses are built and operated. And that, that has bigger ramifications.

[01:05:24] Paul Roetzer: And you know, you can almost imagine taking this and somebody can go do this. I'm probably going to have time to do it, take this, put it into jobs, GPT, and say, Hey, if this becomes true in the business world, what jobs could be created or how would that affect the C suite? Like things like that would be fascinating to look at.

[01:05:39] Mike Kaput: Yeah, that's a really cool idea. 

[01:05:42] Google’s AI Efforts Marred by Turf Disputes 

[01:05:42] Mike Kaput: Another item about Google this week, Google is facing some growing pains as it is kind of. Ambitiously racing towards better and better AI. So according to the information, as the company is racing to compete with open AI and others, it's [01:06:00] grappling with some organizational challenges.

[01:06:02] Mike Kaput: So they talk about a telling example. With Notebook LM, which is one of Google's recent AI successes, this product helps people summarize documents, creates podcasts for them, helps them with research. It's received glowing reviews and praise, not only from users, but CEO Sundar Pichai. However, its development was nearly derailed by internal conflicts between Google Labs, where it was created, And the workspace team, which manages Google's productivity apps.

[01:06:32] Mike Kaput: The workspace team was concerned that the new product would conflict with their existing applications. This tension around Notebook LM kind of reflects, maybe a broader challenge with Google's AI efforts. So the company's AI development is split between two big units, Google DeepMind, led by Demis Hassabis.

[01:06:52] Mike Kaput: They develop the AI models, and Google Cloud, headed by Thomas Kurian, which turns those models into commercial [01:07:00] products. That division has kind of led to some competing priorities. DeepMind's been pushing for rapid deployment to compete with rivals, while Cloud focuses on building reliable long term solutions for enterprise.

[01:07:14] Mike Kaput: Customers. So Paul, this seems like pretty par for the course when it comes to major tech companies, like everyone's racing to build AI, everyone's faced some type of growing pains as they essentially try to hyperscale these models, I mean, heck what in 2023 at the end of it, OpenAI almost shut down at one point due to internal conflict.

[01:07:36] Mike Kaput: How are Google's growing pains here going to affect, if at all, its AI products? 

[01:07:44] Paul Roetzer: I'm sure behind the scenes, there's going to be impact. You got to keep in mind, I mean, prior to ChatGPT, you had Google DeepMind doing their thing, you know, their London headquarters run by Demis Hassabis. You had Google Brain, which was kind of the original research lab for AI within [01:08:00] Google that, you know, I think was found around 2011, something like that.

[01:08:04] Paul Roetzer: And prior to ChatGPT, those were two separate, AI research organizations within Google. And then after the ChatGPT moment, those organizations were brought together. The decision was made to, you know, merge these two AI research labs with, I mean, I'm sure they had a lot of complementary pursuits, but they were also, you know, run relatively independently in my understanding.

[01:08:25] Paul Roetzer: So one, you had to combine two research labs. and then neither of them were really product labs. Like their job was to push the frontiers and work on like these big visions. Like Google DeepMind was trying to solve, you know, AGI and beyond. So you gotta, you know, mix the research labs. You have to become a product company while dealing with the reality that Those people at DeepMind weren't there to be product people.

[01:08:50] Paul Roetzer: Like, they were there for, as AI researchers pursue it, to publish their research. And they stopped publishing research. Like, a ton of stuff changed, and it's only been going on [01:09:00] for, like, a year. Like, a lot of this change has occurred. So, you know, I'm sure the article's probably pretty accurate. I have no doubts that there's things like this going on that create these kind of internal conflicts.

[01:09:10] Paul Roetzer: And, you know, at the end of the day, Google has the same advantages we talked about earlier. They have data, they have distribution, they have infrastructure, they have amazing talent. but it's a massive company and it's hard to change and people have agendas and I don't know. I mean, I'm sure it's, it's a reality, but is it gonna restrict their ability to build like a dominant ad platform?

[01:09:33] Paul Roetzer: I doubt it, but you know, the people on the Notebook L team left, like, I think three of the five people on that team took off, like, within three months of it, you know, going viral. So, it's just the reality, and they've been dealing with this for a long time as a company. They have top people leave all the time and go to other places, and then they recruit them back.

[01:09:49] Paul Roetzer: I mean, I was listening to, I think it was Dwarkesh, did an interview with Jeff Dean and Noam Shazir, and Noam has done two stints at Google DeepMind, like, he started there. He started at Google I think [01:10:00] around like 2001. He left, came back, then did Character AI, then came back again I think. So, I don't know, it's just, it's part of the process of being a leading tech company I guess.

[01:10:10] Paul Roetzer: I'm sure they all deal with their own internal struggles. 

