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Speaker 1: Welcome to Tech Stuff. I'm os Voloshen, and I'm excited

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Speaker 1: to welcome journalist isabel Busquett to talk about two of

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Speaker 1: her recent Wall Street Journal stories that caught my attention. First,

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Speaker 1: there's a battle happening on LinkedIn. It's a real sharks

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Speaker 1: versus jets, the moment between finance bros And tech bros.

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Speaker 1: And it's all part of this bigger picture of software

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Speaker 1: companies being existentially threatened by AI. Then, Isabelle tells us

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Speaker 1: about last week's GtC, the Massive Developer conference held by

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Speaker 1: the multi trillion dollar company in video.

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Speaker 2: Welcome, Isabelle, Thank you so much for having me.

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Speaker 1: Now this is I have to say I admire you

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Speaker 1: tremendously because you went literally straight from GtC on Tuesday

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Speaker 1: evening to the Red Eye to the podcast to do this.

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Speaker 3: Yeah.

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Speaker 2: Yeah, that's right.

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Speaker 3: I had a brief little breakfast at home first, but yeah,

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Speaker 3: fresh from San Jose.

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Speaker 1: So you're not a correct engineer, but you're a correct journalist. Yeah.

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Speaker 1: But I want to ask you what about GtC. But

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Speaker 1: the piece that caught my attention this week was about Bloomberg,

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Speaker 1: and I have to confess I used to work this,

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Speaker 1: so obviously I was particularly intrigued. Yeah, what happened? What

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Speaker 1: was this throwdown and how did it come about?

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Speaker 3: Okay, so I cover enterprise AI, and so for a

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Speaker 3: while we've been seeing crazy releases, these new models, these

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Speaker 3: new tools are coming out all the time. It's constant.

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Speaker 3: So the more these tools come out, the more capable

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Speaker 3: they are. It starts to raise questions about how fast

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Speaker 3: they can recreate versions of popular but very expensive software.

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Speaker 3: So Salesforce is a great example of this. There haven't

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Speaker 3: been a lot of sort of alternatives in the market,

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Speaker 3: and so now with a tool like claud code, some

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Speaker 3: people are saying, oh, we can just sort of code

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Speaker 3: our own version of Salesforce. So that's sort of the

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Speaker 3: context of this conversation. There's sort of this existential threat

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Speaker 3: to software and it's really been hitting some of the

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Speaker 3: software stocks odd.

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Speaker 1: Right, I mean Salesforce at lassion.

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Speaker 3: Yeah, the salesforces the workdays of the world, you know,

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Speaker 3: companies like Legal Zoom.

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Speaker 2: There's been like I think, like a trillion.

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Speaker 3: Dollars lost in the markets over this, And you can

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Speaker 3: argue about whether it's justified or not justified, but I

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Speaker 3: think the idea is like investors are spooked.

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Speaker 1: But let's anchor this in Bloomberg. I think most people

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Speaker 1: listening to this podcast may not know what the Bloomberg

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Speaker 1: terminal even is. So if you could start with explaining

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Speaker 1: the Bloomberg terminal and then how it fits into this story,

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Speaker 1: and then why it kind of went viral on LinkedIn.

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Speaker 3: Yeah, Yeah, the Bloomberg terminal is a little bit of

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Speaker 3: a unique beast. So it's it's it's essentially a computer system.

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Speaker 3: It's the hardware and software. It came out in nineteen

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Speaker 3: eighty two, and the interface looks remarkably similar to the

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Speaker 3: way it looked in nineteen eighty two. Yes, it has

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Speaker 3: that classic dark background, gold tags, very sort of classic feel.

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Speaker 3: And this machine is like the lifeblood of financial institutions.

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Speaker 3: So traders and research teams are on it for hours

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Speaker 3: and hours a day doing everything they need to do,

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Speaker 3: from tracking real time prices of commodities stocks and they

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Speaker 3: can research historical prices, They get news alerts, they can

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Speaker 3: do all the analysis about the way that you know

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Speaker 3: certain news is impacting certain prices.

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Speaker 2: They can execute trades on it, and.

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Speaker 3: One of the most beloved and critical features is called

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Speaker 3: instant Bloomberg, and it's a chat feature where you can

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Speaker 3: chat with anyone else who has a Bloomberg terminal. And

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Speaker 3: because the Bloomberg terminal is sort of so entrenched in

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Speaker 3: financial institutions, like everybody has it, so anyone who is

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Speaker 3: a colleague, a competitor, a customer is on there, and

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Speaker 3: it's sort of created this whole network of a community

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Speaker 3: that is just like on there all the time. That's

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Speaker 3: just their operating system.

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Speaker 1: It was sort of Select before select Bloomberg Messenger and

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Speaker 1: also outside your own organization.

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Speaker 2: Yeah yeah, yeah you met.

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Speaker 1: Their wives there, I mean.

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Speaker 3: True, Yeah, there is like a whole dating scene on

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Speaker 3: instance bloom Yeah. Yeah, people definitely mete their spouses on there,

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Speaker 3: and yeah, a lot going on.

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Speaker 1: So what what was a story you wrote? And because

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Speaker 1: I saw it on LinkedIn and I saw your story

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Speaker 1: and I was just delighted. But how did this kind

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Speaker 1: of come about?

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Speaker 3: Okay, so it sort of goes back to what we

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Speaker 3: were talking about a minute ago, which is that you know,

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Speaker 3: there's been this constant stream of releases of AI tools,

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Speaker 3: and every time there's a new AI tool released, there's

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Speaker 3: this really you know, broad conversation on social media being

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Speaker 3: like Salesforce is dead, workday is dead. This is the

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Speaker 3: end of this piece of SaaS software. This is the

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Speaker 3: end of this piece of SaaS software. So it was

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Speaker 3: on the heels of the launch of this tool called

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Speaker 3: Perplexity Computer, which is sort of an agent building system.

