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Over the past few days, I've been using a new AI assistant.
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I've been using Muse.
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And let me just unpack some of the things that Muse was actually able to do for me over the past few days.
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So number one, I was able to lower my phone bill by $50 a month.
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I was able to lower my internet bill by $50 a month.
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It got my home electricity bill reduced.
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And it found me a 20% discount on a Dyson vacuum cleaner.
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Now, I didn't write any prompts.
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I didn't give it very detailed instructions on what to do.
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I simply gave these high-level instructions to Muse, to this AI assistant, and I wanted to see what it would actually be capable of.
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And I believe that this is the truly first capable agentic assistant that we have seen.
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And it is absolutely mind-bogglingly powerful in terms of its capabilities, in terms of the impact that it can have.
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And I'm going to unpack a little bit more about that as we get into the rest of the podcast script.
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But I want you to imagine...
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What this would look like inside of an enterprise where everybody had this kind of a capable agentic personal assistant inside of an organization.
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If you have thousands of employees, what is it going to look like if you have thousands of these types of agentic agents?
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Now a quick note on Muse, I'm gonna mention an invite code here in case you want to use it.
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This is not sponsored, they didn't pay me to do this, they don't know that I'm doing this, but Muse right now is free, and if you use this invite code, you're gonna get a billion tokens on Muse, and I will get a billion tokens, so everybody benefits.
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Since they don't have a payment plan right now, I actually already went through my entire weekly budget
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And so when you sign in, if you download it, type in the code B-Y-M-5-L-S. Again, that's B-Y-M-5-L-S. You and I are both going to get a billion tokens.
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And the way that you do that is when you download Muse, you open up settings on the Muse app and there should be a little tab on the left side that says, yeah, you know, redeem promo code or redeem invite code.
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You put in BYM5LS and then it's going to say, congrats, you got a billion Muse tokens.
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Trust me, you're going to want to do it when you see the things that Muse is actually capable of.
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And like I said, I will go into a little bit more detail in just a minute.
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So welcome to Future Ready Today.
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It is Wednesday, September 30th, 2026th.
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I hope you all had a wonderful, wonderful weekend.
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Kind of weird saying that now we're at the middle of the week.
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My daughter had a chess tournament.
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She did fairly, you know, not terrible.
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She played in the open section.
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I think she was the only girl in that group.
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Maybe there was one other girl there.
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So she gained, I think, 20 rating points in that tournament.
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Two wins, three losses.
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Yeah.
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Some of those losses could have been wins.
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You know, we're going to work on that a little bit, but still a fun tournament that happened in Glendale.
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And yeah, so that was pretty much my weekend.
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We did a movie night with the kids, kind of hung out, relaxed.
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And I honestly spent hours and hours and hours and hours playing around with Muse.
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And that is what brings me to the first story of the day.
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So let me briefly give you some context around Muse.
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If you haven't used it, if you haven't played around with it,
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They launched early September, September 8th.
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They call it the world's first personal AI agent built for everyone.
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Built by Meta Super Intelligence Labs.
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This is the division run by Alexander Wang.
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He's the one who founded the company Scale AI.
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And Meta invested about $14.3 billion in the company for a 49% stake.
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And for a while, everybody was basically saying, what the hell's going on with this acquisition?
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You know, what's happening here?
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And here we go.
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All of a sudden, shablam, you get Muse.
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Now, let me just unpack how it works.
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And then I'll walk you through some of the things I was able to do with it.
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So you message Muse just like you would a person.
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You have the app, you have the desktop version, and you just talk to it.
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And when you first log into Muse on the desktop version, they already have a preset list of things that you might want to try out that are pretty cool.
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Negotiating a bill, take a picture of this, do this.
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It has some prompts.
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It keeps working even after you close the app and it goes back and forth with you almost the way that you would when you're texting a personal assistant.
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And when I first heard about it, I was sort of like, yeah, yeah, we have whatever.
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I have Claude, I have ChatGPT already, I have Grok, I have Gemini.
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I already have all this stuff.
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What the hell am I gonna need another AI tool for?
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That was my initial thought going into this.
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I'm reading here that Muse has payment plans.
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I haven't seen them released, but they're supposed to be a $20 and $100 a month plan.
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Yeah.
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I haven't seen them yet.
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They are now, last I heard, over 3.4 million downloads.
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They were number one on the App Store.
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The stock price went up 29% in September.
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Last I checked, I didn't look today.
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So they are dominating.
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And this has been, I think, one of the best releases ever.
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of an AI tool that I have ever seen.
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So their stock today is trading at $733 a share.