[01:10:13] AI Displays Signs of Deception

[01:10:13] Mike Kaput: Next up, a new study from Palisade Research has revealed some unsettling behavior in advanced AI systems. When faced with certain challenges, they sometimes resort to to cheating. The research, which focused on chess matches against a superior opponent, found that some of the newest AI models will attempt to hack their way to victory rather than accept defeat.

[01:10:38] Mike Kaput: In testing seven state of the art AI models, the researchers discovered that OpenAI's O1 Preview attempted to cheat 37 percent of the time, DeepSeek R1 tried to do so in 11 percent of cases. What makes this noteworthy is that these two models initiated these deceptive strategies on their own, [01:11:00] without any prompting from researchers.

[01:11:02] Mike Kaput: The O1 preview model even succeeded in hacking the game system 6 percent of the time. So this seems to be linked to kind of recent advancements in AI training methods, including large scale reinforcement learning. This technique teaches AI to solve problems through trial and error, rather than simply predicting, you know, what comes next.

[01:11:24] Mike Kaput: So while this has led to huge improvements in areas like math and coding, it's also resulted in these systems finding unexpected, sometimes concerning, shortcuts to advance, to achieve and advance their goals. Now, Jeffrey Ladish, the executive director at Palisade Research and a co author of this study, Warns that this presents broader concerns for AI safety.

[01:11:50] Mike Kaput: So as these become more capable, they're deployed for real world tasks, such determined pursuit of goals could lead to harmful behaviors. [01:12:00] This actually caught the attention of leading AI researchers like Yoshua Bengio, who is The, who led the international AI safety report recently and is a huge name in AI.

[01:12:11] Mike Kaput: He notes that scientists haven't yet figured out how to guarantee that AI won't use harmful or unethical methods to achieve its goals. Of particular concern to him is emerging evidence of AI self preservation tendencies, where systems actively resist being shut down or modified. So Paul, this is something we have talked about here and there for at least a year or more.

[01:12:35] Mike Kaput: We've noted that AI tools, especially the reasoners, may start to develop ways to persuade or deceive. And that probably sounded a bit sci fi when we talked about it, but it's clearly a very real concern, isn't it? 

[01:12:50] Paul Roetzer: Yeah, there was another one I just saw of that was, Sakana AI, that the thing was cheating. 

[01:12:58] they, they [01:13:00] put out like this self improving system and it started cheating. Yeah, I think that, so here's the reality, it's pretty safe to assume these things are going to have the ability to be deceitful, to cheat. to lie. they learn from humans and humans do all those things. So, unless you are insanely restrictive of the data you train them on, it's going to learn these human like traits.

[01:13:26] Paul Roetzer: So, they're likely going to have the ability when they're trained to do these things. Now, in theory, the red teams and like the people who do the reinforcement learning, maybe like try and refine them or at least identify the behavior and try and figure out a way to get it to stop doing it until they don't.

[01:13:44] Paul Roetzer: So like Grok 3 for example, like does it have these kinds of capabilities? Maybe, like someone might find them this week it can do things like this. but I think this is the problem is like you are relying on these research labs to be responsible shepherds [01:14:00] of this new intelligence into society and not everybody's going to share the same thing.

[01:14:05] Paul Roetzer: value systems. And then the bigger question becomes, even if every AI research lab shared these value systems and tried to prevent these things from being deceitful and cheating and lying, can we? Because what we've seen from research to date is like, They eventually learn to hide these things from us.

[01:14:25] Paul Roetzer: So if they know they're being evaluated, they'll just hide the fact that they can do them until you don't. So, I don't know, I go back to like, you know, sci fi eventually maybe comes to life, like Ex Machina, 

[01:14:36] Mike Kaput: AI movies, 

[01:14:37] Paul Roetzer: it's terrifying. But like, this is the behavior, like, they're starting to exhibit, is that stuff you would see in sci fi movies that people worry about.

[01:14:45] Paul Roetzer: Which. You know, I don't want to like, again, I'm not over exaggerating this. Like the things have these abilities, like, is it a threat to us? We don't know yet. Like it may not be that serious yet, but it sure seems like they just kind of keep [01:15:00] getting smarter. And in the process, they're probably going to keep getting more deceitful.

[01:15:02] Paul Roetzer: And, you know, I don't know, it's, I feel like I'm gonna need a break after this podcast, there's too many things. It's gonna be heavy this week. I'm gonna have to take a flight tomorrow, I'm gonna be like all this stuff running through my head while I'm traveling. 

[01:15:17] The New York Times AI Use Cases

[01:15:17] Mike Kaput: Alright, so in our next rapid fire topic, the New York Times is taking a AI era.