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Speaker 3: And Perplexity Computer comes out and some folks on you know,

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Speaker 3: social media, on acts on LinkedIn start posting that they

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Speaker 3: vib coded a version of a Bloomberg terminal in one shot,

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Speaker 3: so like one one natural language prompt, and they had

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Speaker 3: a Bloomberg terminal in.

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Speaker 2: Front of them.

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Speaker 1: By the way, Bloomberg terminals generate I think about ten

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Speaker 1: billion dollars in revenue a year today.

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Speaker 3: Right, It's a massively expensive tool. I mean it costs

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Speaker 3: thirty thousand a year per seat, per user. The Perplexity

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Speaker 3: tool costs like twenty four hundred dollars a year. So

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Speaker 3: the idea that you might be able to get what

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Speaker 3: you could get on Bloomberg via one shot prompt kind

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Speaker 3: of a wild idea. It doesn't really stack up theoretically,

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Speaker 3: but I think it touches on a lot of sort

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Speaker 3: of sensitive issues that are going on here. What struck

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Speaker 3: my interest about the conversation was just that, you know,

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Speaker 3: the visceral reaction from the finance community was so passionate

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Speaker 3: and energetic and angry, and people were so protective of

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Speaker 3: the Bloomberg terminal and just responded with such backlash, saying

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Speaker 3: like you will never replace this, Nothing you can ever

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Speaker 3: do will ever replace this, you know, questioning people's sanity,

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Speaker 3: saying the you know, users should be locked up, saying

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Speaker 3: that so there was a conspiracy theory that like some

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Speaker 3: of these people were paid by Perplexity to post it,

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Speaker 3: which Perplexity is said is not true. So the conversation

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Speaker 3: just kind of like spun out of control from there,

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Speaker 3: and I just thought it was so fascinating because like

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Speaker 3: nothing has sort of stirred that visceral reaction the way

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Speaker 3: that you know, this love of the Bloomberg terminal emerged.

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Speaker 1: Do you think the kind of Jets versus Sharks a

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Speaker 1: West Side Story comparison is apt? I mean, I guess

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Speaker 1: what I'm asking you really is finance bros were the

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Speaker 1: acme of professional brohood. Yeah, for twenty years. It's almost

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Speaker 1: like I'm wondering this is just a hypothesis. It feels

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Speaker 1: like finance people have essentially been replaced as in the

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Speaker 1: hierarchy of like where value creation and becoming incredibly rich happens.

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Speaker 1: That's been kind of happening for a while. But I

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Speaker 1: like the AI moment in the last couple of years

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Speaker 1: has really accelerated there. So do you think this is

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Speaker 1: like a howl in the wind of saying like no, no,

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Speaker 1: like no tech bros, like we still got us finance bros.

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Speaker 1: Is that fair or what? Do you think?

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Speaker 3: I think, Yeah, that's an interesting idea, Like I do think,

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Speaker 3: like one common criticism from the tech bro side was that,

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Speaker 3: you know, the finance people were just they just wanted

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Speaker 3: to stick their feet in the sand. They were ignorant of,

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Speaker 3: you know, how important and how critical AI advances were

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Speaker 3: going to be, that they were stuck in their old ways,

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Speaker 3: that it was naive of them to think that there's

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Speaker 3: any piece of software that's irreplaceable. But yeah, I mean,

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Speaker 3: I think, like what's so interesting about these two groups

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Speaker 3: is that the people who work in finance just have

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Speaker 3: this love of Bloomberg, Like it is like it is

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Speaker 3: like a phantom limb for that. One person I talked

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Speaker 3: to told me like using his terminal was like crack,

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Speaker 3: which I was like, okay. And on the other side

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Speaker 3: of that, like people worship AI.

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Speaker 2: So there's this like extreme.

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Speaker 3: Like almost religious like passion on both sides, and I think.

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Speaker 2: That's sort of the root of why we saw.

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Speaker 1: This clash that's so well put. You know, a friend

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Speaker 1: of mine worked in a scrapyard in France when he

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Speaker 1: was a teenager. Briefly we told me the other day

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Speaker 1: that there was an occasion when a guy who was

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Speaker 1: like a truck driver brought his truck to be scrapped

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Speaker 1: and he'd driven it for twenty years, and as it

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Speaker 1: was kind of entering a scrapyard, he just collapsed on

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Speaker 1: the floor and was screaming like a child in the

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Speaker 1: fetal position, saying look by to his truck. And we

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Speaker 1: were talking, my friend and I about this and how like,

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Speaker 1: as a person in the world, you ultimately spend like

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Speaker 1: the majority of your waking hours working, whether that's like

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Speaker 1: driving a truck or on the Bloomberg terminal, And so

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Speaker 1: that like extreme like nostalgia and personal identification with these tools,

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Speaker 1: whether it's a truck or a terminal, it's kind of interesting,

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Speaker 1: but is there something different about the terminal from say salesforce, Like,

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Speaker 1: what do you really think makes this like such a

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Speaker 1: rubicun for finance people.

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Speaker 2: I mean, it's a good question.