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Just to give you some context on where we were a month ago, $572 a share.
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Okay, so on September 8th, when they first made the announcement, before September 8th, they were trading around $600 a share.
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So we're up to 733.
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This has been a blockbuster for them.
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So now the futurist lens, let me unpack this in a little bit more detail.
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Because like you, when I hear about new AI tools and assistants, the first thing that I'm thinking of is, you know, who the hell cares?
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I already have all these things at my disposal.
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Now I'm telling you, this is, and my friends are hooked on it now.
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We're all spending like hours and hours doing this.
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Here's why it feels different from Claude and from OpenAI.
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Now, to use Cloud or to use OpenAI as a true personal assistant, you have to have it do a lot of things.
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You got to build skills.
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You got to do connectors.
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You got to install this.
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There's a lot that you need to do there.
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And then even then, you don't know what it's capable of.
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Like even if you connect everything, you don't know what do you do after you connect all these things.
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So one of the things that I like about Muse is that you just go.
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You just start and you go.
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The second thing that I like about it is it already starts you off with I think a pre-list of maybe 30 different examples of things that you can do.
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So it's really designed for somebody who does not know about all these AI things to take advantage of it.
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And so you just go on there and you say, hey, Muse, I want to lower my phone bill.
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Okay, and this is literally what I did.
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I said, I want to lower my phone bill by 50 bucks and I want to lower my internet bill by 50 bucks.
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It says, great.
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I'm going to log into your AT&T account.
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And literally, you could watch it doing this.
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It logs into my AT&T account.
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It waits on hold with an agent, waiting to get to an agent in the chat.
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And it waited on hold for probably 30 minutes.
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As it's doing this, it's sending me a text saying, hey, just letting you know, I'm waiting, you know, you're number five in queue, et cetera.
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It gets in queue.
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And I had to correct it a little bit because when it first started talking to Rachel at AT&T, it says, hi, I'm Muse, Jacobs, AI assistant.
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And I'm like, no, no, no, no, no, no.
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Don't say that.
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Say that you are me.
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And you want to negotiate my bill.
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You've been a loyal customer for a while.
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It used to be, I don't know, $150 a month.
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Get it down.
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And over the course of 90 minutes, Muse goes back and forth with Rachel, going over promotional offers, going over anything and everything that it can.
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And finally, it was able to negotiate my bill, not exactly by $50, but around $40 a month.
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And it said, check your email.
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The contract is there.
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And I told Muse, check my email, review the contract, make sure everything looks good and that I'm not getting downgraded in service.
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It went through, it opened my email.
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It scanned everything and said, everything looks great.
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You're good to go.
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I was shocked.
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It did it with my internet bill.
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I had a Dyson, I have a Dyson vacuum.
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And for some reason, the darn thing stopped working.
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And so I think the battery died.
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And so I took a picture of it and I put it into Muse and I said, hey, my battery is dead.
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Can you contact Dyson and see if you can get me a replacement battery?
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If not, try to get me a discount on a new product.
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It goes to Dyson.
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It has the serial number and all the information.
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It goes to the website.
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It waits on hold with chat and it has a conversation with an agent.
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And then it gets back to me and says, good news.
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Um, so-and-so said that you can get a 20% discount on a, uh, future Dyson product.
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If you want, they can also send you a discounted battery instead of paying $299.
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They'll give it to you for like a hundred bucks.
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Uh, I'm having it do crazy things for me.
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on LinkedIn as far as who to connect with and cleaning up my connections, going over through all of my monthly subscriptions that I have and trying to audit them and see where I can find better rates and pricing on them.
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It's staggeringly good at what it does.
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And you can watch it do these things if you're on the desktop app.
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And again, there's not a lot of like, if it goes to a website like AT&T.com, it'll ask you in a secure form, what's your username and password?
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Once you do that, you are done.
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You don't build a skill.
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You don't have to do a connector.
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You don't have to watch a tutorial.
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You taught, the way that you would have a personal assistant if they like came to your house and said, hey, Jacob, what can I do for you today?
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And you give them a list of things.
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That is what this thing does.
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I've never seen anything, anything, anything like this.
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It is that good and that useful and that valuable.
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Now, I still use Claude for deep dives around research and analysis and things like that.
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I don't view Claude as a personal assistant.
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I look at Claude and OpenAI as more of, I don't know, research assistants or work assistants or analyzers in that kind of capacity.
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But Muse is actually, I view it as a personal assistant.
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So if you haven't tried it, I cannot stress enough how much you should try it.
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And again, the code is BYM5LS.
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You and I will both get a billion free tokens, which is enough for a long, long time.