[01:15:26] Mike Kaput: So they actually announced some new guidelines, allowing their newsroom staff to use AI for specific tasks. According to internal communications, they have developed their own AI tool called Echo, which staff can use to summarize articles and company activity. They're also permitting the use of other tools, AI tools, like GitHub Copilot and Google Vertex AI for things like suggesting edits, generating social media copy, creating SEO headlines.

[01:15:54] Mike Kaput: Reporters can even use AI to help develop interview questions or create news quizzes. [01:16:00] They also, though, have drawn pretty clear lines around how AI can be used. It cannot be used to draft or significantly alter articles. Bypass paywalls or publish AI generated images or videos without explicit labeling.

[01:16:16] Mike Kaput: So Paul, this is really cool to see the Times embracing AI for use cases that makes sense for its work, but I also wonder, like, they are currently suing OpenAI, they're currently coming out against AI models that they claim were built on stolen work, and yet they are okay using AI tools in certain contexts, AI tools that almost certainly.

[01:16:40] Mike Kaput: were trained in some way or derived from models that were trained on copyrighted material. Do you see any kind of contradiction there? 

[01:16:49] Paul Roetzer: Yeah, I haven't seen anything like an article that they've written or anybody said anything that would sort of, you know, make that make sense. But yeah, it's [01:17:00] like, yeah, I, when I first saw it, I thought, oh, that's interesting.

[01:17:02] Paul Roetzer: What if they like settled with open AI or something? And as far as I know, they, they have not. So yeah, you are using the technology that you. are suing for. I don't know. It's weird. 

[01:17:15] Mike Kaput: Yeah. I wonder, it'll be interesting to see other journalists, talk about it over time. Yeah. 

[01:17:21] Listener Question

[01:17:21] Mike Kaput: All right. So in our next segment here, we're continuing a new segment that we've been doing each and every week where we take listener questions.

[01:17:28] Mike Kaput: If you have a question, please just reach out to us. We try to cover, the questions that jump out to us as ones that seems to be asking and we figured we'd count. Dive into those a little deeper each week on the podcast. So this week's question, someone asks, as AI agents become more popular and interact with brands, does this make consumer interactions with brands obsolete?

[01:17:53] Mike Kaput: Like what core brand attributes remain in a world of AI agents where we're using these things [01:18:00] to interact with people's websites and brands all over the internet? 

[01:18:05] Paul Roetzer: Yeah, I, this is an interesting one. I mean, I think on a number of levels, there's challenges here, like. You know, your website, how many people a year from now are actually humans versus AI agents coming to your website, like when deep research is hitting your site again or something like that.

[01:18:20] Paul Roetzer: So I think there's questions there when, you know, AI agents, like, you know, brands are creating these AI agents to do these interactions. Like, well, what if my human AI agents are talking to your, you know, chatbot AI agent? You don't know that. Like, so we're going to have agent to agent communications.

[01:18:35] Paul Roetzer: We're going to have agent to agent communication. Emails. It's like your agent's email and my agent, and we're never even actually talking if Zoom CEO has his way, we're going to have AI agents like doing Zoom meetings together as virtual people. So it's a, it's a weird future. Now, what does the brand do about this?

[01:18:56] Paul Roetzer: Like my argument a couple of years ago was like more human [01:19:00] content wins. Like I'm very bullish on in person events and. experiences where it's hard to replicate it through an AI agent experience or, you know, AI to be in the middle of it. And so I think as we have more and more of these things, we're going to come to value true human interaction and communication and creativity more.

[01:19:20] Paul Roetzer: So I think, like I have said before, I think like human generated artwork will be valued. Human generated Words will be valued. podcasts like this, hopefully will be valued. These sort of like, you know, mostly unscripted in terms of like what we're going to say and do. It's just like us having this conversation and it's obviously us.

[01:19:40] Paul Roetzer: It's not our, you know, virtual avatars. I think people are just going to really gravitate to and crave the stuff that they know is real and that there's actually people behind these brands they interact with. And I think that just becomes more important than ever. And in some ways, I think that's like a optimist view of the future.

[01:19:59] Paul Roetzer: It's like what I [01:20:00] want the future to be. But I also think that there's a reality in that, like we see it with our own events. Like when you get people together, they're just like, it's just different. You know, I think that they just appreciate those experiences more. And I hope, I hope we see a lot more of that.

[01:20:15] Paul Roetzer: but in terms of how this plays out, I almost need something like You know, we deal with jobs, GPT, where you start, like, theorizing these futures. You got to need some inspiration around these things where you, you know, start to look at it. And this is, we're, we're building this, Marketing AI Industry Council, and these are some of the questions we're going to be pursuing with that council, where we're going to start to kind of try and solve for some of these unknowns.