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Speaker 3: I think the fact that it's been around so long

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Speaker 3: and it's had such staying power throughout that period of

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Speaker 3: time is important. I think the fact that it is

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Speaker 3: so expensive also is important. You know, when you reach

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Speaker 3: the point in your career at which you get a

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Speaker 3: Bloomberg terminal, that's a big deal. That's sort of like

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Speaker 3: a rite of passage for you, and then you have it,

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Speaker 3: and it's sort of a status symbol. So I think

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Speaker 3: it's it's come to represent all these things. But I

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Speaker 3: also think the reality is that they do love the functionality,

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Speaker 3: like they just they've sort of come to depend on

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Speaker 3: that functionality and they couldn't really live without it. And

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Speaker 3: I think it's easy to be skeptical of, you know,

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Speaker 3: some sort of flashy new AI coming in and being like, oh,

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Speaker 3: we can do this, Like I think, you know, the

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Speaker 3: natural reaction is no, you can't. Bloomberg has been doing

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Speaker 3: this for decades. This AI tool was invented a couple

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Speaker 3: weeks ago, Like, what do you people know about this?

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Speaker 3: Don't try to come in and take away our Bloomberg

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Speaker 3: terminals and give us some shoddy, cheaper vibe coded replacement

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Speaker 3: because you're taking away like what we need to do

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Speaker 3: our jobs?

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Speaker 1: And did you experiment with the vibe coded replacement? I mean,

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Speaker 1: what was the take of people who actually used these

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Speaker 1: products in terms of how close did the perplexity vibe

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Speaker 1: coded version get to successfully replicating the Bloomberg functionality.

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Speaker 3: Yeah, so I think, to be honest, on both sides,

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Speaker 3: there's an acknowledgment that whatever you can vibe code in

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Speaker 3: one shot prompt is not going to be anywhere near

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Speaker 3: the Bloomberg terminal. Like I think even the tech people

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Speaker 3: realize how I acknowledge that, And I think they would say, like, oh, yeah,

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Speaker 3: but it's a great starting point and if you put

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Speaker 3: in a little more effort and maybe it's not all

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Speaker 3: vibe coding, maybe some of it's real coding, and maybe

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Speaker 3: you can't get all the way there, but maybe you

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Speaker 3: can get close. But these sort of like one shotted

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Speaker 3: vibe coded demonstrations, it's hard to tell because the people

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Speaker 3: that post them, you can't sort of go it. They

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Speaker 3: don't always share the prompt and they don't always share

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Speaker 3: the experience. They just share like a screen grap and

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Speaker 3: so a lot of what was being shared is these

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Speaker 3: tools for like tracking stocks and financial analysis, which people

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Speaker 3: were right to point out is just one part of Bloomberg.

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Speaker 3: Like a big question that came up is sort of

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Speaker 3: like the real time data inputs the Bloomberg gets. Like

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Speaker 3: Bloomberg has these proprietary data inputs, it gets them faster

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Speaker 3: than anybody else. It gets more data, one might say,

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Speaker 3: better data than anybody else. And yes, there is stock

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Speaker 3: data and commodities data available on the Internet, but it's

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Speaker 3: maybe not the same breath and depth that Bloomberg gets.

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Speaker 3: So I think, like no, to answer your question, like

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Speaker 3: the one shotted, vibeut it apps like don't come close

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Speaker 3: to what the Bloomberg terminal is. But again, like the

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Speaker 3: tech people would say, like bo we.

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Speaker 2: Can get there.

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Speaker 1: You know what I mean, you are another story couple

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Speaker 1: of weeks ago, meet the company's vibe coding that own CRM.

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Speaker 1: What's your like medium term perspective? You do you think

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Speaker 1: software companies are like literally gonna go away or do

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Speaker 1: you think a new crop of more targeted software companies

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Speaker 1: will emerge to have less legacy baggage and then probably

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Speaker 1: be consolidated into selfwaking commorts. Again, like, what do you

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Speaker 1: think is gonna happen here.

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Speaker 2: Yeah, No, it's a good question.

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Speaker 3: I mean, like there's been talk of the death of

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Speaker 3: software for a really really, really really long time, and

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Speaker 3: it has been resilient and it has stuck around. And

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Speaker 3: you know, three years ago when Chatchibt first came out,

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Speaker 3: everyone was like, oh, it's the end of software, like goodbye,

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Speaker 3: and that I think that hasn't really happened. Although, like

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Speaker 3: I think there is like if you're a legacy SaaS

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Speaker 3: company and you can pivot into the modern age and

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Speaker 3: you can invest really deeply in AI, invest really deeply

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Speaker 3: an agents, provide that to your customers and sort of

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Speaker 3: be a value proposition that's you know, not dissimilar from

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Speaker 3: what they could get from a more AI native startup,

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Speaker 3: then I think that those companies have a chance of succeeding.

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Speaker 3: If you're a legacy sasas provider and you kind of

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Speaker 3: stick your feet in the sand and you just count

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Speaker 3: on the fact that your customers are sort of locked

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Speaker 3: into you. They have years alone contracts, it's going to

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Speaker 3: be really hard for them to exit your platform. Then

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Speaker 3: maybe you're in a situation where you're getting in trouble

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Speaker 3: in a few years. But I do like, I think

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Speaker 3: most of the SaaS companies know they have to pivot,

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Speaker 3: Like AI is AI is the big thing. AI is here.

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Speaker 3: Everybody in the software world knows that, and so they're

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Speaker 3: all sort of jostling to position themselves as the provider

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Speaker 3: of AI and AI native companies are in a like

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Speaker 3: you could argue they're in a better position to do that,

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Speaker 3: but legacy companies have at yeah, big customer bases, more

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Speaker 3: of an understanding of like what you know, business requirements

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Speaker 3: and stuff like that.

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Speaker 2: So I don't know how it's all going to play out.

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Speaker 3: People like compare this sort of AI moment a lot

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Speaker 3: to like the dawn of the Internet and sort of

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Speaker 3: like the dot com must and you know, there will

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Speaker 3: be a lot of new companies that come up and

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Speaker 3: are great, and there will be other companies that sort

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Speaker 3: of go on the corner and die and other companies

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Speaker 3: that survive.