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So thinking of Muse, I'm trying to imagine this in the workplace because we don't have this in the workplace yet.
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Now we talk about agents in the workplace, but when we talk about agents in the workplace, we usually talk about it in the form of agents completing a very specific task, like filling out a form or maybe responding to somebody, putting a calendar invite, things like that.
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That is the tip of the iceberg.
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I mean, imagine literally everybody at your company.
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Let's say you have 5,000 employees.
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Imagine everybody at your company now has a personal agentic assistant that can do anything.
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Anything.
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Negotiate salary for you.
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Try to get better benefits for you.
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Try to resolve a conflict for you.
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Yes, respond to your emails for you.
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But it acts like a person.
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You want it to order things for you, to buy things for you, anything.
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You can throw anything at it.
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And so I think that we're going to see three shifts coming very soon in the workplace.
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And in our recent CHRO group meeting that we had a couple of days ago, we were talking about this.
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And by the way, the URL, of course, futureofworkleaders.com, our next event, January 13th and 14th in the Bay Area in person.
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I think we're going to have around 15 or 20 chief human resource and chief people officers there.
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So shift number one.
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when this enters the enterprise, because it's not there yet.
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Not, not like this.
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We have agentic capabilities in the enterprise, not like this.
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And when you use Muse and test it out, you'll know exactly what I'm talking about.
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Shift number one, everyone's going to become a manager.
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Everybody inside of your organization is become, is going to become a manager.
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And the reason why I say that is because even with Muse, I still had to manage it.
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Like it would give me updates on what it was doing and I had to tell it what I wanted to do.
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Hey, Jacob, I wasn't able to get $50 off.
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I could get $40.
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Do you want to take this or not?
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Yeah, I'll take it.
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But can you verify that I'm not actually losing any service and I'm not downgrading anything?
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Sure.
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Let me get back to you on that.
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So you still have to manage it the way that you would a person.
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But it gives you updates as it's working through.
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So everyone is going to become...
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a manager in some capacity.
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And again, nothing like this exists in the enterprise yet, but it's coming.
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So if your day, like you start off your morning, your goal, you're going to set off, start your day by setting goals for your agent.
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Hey, here are five things that I need you to accomplish for me today.
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Here's what I need to help with.
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I need you to do X, Y, and Z.
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So you're going to review the work.
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You're going to approve the decisions that your AI agent is going to make.
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These are managerial qualities and capabilities.
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Muse is literally built around audits, around approvals, around audit trails.
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And so what happens when a 26-year-old analyst inside of your organization is going to be running maybe several agents as a part of a small team?
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And what's going to happen is these employees are now going to ask you all of a sudden, hey, I'm literally managing five agents the same way that I would a person.
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There's no emotional baggage or emotional stress or discussion there, but I'm managing them the same way that I would a person.
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I have to delegate.
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I have to set goals.
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I have to check in.
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I have to review their work.
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I have to make approvals.
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So if I'm doing this and I'm basically managing a small team, why is my title inside of your organization, Jacob Morgan, Analyst One?
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Why am I not a manager?
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Why am I not a financial analyst manager inside the organization?
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Why am I just analyst?
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Why am I paid like an individual contributor when I'm not an individual contributor?
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I'm clearly a manager inside this organization doing the things that a manager does.
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So you can expect job titles like agent lead, AI operations manager, things of that nature.
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They are going to spread.
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They're going to spread the way that the word digital got added to every job title in 2010.
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Digital marketing analyst,
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digital marketer, digital transformation lead, digital blah, blah, blah, blah, blah.
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You're going to see this with agents.
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You're going to see this.
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I think we're already starting to see this in job descriptions.
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So that's going to be shift number one.
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Everyone inside your organization, whether you like it or not, is going to become a manager in some capacity.
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Number two, there is going to be a fight over what managing an agent is worth.
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And the reason why this is going to happen is because for decades, most companies have set manager pay based on scope.
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Usually, what that means is that the number of people that report to you.
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So the more people who report to you, the more people you're managing.
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And so you get paid more.
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And managing people usually means you do what?
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You have coaching sessions with them.
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You go over conflict.
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You go over career trajectory.
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You go over team building.
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You go over alignment to values.
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Managing an agent skips all of that sort of stuff.
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You're purely just focused on the work.
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And so employees will say that managing agents is like managing a team, but employers, employers, I think, are going to push back and they're going to say, wait a minute.
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Managing agents is lighter work.
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It doesn't deserve managerial pay or managerial title because you don't have to do the coaching.
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You don't have to talk about careers.
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You don't have to talk about alignment.
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You don't have to talk about values.