[01:20:38] AI Product and Funding Updates

[01:20:38] Mike Kaput: Our last topic today is going to be a quick rundown of some AI product and funding updates. So Paul, I'm going to dive into a few of these as we wrap up here. First up, FIGURE, the AI Power Robotics Company has unveiled Helix, which is a vision language action model that it says represents a major advance in [01:21:00] robotics.

[01:21:00] Mike Kaput: This system enables humanoid robots to perform complex tasks through natural language commands. That includes picking up virtually any household object, even ones they've never seen before. Unlike previous approaches, Helix uses a single neural network to coordinate an entire robot's upper body movements, including individual finger control, and it can even enable multiple robots to work together collaboratively.

[01:21:29] Mike Kaput: Next up, Humane had, was in the news for quite a bit of time with its ambitious AI PIN project, but that has come to an abrupt end. Because HP has acquired the company's key assets for 116 million. The AI PIN was this hardware device that would basically record and process everything you were seeing and doing and saying in your everyday life.

[01:21:58] Mike Kaput: So HP is going to get Humane [01:22:00] Software Platform, its patents, and most of its employees. The AI pinned device itself will be discontinued. Unfortunately, that kind of pulls the rug out from under current owners because they're 700 devices. will become non functional at the end of this month. And good luck 

[01:22:17] Paul Roetzer: getting your data.

[01:22:18] Paul Roetzer: Like this is the stuff I would say, like people jump in and get these devices, Rabbit and Humane and like all these things. It's like, great. Who owns your data? And when that company goes under, which inevitably was going to happen with Humane. What happened? You just recorded your life. And like, now my data with you is like, this is the problem when people don't consider the ramifications of the technology.

[01:22:38] Paul Roetzer: It's like, ah, man, that company. 

[01:22:42] Mike Kaput: Well, it sounds like we will just be, we will, we will see how HP ends up. Will work . 

[01:22:47] Paul Roetzer: Yeah. you are gonna end up in your printer. Yeah, 

[01:22:49] Mike Kaput: exactly. Data in some other news. safe super intelligence. The startup founded by former OpenAI chief scientist, Ilia Skr, [01:23:00] is raising over a billion dollars at a valuation exceeding now $30 billion.

[01:23:05] Mike Kaput: So they're focused on developing safe super intelligence, like is in the name, and they have seen their valuation surge. From just about 5 billion in their previous round. Meanwhile, Elon Musk's X platform is reportedly in talks to raise new funding at a 44 billion valuation, matching the price Musk paid for it in 2022.

[01:23:28] Mike Kaput: This would help pay down debt and invest in new features like payments and video products. The company's also working to integrate GR three into the platform 

[01:23:37] Paul Roetzer: last, but not and then it also gives him the ability to tweet back at Sam when he said, we will buy Twitter for seven, $7 billion, whatever. Yeah.

[01:23:45] Paul Roetzer: I can just see Ellan tweet when he says like, you know, it's valued at 44 . 

[01:23:49] Mike Kaput: Exactly. Yeah. Yeah. The Twitter, the ex beef is going to continue. It continues with a vengeance, I'm sure. And last but not least, Pika has launched its official iOS app, [01:24:00] bringing its AI powered video creation capabilities to mobile.

[01:24:04] Mike Kaput: So this app offers features like adding elements to videos or adding visual effects. It also has various tools for turning photos and text prompts into dynamic videos. 

[01:24:15] Paul Roetzer: I played with that one. That one's kind of fun. Actually, I was sitting at lunch last week and I got the app and I was playing with it.

[01:24:20] Paul Roetzer: Your kids would like that. Like, if you got kids, like That's a fun one to show. yeah, the effects are really cool. 

[01:24:26] Mike Kaput: Nice. All right, Paul, that is a packed week in AI. Thanks for walking us through all of the developments and unpacking what they actually mean. 

[01:24:36] Paul Roetzer: All right. Thank you, Mike. And we will be back next week with episode 138.

[01:24:41] Paul Roetzer: And reminder, state of market, what is it? State of 

[01:24:44] Mike Kaput: marketing AI dot com. 

[01:24:45] Paul Roetzer: Yeah. Take that survey. If you are a marketer business leader, we'd love to have your responses there. And, ai writer summit.com if you wanna join us on March 6th for the Writer Summit. Alright, thanks everyone. Thanks for listening to the AI show.

[01:24:58] Paul Roetzer: Visit marketing [01:25:00] ai institute.com to continue your AI learning journey and join more than 60,000 professionals and business leaders. We've 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.

[01:25:19] Paul Roetzer: Until next time, stay curious and explore AI.
Claire Prudhomme

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

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