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Speaker 1: Yeah, we'll just have to say, Well, the company, which

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Speaker 1: is in many ways at the very center of all

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Speaker 1: this is in video when we come back, Isabelle tells

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Speaker 1: us all about her trip to Invidia's develop a conference

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Speaker 1: last week, what Jensen Huang has called the super Bowl

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Speaker 1: of AIM. So before the break. We were talking about

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Speaker 1: Bloomberg and software and whether AI will eat softwa where,

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Speaker 1: And I want to talk a bit more about that

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Speaker 1: in respect to what you learned this week at the

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Speaker 1: Nvidia conference. Before we get into those specific topics, what

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Speaker 1: was it like and describe the scene.

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Speaker 3: The energy at Nvidia GGC is very unique and it's

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Speaker 3: kind of electric. It takes over the entire city of

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Speaker 3: San Jose, thirty thousand people.

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Speaker 2: I interviewed the mayor of San.

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Speaker 3: Jose last year about this and he was like, the

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Speaker 3: coffee shops make their entire year's rent in the course

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Speaker 3: of a couple days. Of course, that means like if

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Speaker 3: you want to get a cup of coffee, you are

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Speaker 3: waiting in line for like an hour. You're waiting in

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Speaker 3: line for an hour for everything to like get into panels,

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Speaker 3: to get into buildings, to get coffee, to get a

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Speaker 3: granola bar. It's, you know, like you're out there in

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Speaker 3: the wilderness, like you have to fight to get like

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Speaker 3: a sandwich.

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Speaker 1: No, the Fortralling Company doesn't provide any free coffee and sandwich.

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Speaker 4: This is no.

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Speaker 3: I mean it's like it's wild though, I mean it

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Speaker 3: does like people say, it has this like rock concert

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Speaker 3: like sort of atmosphere. There's music and there's you know,

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Speaker 3: in the evening they like bring out shot luges. There's

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Speaker 3: robots everywhere of every different kind of roy you can imagine.

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Speaker 3: Because in Video is like really invested in this physical

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Speaker 3: AI thing.

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Speaker 2: They're also really.

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Speaker 3: Invested at autonomous driving, so they have like all these

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Speaker 3: like different cars. Like everything in the city is decked

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Speaker 3: out in in Vidia signature green and it's sort of

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Speaker 3: like Disneyland for like the AI industry, Like there's just

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Speaker 3: so much to do and see and play with and

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Speaker 3: it's just like a totally different world. Everyone is super

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Speaker 3: excited about the technology. Everyone is super bullish about it.

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Speaker 3: So yeah, it's it's sort of like this little like

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Speaker 3: hype of Vortex.

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Speaker 2: It's so easy to get sucked in.

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Speaker 1: Well, how do you avoid getting secked in? What the

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Speaker 1: kind of what were the more critical questions that you

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Speaker 1: had about what was going on?

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Speaker 3: I suppose yeah, yeah, I mean I think like if

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Speaker 3: you think about Nvidia as a chip company, like there's

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Speaker 3: sort of been this inflex point in the chip industry,

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Speaker 3: which is this shift from training to inference, Like for

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Speaker 3: the last few years, like the bulk of compute has

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Speaker 3: been used to train these really big, large language models,

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Speaker 3: and now we're getting to the point where a lot

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Speaker 3: of compute is being used to run these large language models.

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Speaker 3: And so in Vidia's chips historically have been really, really

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Speaker 3: really great.

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Speaker 2: At the training.

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Speaker 3: Like that is what propelled them to their current valuation.

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Speaker 3: And I think like there were always questions about what

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Speaker 3: does it mean for Nvidia in the era of AI

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Speaker 3: and friends, And in Vidia is an incredibly innovative company

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Speaker 3: and it comes from the top. Jensen is just an

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Speaker 3: incredibly curious, innovative person. They have a very flat structure

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Speaker 3: as a company, not a lot of middle management.

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Speaker 2: Jensen and sort of like all the top leaders have

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Speaker 2: a lot of direct reports.

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Speaker 3: There's a lot of autonomy, but there's just this constant

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Speaker 3: push to just like innovate as fast as you can

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Speaker 3: and always be on the next thing, and always be

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Speaker 3: on the next thing, and always be on the next thing.

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Speaker 3: So they acquired an inference chip startup called Grock recently,

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Speaker 3: so a lot of the conference was.

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Speaker 1: There's nothing to do with Elan's Grock, This is Grock

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Speaker 1: with Q or other case.

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Speaker 3: Yeah, it's incredibly confusing and a weird name to have

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Speaker 3: twice and what is stream But yeah, so there was

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Speaker 3: a lot of talk about how they're integrating sort of

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Speaker 3: like Grock's offerings into in video offerings and you know,

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Speaker 3: just this this big push to you know, stay on

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Speaker 3: top of things on the compute side, and then on

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Speaker 3: the flip side of that, there's also you know, part

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Speaker 3: of this conference is for Jensen to get out there

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Speaker 3: and you know, prove that demands for AI workloads is

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Speaker 3: going to be greater than ever before. And that's what

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Speaker 3: he says every year, and that's what he has to

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Speaker 3: go and prove out every year because that's what's sort

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Speaker 3: of spurring their demand. And so there was a lot

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Speaker 3: of talk about the software side and what companies are

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Speaker 3: doing with AI and what they're going to be doing

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Speaker 3: with AI, and there was a lot of talk about

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Speaker 3: something called open Claw.

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Speaker 1: I want to ask you about them, and particularly how

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Speaker 1: it relates to the Bloomberg and the software story we're

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Speaker 1: talking about earlier. Just before we get there, I want

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Speaker 1: to read a quote from your newsletter. Today's maturity curve

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Speaker 1: in AI is much higher, and the explosive growth and

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Speaker 1: improvement of AI coding tools has arguably given a gentic tech.