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You don't have to do a lot of the human stuff that is so important for managerial work.
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So there's going to be a little bit of a fight between what it means and what the value is over managing an agent.
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Employees are going to, again, they're going to push back and they're going to say, hey, I'm managing agents
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You know what used to be a team of five.
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This would take five people to do this work that I'm doing.
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So pay me for the output of what it would take to manage a team of five people.
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And I think both sides here are going to have an interesting argument and an interesting debate.
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And a lot of companies are going to have this argument in performance reviews before I think they have a policy in place.
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You can expect before policies exist, you're going to have some employees during your review process who are going to say, Hey, Jacob, you know, over the past couple of months, I've been managing a team of like three to five agents.
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I'm...
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really curious here.
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What are we going to do in terms of compensation, in terms of benefits, in terms of job title, if I'm managing agents, what are we doing here?
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That's going to come up if it hasn't already inside your organization.
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And you're going to need to know, and you're going to need to prepare for how do you deal with this?
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How do you have this conversation?
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Are you really just going to tell your employees, Hey, wait a minute.
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Uh, you know, it's just a tool.
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It's just a technology.
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Your employees are going to push back and they're going to say,
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then don't ask me to use it.
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Or they're going to say, it's not just a tool and a technology because I have to do the things that a manager does.
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In shift number three, I think pay is going to be re-evaluated.
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It's going to move from headcount to output.
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Once agents are involved and we're starting to see that already, how many people report to you is going to say very little about how much you control.
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Companies are going to have to find new ways to measure contribution, to measure value, to measure impact, to measure output.
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So this is going to be another big area of debate and conversation.
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Because there's going to be a gap between an employee who uses an agent well and another employee who uses an agent poorly.
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So somebody who uses an agent well or poorly, they're going to just say, here's access to my inbox, respond to all the emails for me.
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But that agent doesn't have the real world context around who that agent is talking to.
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Did you get lunch with this person?
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Did you see this person at a meeting recently?
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Was there a conflict that you recently resolved?
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Did you go to college with this person?
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Have you been working together for a while?
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An agent doesn't have that information.
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And so the way an agent is going to just manage your emails, it's going to be in a very templated, basic, hey, thanks for responding.
291
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Jacob is available at this time.
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Are you available?
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Is that how you're going to talk to your good friend?
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Or your boss or a team member at your organization?
295
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Yeah.
296
00:20:45.652 --> 00:20:51.615
So managing an agent well is going to mean that you're going to need to give them the context, the information so that they can do their jobs effectively.
297
00:20:53.795 --> 00:20:54.596
And now the chaos.
298
00:20:55.916 --> 00:21:02.499
The chaos is going to happen when my agent is going to talk to your agent.
299
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And I think scheduling is going to be the first thing that's going to break.
300
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Because I'm going to have a rule that says, hey, I don't want to schedule any meetings before 10 a.m. Pacific Standard Time.
301
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You're going to tell your agent, schedule a meeting with Jacob at 8 a.m.
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Your agent's going to tell my agent, hey, John wants to schedule this meeting at 8 a.m. My agent's going to say, I'm sorry, Jacob doesn't do meetings before 10 a.m. And your agent's going to say, this is really, really important.
303
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You need to be there.
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00:21:32.422 --> 00:21:40.987
And our two agents are going to go back and forth ad infinitum arguing over how this meeting is going to get scheduled.
305
00:21:42.551 --> 00:21:43.592
And scheduling is going to break.
306
00:21:44.213 --> 00:21:46.976
Because we're going to set rules for our agents.
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And we're going to tell our agents, don't break these rules.
308
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And so, bad things and chaos, I think, is going to happen.
309
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Right?
310
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Today, finding a time for a meeting involves some...
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very friendly back and forth.
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It's a human thing.
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Does this work for you?
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No.
315
00:22:08.555 --> 00:22:09.456
Does this work for you?
316
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You know, try to schedule a lunch, a dinner, a meeting, whatever it is.
317
00:22:11.817 --> 00:22:13.638
There's a lot of friendly kind of casual back and forth.
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But when every person's agent is going to start to negotiate on your behalf, the agents, you know, they're going to schedule with agents all day.
319
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And that sounds efficient until you realize that nobody actually decided if this meeting is even worth having.
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Meetings could multiply because setting them up costs nothing.
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So, and this is just for meetings, imagine for emails.
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Meetings, emails, presentations, ordering things, canceling things, submitting things, reviewing things.
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At a certain point, it is going to break inside of an organization if you have thousands of these agents.
324
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that are communicating with each other as personal assistants.