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Speaker 1: It's killer app Still, the technology continues to speed far

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Speaker 1: ahead of business adoption. Talk about that. Why is business

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Speaker 1: adoption slower than technology development?

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Speaker 2: I mean there's a lot of reasons for that. One

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Speaker 2: is just because technology development is so fast. Silicon Valley

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Speaker 2: is moving so fast right now with the models are

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Speaker 2: coming out all the time, they're always better. And then

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Speaker 2: there's you know, last year, agents were just starting to

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Speaker 2: become a thing. So I remember being a GtC.

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Speaker 3: Last year and everyone was talking about agents, but also

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Speaker 3: nobody was really using them. There was just this understanding that, like,

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Speaker 3: agents are going to be the next thing. And it

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Speaker 3: was this transition from we have chatbots and we can

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Speaker 3: ask them questions and we can get answers and this

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Speaker 3: is incredible to Okay, agents are actually going to do

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Speaker 3: things for us. And I think last year was just

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Speaker 3: sort of like a buzzword. This year is actually starting

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Speaker 3: to happen in businesses. But the tech is sort of

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Speaker 3: chugging along even beyond that too. It's not just agents

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Speaker 3: that will do a task for you, it's now these

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Speaker 3: sort of complex groups of agents that are delegating tasks

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Speaker 3: to each other and can access your documents and files

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Speaker 3: and business data and run for a really long time

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Speaker 3: and sort of do these complex workflows and then you know,

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Speaker 3: report back to you. So it's it's even beyond just

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Speaker 3: doing tasks to like you know, doing almost like entire jobs.

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Speaker 3: But I think that's where businesses are still sort of

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Speaker 3: like the initial agentic deployment point of just like figuring out,

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Speaker 3: like where do I even like trust this type of

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Speaker 3: offer to like do a task for me let alone,

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Speaker 3: like you know, give it access to like my entire

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Speaker 3: operating system.

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Speaker 1: And where does open claw fit into all of this?

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Speaker 1: Animo Claw which was announced at GtC.

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Speaker 3: Open Claw is another thing that just sort of it's

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Speaker 3: so hard to keep up in this industry.

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Speaker 2: Someone was saying the other day.

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Speaker 3: Like you have to be fully unemployed to keep up

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Speaker 3: with anything that's going on in AI, and like I

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Speaker 3: can really respect that.

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Speaker 1: I can relate to that too. I mean I do

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Speaker 1: this podcast twice a week, but it's just the deluge

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Speaker 1: of information.

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Speaker 3: Wild It's like one day and there's this thing called

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Speaker 3: open claw, and it's really confusing because open claw is

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Speaker 3: about creating these things called.

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Speaker 1: Clause clause agents. Yeah.

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Speaker 3: Yeah, Claws are basically these long running autonomous agents. And

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Speaker 3: open claw it's it's an open source standard for building

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Speaker 3: these sort of like complex orchestrations of all these different

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Speaker 3: age So you can set up a claw to do

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Speaker 3: something and that claw will like tap a bunch of

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Speaker 3: series of subagents delegate tasks. You can give it access

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Speaker 3: to your files. It can do things for you, and

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Speaker 3: it can do it like continuously over a long period

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Speaker 3: of time. Like you could do things like summarize all

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Speaker 3: the news of the week and shoot me an email

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Speaker 3: every Monday at noon with what's going on, and it

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Speaker 3: will just sort of keep running and do that. Or

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Speaker 3: you know, you could set it to do a task

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Speaker 3: overnight and then come back and it's done in the morning.

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Speaker 3: So I think like the biggest sort of shift with

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Speaker 3: open claw is this move from I'm talking to an

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Speaker 3: agent and I'm prompting an agent and it's giving me

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Speaker 3: a response, and then I'm giving it a response, and

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Speaker 3: it's sort of this like real time interaction collaboration versus

440
00:22:45,440 --> 00:22:49,400
Speaker 3: with clause, you're really just sort of like sending it

441
00:22:49,480 --> 00:22:53,960
Speaker 3: off okay for a while, and then it's coming back

442
00:22:54,000 --> 00:22:54,600
Speaker 3: to you later.

443
00:22:54,640 --> 00:22:56,680
Speaker 2: It's like it's asynchronously working with.

444
00:22:56,680 --> 00:22:59,920
Speaker 1: A single natural language prompt or a more complicated promo.

445
00:23:00,080 --> 00:23:02,240
Speaker 1: What's the use case of open clure that you've seen

446
00:23:02,320 --> 00:23:03,320
Speaker 1: that you find intriguing.

447
00:23:03,640 --> 00:23:05,600
Speaker 3: Yeah, yeah, no, I mean that's a great question, And

448
00:23:05,640 --> 00:23:07,680
Speaker 3: that was exactly my question when I was at the

449
00:23:07,720 --> 00:23:11,639
Speaker 3: conference and in video had this experience called build a Claw,

450
00:23:11,960 --> 00:23:15,320
Speaker 3: which was this tent where everybody got to go inside

451
00:23:15,320 --> 00:23:17,800
Speaker 3: and build a claw, and I think like it was

452
00:23:17,920 --> 00:23:21,840
Speaker 3: really interesting because I went to go build my own claw,

453
00:23:21,880 --> 00:23:24,040
Speaker 3: and I just said to the video exact I was

454
00:23:24,080 --> 00:23:29,400
Speaker 3: talking to, I can't think of something that would need

455
00:23:29,440 --> 00:23:32,359
Speaker 3: to be a claw that I couldn't just do with

456
00:23:32,520 --> 00:23:38,159
Speaker 3: an agent or a chat bot. And it was just

457
00:23:38,200 --> 00:23:42,120
Speaker 3: it's this hard conceptual shift that needs to happen. It's

458
00:23:42,160 --> 00:23:44,760
Speaker 3: like it's sort of like the same way when chattyb

459
00:23:44,880 --> 00:23:47,119
Speaker 3: Tea came out, we would all talk to it the

460
00:23:47,160 --> 00:23:47,639
Speaker 3: same way.

461
00:23:47,520 --> 00:23:48,440
Speaker 2: We talked to Google.

462
00:23:48,720 --> 00:23:50,560
Speaker 3: We would just put in a few keywords because that's

463
00:23:50,600 --> 00:23:53,479
Speaker 3: the way we were used to interacting with those systems. So

464
00:23:53,640 --> 00:23:56,119
Speaker 3: now it's like when we get the opportunity to use

465
00:23:56,119 --> 00:23:58,520
Speaker 3: a claw, we're kind of interacting with it the same

466
00:23:58,520 --> 00:24:01,000
Speaker 3: way we would do with chatchybet. Like I was with

467
00:24:01,000 --> 00:24:02,880
Speaker 3: my editor and he was like, oh, help me plan

468
00:24:02,960 --> 00:24:06,560
Speaker 3: a road trip, and it could do that, but it's

469
00:24:06,560 --> 00:24:10,200
Speaker 3: like it's capabilities are so far beyond that that again

470
00:24:10,240 --> 00:24:12,920
Speaker 3: it's hard to conceptualize, like how to actually do this.

471
00:24:13,040 --> 00:24:14,960
Speaker 1: Do the invideoor exec give you any advice on what

472
00:24:15,000 --> 00:24:16,680
Speaker 1: type of clore to build or were the other people

473
00:24:16,680 --> 00:24:20,520
Speaker 1: that build? Yeah, I mean I that you came.

474
00:24:20,920 --> 00:24:25,680
Speaker 3: I remember talking to this one marketing leader who said

475
00:24:25,720 --> 00:24:28,199
Speaker 3: she had like, you know, a dozen different ideas for

476
00:24:28,359 --> 00:24:32,119
Speaker 3: clause and a lot of it is, you know, scanning

477
00:24:32,920 --> 00:24:36,080
Speaker 3: information on the internet. So her idea is, you know,

478
00:24:36,600 --> 00:24:39,560
Speaker 3: go on social media and read all these news sites

479
00:24:39,560 --> 00:24:41,840
Speaker 3: and read all these newsletters and report back to me

480
00:24:41,880 --> 00:24:42,520
Speaker 3: every morning.

481
00:24:42,920 --> 00:24:43,840
Speaker 2: What are sort of like.

482
00:24:43,800 --> 00:24:47,240
Speaker 3: The key themes of you know, what we should be

483
00:24:47,520 --> 00:24:50,840
Speaker 3: putting our marketing and thought leadership content around, and then

484
00:24:50,920 --> 00:24:53,280
Speaker 3: like help us like make sure that whatever we plan

485
00:24:53,400 --> 00:24:54,879
Speaker 3: to put out that day is like sort of like

486
00:24:54,960 --> 00:24:58,080
Speaker 3: drafted around that. Another example was an Nvidia exec was

487
00:24:58,080 --> 00:25:00,520
Speaker 3: telling me that they had this college student come through

488
00:25:00,600 --> 00:25:04,000
Speaker 3: and she was job searching, and so she was like

489
00:25:04,640 --> 00:25:07,240
Speaker 3: kind of claw helped me, you know, identify jobs to

490
00:25:07,280 --> 00:25:11,159
Speaker 3: apply for. And he was like, yes, that's like a

491
00:25:11,160 --> 00:25:14,320
Speaker 3: great chop out question. But like take it a step further,

492
00:25:14,880 --> 00:25:17,439
Speaker 3: like once it finds the role, then have it like

493
00:25:17,960 --> 00:25:20,920
Speaker 3: identify the right skills and then like optimize and tweak

494
00:25:20,960 --> 00:25:23,159
Speaker 3: your resume. And she was like, okay, great, and then

495
00:25:23,200 --> 00:25:25,159
Speaker 3: in videos, I can then take it a step further

496
00:25:25,280 --> 00:25:27,600
Speaker 3: and then give it access to your LinkedIn and then

497
00:25:27,640 --> 00:25:29,639
Speaker 3: give it access to your data and like have it

498
00:25:29,680 --> 00:25:32,680
Speaker 3: build out these coverlead It's like, have it do these applications.

499
00:25:32,680 --> 00:25:34,639
Speaker 3: And so it's like you just have to have that

500
00:25:34,840 --> 00:25:39,159
Speaker 3: mindset shift of like always take it a step further.

501
00:25:39,359 --> 00:25:42,040
Speaker 3: And I think that's not totally clear to all of

502
00:25:42,119 --> 00:25:45,159
Speaker 3: us yet unless you're, like, you know, an AI.

503
00:25:45,040 --> 00:25:46,359
Speaker 2: Super super super user.

504
00:25:46,680 --> 00:25:48,640
Speaker 3: But even since the AI super users that were at

505
00:25:48,680 --> 00:25:51,200
Speaker 3: the video conference, I think we're just like having trouble

506
00:25:51,280 --> 00:25:54,880
Speaker 3: conceptualizing like the real capabilities of what we can.

507
00:25:54,800 --> 00:25:57,840
Speaker 1: Do with this. Jensen said, this is the new chat moment.

508
00:25:58,040 --> 00:26:01,400
Speaker 1: He said that we're having the che cheapy team moment

509
00:26:01,480 --> 00:26:03,800
Speaker 1: for robotics, so having one check cheap team.

510
00:26:05,400 --> 00:26:08,679
Speaker 3: He was like he was really really all in on this,

511
00:26:08,760 --> 00:26:11,440
Speaker 3: I mean all on open claw.