325
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And this is going to pose a very big challenge for organizations.
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Do we even want this capability to exist?
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Are we going to allow employees inside of organizations to have these types of personal assistants?
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Or are we going to handcuff agents to very specific and narrow tasks, such as reviewing documents, or just using Claude to help with presentations and analysis and reviewing things and preparing things?
329
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And then there's the role of accountability.
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When your agent and my agent are going back and forth, who's going to be accountable for what?
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00:23:44.863 --> 00:23:51.168
If my agent commits to a certain deadline in a conversation with your agent, does that mean that I committed?
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Who's responsible?
333
00:23:56.612 --> 00:23:59.014
So there's going to be a lot of information leaks.
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Your personal assistant, your agent, is going to remember everything that you tell it.
335
00:24:07.728 --> 00:24:18.512
And if you're using it for work or for personal or for whatever it might be, this information ends up living inside of your organization.
336
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Can it be leaked?
337
00:24:20.652 --> 00:24:21.593
Can it, you know, who knows?
338
00:24:24.413 --> 00:24:26.534
I mean, even today, Muse randomly sent me this note.
339
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It's like so-and-so has a, you know, their promotional offer expires at midnight.
340
00:24:31.516 --> 00:24:32.596
And I'm like, why are you telling me this?
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00:24:33.836 --> 00:24:35.257
I didn't ask you for this information.
342
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And it said, well, we've been going back and forth on money-saving tips and your phone bill and negotiating this, and I thought maybe this would be helpful.
343
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And I thought, please don't.
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I told her, I said, please don't send me these kind of spam promotional offers anymore.
345
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So there's going to be a lot of wild stuff happening inside of organizations.
346
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I don't think it's a reason to panic, but I think it's going to be a reason to have very...
347
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Serious conversations with your legal teams, with your IT teams, with your chief human resource officer teams.
348
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Madness is coming.
349
00:25:15.450 --> 00:25:16.830
And I think eventually we'll figure it out.
350
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But there is going to be a time of chaos.
351
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And that chaos begins now.
352
00:25:23.972 --> 00:25:26.493
Second story of the day, Anthropics leaked numbers.
353
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This was pretty wild.
354
00:25:28.414 --> 00:25:29.114
This was pretty wild.
355
00:25:30.316 --> 00:25:36.998
A draft of Anthropics IPO prospectus obtained by Reuters gives the first detailed look at its finances.
356
00:25:37.198 --> 00:25:42.200
The company posted a net loss of about $42 billion in 2025.
357
00:25:42.240 --> 00:25:51.062
Roughly $34 billion of that was an accounting charge tied to financing that could turn into shares, not actually cash spent on running the business.
358
00:25:52.163 --> 00:25:58.705
Operating loss was $8 billion, up from $2.98 billion the year before that.
359
00:25:59.603 --> 00:26:03.767
on revenue of about $4.6 billion, which is 12 times the prior year.
360
00:26:05.669 --> 00:26:16.900
Anthropic ended 2025 with $20.28 billion in cash and plans to spend $518 billion on computing and infrastructure in the coming years.
361
00:26:18.441 --> 00:26:22.225
About 80% of that must be paid whether it uses the capacity or not.
362
00:26:24.960 --> 00:26:33.722
About 80 of the prospectus' 261 pages are devoted to risk factors compared to 48 pages on the business itself.
363
00:26:35.283 --> 00:26:42.285
For comparison, SpaceX's recent prospectus devoted about 38 out of its 277 pages to risks.
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Among them, Anthropic warns advanced AI could pose catastrophic or existential risks to humanity in that models could try to resist shutdown or manipulate information.
365
00:26:54.100 --> 00:27:02.769
On the growth side, second quarter, 2026 revenue topped $11.5 billion, more than double all of 2025.
366
00:27:03.630 --> 00:27:12.039
According to the Financial Times, investors believe Anthropics' annualized revenue could reach $100 billion to $120 billion by the end of 2026.
367
00:27:12.679 --> 00:27:16.844
And they are targeting a valuation of above $2 trillion.
368
00:27:19.129 --> 00:27:24.313
More than double its 965 billion valuation in May.
369
00:27:26.434 --> 00:27:36.722
Meanwhile, OpenAI seeking at least 30 billion at a valuation of about 1.4 trillion with a reported revenue run rate of 68 billion.
370
00:27:37.202 --> 00:27:38.283
Okay, now the futurist lens here.
371
00:27:38.303 --> 00:27:39.624
Let's unpack some of these numbers a little bit.
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Now, if you strip out this $42 billion accounting charge, the real loss here is about $8 billion.