512
00:26:11,520 --> 00:26:11,840
Speaker 4: Yeah.

513
00:26:12,000 --> 00:26:14,199
Speaker 3: But okay, so here's the thing about open Claw we

514
00:26:14,200 --> 00:26:16,560
Speaker 3: haven't talked about, which is that like there's a lot

515
00:26:16,600 --> 00:26:21,439
Speaker 3: of security challenges, and I did not find anyone at

516
00:26:21,880 --> 00:26:24,679
Speaker 3: the video conference who was comfortable using open claw for

517
00:26:24,760 --> 00:26:29,080
Speaker 3: any context in their actual like work slash business. So

518
00:26:29,359 --> 00:26:31,960
Speaker 3: when people want to use open claw, a lot of

519
00:26:31,960 --> 00:26:34,479
Speaker 3: times they'll go out and get a separate computer and

520
00:26:34,600 --> 00:26:36,440
Speaker 3: just run it on that computer, like.

521
00:26:36,400 --> 00:26:37,720
Speaker 1: A clean computers.

522
00:26:37,760 --> 00:26:42,760
Speaker 3: So it's like it's literally just like a separate computer. Yeah,

523
00:26:42,800 --> 00:26:46,480
Speaker 3: and just for open claw, because if you have open

524
00:26:46,520 --> 00:26:48,439
Speaker 3: claw on your computer where you have all your business

525
00:26:48,480 --> 00:26:51,840
Speaker 3: data and you're giving it access to things, it can

526
00:26:51,920 --> 00:26:53,800
Speaker 3: really I mean there's been a lot of stories of

527
00:26:53,840 --> 00:26:56,840
Speaker 3: open claw deleted all your emails or if you know,

528
00:26:56,880 --> 00:26:58,520
Speaker 3: if you have a Claw that's on the internet and

529
00:26:58,520 --> 00:27:01,919
Speaker 3: interacting with people, you know, it's not that difficult to

530
00:27:01,960 --> 00:27:03,959
Speaker 3: like prompt injected and then open clock can like give

531
00:27:03,960 --> 00:27:07,119
Speaker 3: away or credit card details. So there's so many data

532
00:27:07,119 --> 00:27:09,880
Speaker 3: concerns about open claw, which is why it is not

533
00:27:10,200 --> 00:27:11,000
Speaker 3: enterprise ready.

534
00:27:11,240 --> 00:27:13,640
Speaker 1: And the video are trying to basically with Nemo Claw,

535
00:27:13,640 --> 00:27:17,800
Speaker 1: which is their product, create a version of layer on

536
00:27:17,840 --> 00:27:20,040
Speaker 1: top of the open source open flaw to make it

537
00:27:20,119 --> 00:27:20,760
Speaker 1: business safe.

538
00:27:21,040 --> 00:27:23,000
Speaker 2: Yes, yeah, yeah, that's what they're trying to do.

539
00:27:23,400 --> 00:27:25,720
Speaker 3: I don't know if they're fully there yet I think

540
00:27:25,760 --> 00:27:28,680
Speaker 3: there's this is this tack is so new, there's still

541
00:27:28,800 --> 00:27:32,840
Speaker 3: so many concerns because a lot could go wrong, clearly,

542
00:27:33,400 --> 00:27:36,000
Speaker 3: but I think they're excited about what could go right

543
00:27:36,040 --> 00:27:37,280
Speaker 3: and they just sort of want to put it in

544
00:27:37,280 --> 00:27:37,720
Speaker 3: that direction.

545
00:27:38,080 --> 00:27:40,120
Speaker 1: The other thing which came up, I think a fabit

546
00:27:40,160 --> 00:27:41,639
Speaker 1: was space, right AI and space?

547
00:27:42,040 --> 00:27:42,920
Speaker 4: Yeah?

548
00:27:43,240 --> 00:27:45,080
Speaker 1: Was that? What else kind of surprised you and caught

549
00:27:45,119 --> 00:27:46,600
Speaker 1: your eye while you were there on the ground.

550
00:27:46,840 --> 00:27:49,119
Speaker 3: Look, the amount of announcements and video puts out at

551
00:27:49,160 --> 00:27:52,320
Speaker 3: GtC is crazy. I mean there was there were more

552
00:27:52,320 --> 00:27:53,119
Speaker 3: than a dozen.

553
00:27:52,840 --> 00:27:56,160
Speaker 2: Press releases, Like it's just wild.

554
00:27:56,400 --> 00:27:59,560
Speaker 3: But yeah, I mean computers going into space, that's one

555
00:27:59,600 --> 00:28:02,399
Speaker 3: thing you know in videos playing a lot in the

556
00:28:02,520 --> 00:28:05,200
Speaker 3: quantum landscape, Like, that's another thing we should be paying

557
00:28:05,240 --> 00:28:09,480
Speaker 3: attention to. Autonomous driving is a really big focus, and

558
00:28:09,560 --> 00:28:12,480
Speaker 3: I think that caught my eye because you know, in

559
00:28:12,560 --> 00:28:17,160
Speaker 3: video they announced a partnership with Uber to sort of

560
00:28:17,280 --> 00:28:20,080
Speaker 3: invest in this robotaxi fleet and it's coming to a

561
00:28:20,280 --> 00:28:22,400
Speaker 3: bunch of cities around the world in the next couple

562
00:28:22,400 --> 00:28:25,520
Speaker 3: of years. And you know, you can imagine a future

563
00:28:25,600 --> 00:28:29,600
Speaker 3: where like you drive to work and then while you're

564
00:28:29,760 --> 00:28:33,119
Speaker 3: in your office, you send your car out to be

565
00:28:33,840 --> 00:28:36,520
Speaker 3: an autonomous uber for the day, and then it drives

566
00:28:36,520 --> 00:28:37,520
Speaker 3: back and picks you up.

567
00:28:37,640 --> 00:28:39,920
Speaker 2: So that was another interesting thing.

568
00:28:40,480 --> 00:28:42,560
Speaker 1: Just coming back to the to the beginning of our conversation.

569
00:28:42,640 --> 00:28:46,040
Speaker 1: I mean, the kind of open law world you're describing

570
00:28:47,040 --> 00:28:50,160
Speaker 1: does sound like a world whereas seems like there will

571
00:28:50,160 --> 00:28:53,400
Speaker 1: be serious competitive threats to people paying thirty thousand dollars

572
00:28:53,440 --> 00:28:54,240
Speaker 1: a year for Bloomberg.

573
00:28:54,400 --> 00:28:57,240
Speaker 3: Yeah, yeah, I mean you could definitely make that argument.

574
00:28:57,480 --> 00:28:59,520
Speaker 3: You know, you could also make the other argument that,

575
00:28:59,640 --> 00:29:03,200
Speaker 3: you know, there's a reason Bloomberg costs thirty thousand dollars

576
00:29:03,240 --> 00:29:07,440
Speaker 3: and it's incredibly secure and they have spent many, many

577
00:29:07,520 --> 00:29:11,800
Speaker 3: years investing in you know, the algorithms that model the

578
00:29:11,880 --> 00:29:16,840
Speaker 3: pricing for this, and that I think, you know, you

579
00:29:16,840 --> 00:29:20,440
Speaker 3: could build an alternative with open claw and people will

580
00:29:20,840 --> 00:29:24,080
Speaker 3: And you know, maybe if you're just sort of like

581
00:29:24,120 --> 00:29:28,160
Speaker 3: a less serious retail investor, like that will be great

582
00:29:28,200 --> 00:29:30,160
Speaker 3: for you because you're someone who's never going to invest

583
00:29:30,160 --> 00:29:33,920
Speaker 3: in a Bloomberg terminal. But if you're like a finance company,

584
00:29:34,720 --> 00:29:38,040
Speaker 3: it's hard to imagine you would walk away from like

585
00:29:38,320 --> 00:29:42,880
Speaker 3: the security and consistency of something like a Bloomberg terminal

586
00:29:42,920 --> 00:29:44,240
Speaker 3: At least in the near future.

587
00:29:44,480 --> 00:29:46,240
Speaker 1: I think brand metters, and I think about on the

588
00:29:46,280 --> 00:29:49,280
Speaker 1: legal side, for example, like sure, if you're a finance company,

589
00:29:49,280 --> 00:29:52,080
Speaker 1: you could use some you know, legal AI software like

590
00:29:52,920 --> 00:29:55,320
Speaker 1: or you could pay Lathan Watkins one thousand dollars an

591
00:29:55,360 --> 00:30:01,080
Speaker 1: hour perer because you know you've got Yeah.

592
00:30:01,160 --> 00:30:03,120
Speaker 3: Yeah, And I mean one thing we haven't touched on

593
00:30:03,160 --> 00:30:05,520
Speaker 3: either is the fact that, like you know, Bloomberg is

594
00:30:05,560 --> 00:30:08,560
Speaker 3: integrating AI. They have AI features of the terminal, and

595
00:30:08,640 --> 00:30:10,560
Speaker 3: so I think like that's their push to sort of say,

596
00:30:10,600 --> 00:30:12,680
Speaker 3: like this is not a point when you need to

597
00:30:12,680 --> 00:30:15,160
Speaker 3: go out and find an alternative, like we are everything

598
00:30:15,200 --> 00:30:19,080
Speaker 3: we've always been to you and more and we're worth

599
00:30:19,120 --> 00:30:22,880
Speaker 3: that price tag. So yeah, I mean again, there's arguments

600
00:30:22,920 --> 00:30:24,680
Speaker 3: on both sides, which is again why it's started up

601
00:30:24,720 --> 00:30:26,680
Speaker 3: playing so much angry debate online.

602
00:30:27,320 --> 00:30:30,240
Speaker 1: Isabel, thank you, thanks for coming straight from the Red Eye.

603
00:30:30,840 --> 00:30:32,200
Speaker 1: A good luck with the rest of your day. I

604
00:30:32,200 --> 00:30:33,800
Speaker 1: gather you're going into the office now to write some

605
00:30:33,840 --> 00:30:34,400
Speaker 1: more stories.

606
00:30:34,640 --> 00:30:36,840
Speaker 3: Yeah, yeah, I'm about to. Could I go write some

607
00:30:36,880 --> 00:30:38,360
Speaker 3: more Open Claws stories?

608
00:30:38,440 --> 00:31:02,040
Speaker 4: So thanks for having me, h for text stuff.

609
00:31:02,120 --> 00:31:04,920
Speaker 1: I'm as Volosian. This episode was produced by Eliza Dennis

610
00:31:04,920 --> 00:31:08,200
Speaker 1: and Melissa Slaughter. It was executive produced by me Julian

611
00:31:08,280 --> 00:31:11,320
Speaker 1: Nutter and Kate Osborne for Kaleidoscope and Katrina Norvell for

612
00:31:11,360 --> 00:31:15,840
Speaker 1: iHeart Podcasts. The engineer is Charles de Montebello for CDM Studios.

613
00:31:16,240 --> 00:31:19,120
Speaker 1: Jack Insley mixed this episode and Kyle murdoch Rodart theme

614
00:31:19,200 --> 00:31:22,600
Speaker 1: song and please do rate and review this show wherever

615
00:31:22,640 --> 00:31:23,680
Speaker 1: you listen to your podcast