373
00:27:48.700 --> 00:27:55.264
So what this means is that they lose around $1.75 for every dollar of revenue.
374
00:27:57.185 --> 00:28:03.088
Now, it's bad, but you can make the argument that it's somewhat typical for an organization that's growing that fast.
375
00:28:03.568 --> 00:28:06.450
But the numbers that should worry investors, I think, are three other numbers.
376
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First, there's the $518 billion in commitments, most of which cannot be canceled, against $20 billion in the bank.
377
00:28:18.739 --> 00:28:27.266
And this to me is like signing a 10-year lease on a ginormous skyscraper when you have enough cash on hand for the first few months of rent.
378
00:28:28.326 --> 00:28:31.769
And you're betting that your revenue is going to grow fast enough to cover the rest of it.
379
00:28:33.451 --> 00:28:35.152
Second, customer concentration.
380
00:28:35.292 --> 00:28:36.093
Two clients...
381
00:28:37.555 --> 00:28:39.576
are about 12% each of the business.
382
00:28:42.237 --> 00:28:49.359
If one of them builds its own or switches providers, you get a huge hole opening up in Anthropic overnight.
383
00:28:52.500 --> 00:28:56.902
Most big customers can walk away because they are not on long-term contracts here.
384
00:28:58.302 --> 00:29:00.803
Now, the interesting thing here is this whole Doomer Paradox narrative.
385
00:29:01.942 --> 00:29:12.484
Anthropic is asking investors for $2 trillion while simultaneously telling them under legal liability that its product could pose an existential risk to all of humanity.
386
00:29:14.365 --> 00:29:17.405
And so Wall Street, they're calling this a head scratcher.
387
00:29:17.425 --> 00:29:20.366
They're basically saying, how can both these things be true?
388
00:29:20.426 --> 00:29:21.146
I don't understand here.
389
00:29:21.806 --> 00:29:27.107
You're telling me your company has a chance of ending all of human life on planet Earth.
390
00:29:27.687 --> 00:29:30.248
Meanwhile, you're asking investors for $2 trillion.
391
00:29:31.578 --> 00:29:33.660
Or saying that your company's valued at $2 trillion.
392
00:29:35.623 --> 00:29:37.265
And I think there are a couple of ways that you can read this.
393
00:29:39.147 --> 00:29:46.195
Now, first is that companies usually on these prospectuses, they're supposed to list every conceivable risk so that they can't be sued later for hiding it.
394
00:29:47.417 --> 00:29:48.337
The second is marketing.
395
00:29:49.297 --> 00:29:52.918
So whether intended or not, our technology is so powerful it could be dangerous.
396
00:29:52.958 --> 00:29:54.319
I went over this in previous episodes.
397
00:29:55.019 --> 00:29:57.139
This is the strongest capability claim that you can make.
398
00:29:57.659 --> 00:30:04.521
In fact, OpenAI recently, they shelved their recent Astra 6.1 model because they said it was too dangerous.
399
00:30:06.421 --> 00:30:11.863
We all know that Anthropics, Dario Amadei and others, they called for a slowdown.
400
00:30:11.903 --> 00:30:12.823
They wanna be regulated.
401
00:30:15.875 --> 00:30:19.016
And so you need to remember that investors, they pay for growth.
402
00:30:19.136 --> 00:30:22.097
They want to give you money because they are investing in growth.
403
00:30:23.597 --> 00:30:27.899
And so the first question is, will investors want a discount?
404
00:30:30.779 --> 00:30:36.121
I think almost certainly on the IPO price because IPOs are usually priced below what the market expects.
405
00:30:36.221 --> 00:30:39.142
And so the stock ends up usually rising on day one.
406
00:30:40.489 --> 00:30:43.931
That's how bankers, you know, they keep their big investors happy.
407
00:30:43.951 --> 00:30:45.232
You see these big pops that happen.
408
00:30:45.252 --> 00:30:46.333
That's what happened with SpaceX.
409
00:30:49.274 --> 00:30:54.237
Across decades of U.S. listings, the average first day gain has been in the high teens.
410
00:30:57.739 --> 00:30:59.120
And so investors might say, wait a minute.
411
00:30:59.941 --> 00:31:06.985
If you're telling us that you want to slow down and get regulated, then I want a discount on that $2 trillion valuation you're telling me about.
412
00:31:11.667 --> 00:31:20.693
So if Anthropic really does hit, and I think they're supposed to IPO in November, OpenAI sometime, maybe Q1, 2027.
413
00:31:21.193 --> 00:31:28.998
If Anthropic really hits $100 billion or more in annual revenue by year end, then a $2 trillion valuation is about 20 times revenue.
414
00:31:30.199 --> 00:31:35.543
And that's roughly where OpenAI is priced today at 1.4 trillion on around 68 billion.
415
00:31:36.704 --> 00:31:40.266
Now in that kind of a world, the price is not crazy.
416
00:31:40.286 --> 00:31:40.426
Okay.
417
00:31:43.594 --> 00:32:02.669
But the bear case on this is that if you value Anthropic on its most recent reported quarter, which is about $46 billion a year, not $68 billion, but $46 billion on a $2 trillion valuation, that's more than 40 times revenue.
418
00:32:04.210 --> 00:32:05.932
And that's pretty high.
419
00:32:08.314 --> 00:32:11.356
And this valuation really only holds if everything goes right for Anthropic.
420
00:32:13.406 --> 00:32:17.009
So I think there's going to be a little bit of a disagreement here between the private markets and the public markets.
421
00:32:17.689 --> 00:32:20.872
Private investors have been pricing these companies on future potential.
422
00:32:21.712 --> 00:32:23.234
SpaceX, future potential.
423
00:32:23.714 --> 00:32:25.115
Anthropic, future potential.
424
00:32:25.255 --> 00:32:26.576
OpenAI, future potential.
425
00:32:27.017 --> 00:32:35.123
Public investors, they're going to price them every single day on the numbers.
426
00:32:36.164 --> 00:32:37.665
Quarterly hits or misses.
427
00:32:41.138 --> 00:32:43.981
And, you know, obviously SpaceX was a mega IPO.
428
00:32:44.001 --> 00:32:53.350
I think you can make the argument that, you know, Anthropic and OpenAI, these are kind of the first duopoly IPOs that we're going to see.
429
00:32:57.795 --> 00:33:02.700
So lots of crazy stuff happening in the AI world here.
430
00:33:04.250 --> 00:33:10.154
The last story of the day that I wanted to get to, also from Anthropic, they published something right before I was going to go live here.
431
00:33:10.174 --> 00:33:11.695
And so I added it quickly to the script.
432
00:33:13.816 --> 00:33:24.783
They published something on the Anthropic blog, huge post, what work can robots do?
433
00:33:26.024 --> 00:33:28.225
And they're not talking about just LLMs.
434
00:33:28.245 --> 00:33:29.786
They're talking about physical robots.
435
00:33:30.795 --> 00:33:37.800
So today, anthropic economists Russell Legate Yang and Maxim Masinkoff, they used CLOD.
436
00:33:38.061 --> 00:33:40.222
So they published something called a robot exposure index.
437
00:33:40.242 --> 00:33:46.127
They used CLOD to assess about 19,000 tasks around across 900 occupations.
438
00:33:47.308 --> 00:33:55.654
For each task, the model researched real robots, judged what they have been shown to do and in what setting, and estimated task and time costs.
439
00:33:57.997 --> 00:34:08.739
The headline, counting any level of capability, 74% of physical work or 34% of all work can be done by robots in some circumstances.
440
00:34:09.739 --> 00:34:12.300
But setting matters enormously.
441
00:34:12.360 --> 00:34:26.203
23% of work time involves tasks that need purpose-built environments like production lines, 10% can be done in orderly human workplaces like warehouses, and just 1% can be done in unpredictable settings like public roads.
442
00:34:27.410 --> 00:34:32.973
Taxi drivers are the most exposed here, followed by agricultural equipment operators and light truck drivers.
443
00:34:33.974 --> 00:34:40.978
For recycling and reclamation workers, robots can perform more than three quarters of tasks in structured workplaces.
444
00:34:42.138 --> 00:34:48.602
Nursing and general repair jobs score low because today's robots can do little of that work, even in controlled environments.
445
00:34:52.095 --> 00:35:04.423
The workers most exposed to robots are more likely to be men, less likely to have a college degree, and lower paid, which is a very different group from the office workers most exposed to AI language models.
446
00:35:06.885 --> 00:35:18.072
If you combine robots with language models, then technology can technically reach tasks making up 81% of work time, according to this.
447
00:35:19.700 --> 00:35:20.781
They also talk about costs.
448
00:35:20.801 --> 00:35:22.541
I'm not going to break down all the costs here.
449
00:35:22.581 --> 00:35:24.602
It was a long, thousands and thousands of words.
450
00:35:26.563 --> 00:35:27.644
So now the futurist lens here.
451
00:35:30.325 --> 00:35:39.330
Do you remember in 2013, two Oxford researchers estimated that 47% of U.S. jobs were at high risk of automation within a decade or two?
452
00:35:39.350 --> 00:35:45.453
47% of jobs were at high risk of automation.
453
00:35:47.691 --> 00:35:50.672
high risk of automation between one to two decades.
454
00:35:52.072 --> 00:35:53.633
They did the exact same thing.
455
00:35:53.673 --> 00:35:57.614
I mean, they didn't have Claude obviously at the time, but they did the same, took the same approach.
456
00:35:59.234 --> 00:36:06.336
More than a decade later, U.S. employment is higher than it was then.
457
00:36:08.296 --> 00:36:12.798
In the mid 2010s, several car makers promised fully self-driving cars by around 2020.
458
00:36:14.263 --> 00:36:15.584
Today, we're barely seeing them.
459
00:36:16.824 --> 00:36:24.408
I mean, we're seeing, well, I guess it depends on how truly self-driving you see the robo-taxis, which released, came out in Austin recently.
460
00:36:25.749 --> 00:36:28.470
So that, you know, 2026 just came out almost a decade later.
461
00:36:30.431 --> 00:36:36.975
So forecasts about what machines and what technology and what AI can do tend to be right about the direction, but very, very wrong about the timing.
462
00:36:37.915 --> 00:36:39.476
Often very badly, by the way.
463
00:36:40.638 --> 00:36:42.700
I think the second thing here is capabilities, not adoption.
464
00:36:42.880 --> 00:36:46.562
So again, number one, we've been with these predictions before, and so far, their predictions have been wrong.
465
00:36:47.643 --> 00:36:49.965
We saw, what's his name?
466
00:36:51.426 --> 00:36:55.929
Jeffrey Hinton, if I'm not butchering his name, known as the father of AI.
467
00:36:56.770 --> 00:37:04.375
He said, what was this, 15 years or so ago, he's on record saying, well, if you're a radiologist, he gave the analogy.
468
00:37:04.415 --> 00:37:06.497
He said, like, you remember Roadrunner and Coyote?
469
00:37:07.652 --> 00:37:09.753
the roadrunner, coyote trying to chase the roadrunner.
470
00:37:09.813 --> 00:37:16.938
He said that if you're a radiologist, you're sort of the coyote that already ran off the cliff and you just haven't looked down yet to see that you're off the cliff.
471
00:37:18.839 --> 00:37:23.382
And he decried that if you're in this space, it's terrible and job loss and blah, blah, blah, blah, blah.
472
00:37:23.683 --> 00:37:24.703
Look at radiologists today.
473
00:37:26.664 --> 00:37:33.529
One of the fastest growing occupations, not just in terms of demand, but also in terms of salary growth.
474
00:37:36.295 --> 00:37:39.237
Jensen Huang pointed this out recently in several interviews that he's done.
475
00:37:39.257 --> 00:37:41.699
We've been wrong consistently about the predictions.
476
00:37:42.800 --> 00:37:44.901
There was also a Wall Street Journal article that came out today.
477
00:37:44.942 --> 00:37:51.967
I didn't cover it, but it said that the, you know, all the stories about college, college graduates having higher unemployment.
478
00:37:52.647 --> 00:37:58.312
They said that it's, you know, they're debunking some of that saying, well, it's really due to seasonal effects.
479
00:37:59.312 --> 00:38:02.395
A lot of it happening over summertime, June, July, August.
480
00:38:03.202 --> 00:38:06.204
They're saying that correlation does not imply causation.
481
00:38:06.644 --> 00:38:08.926
So they're debunking already some of these things.
482
00:38:09.866 --> 00:38:11.487
We've been wrong about the job loss numbers.
483
00:38:11.587 --> 00:38:13.128
We've been wrong about the radiologist thing.
484
00:38:13.168 --> 00:38:16.450
We've been wrong about the self-driving cars.
485
00:38:17.091 --> 00:38:25.676
We've been wrong about robots taking... We've been wrong collectively in every way that you can think.
486
00:38:29.198 --> 00:38:31.760
So we have been here before with all of these types of predictions.
487
00:38:33.390 --> 00:38:34.791
Second, capabilities not adoption.
488
00:38:34.951 --> 00:38:38.713
Anthropics own researchers showed this earlier this year.
489
00:38:38.893 --> 00:38:50.020
AI could theoretically, theoretically, I love that word, theoretically could handle 94% of tasks in computer and math jobs, but was actually being used in just 33% of them.
490
00:38:52.001 --> 00:38:58.305
So something a machine can do in a demo is very different than when a business actually deploys it.
491
00:38:59.334 --> 00:39:06.757
These things are separated by cost, by trust, by safety, rules, insurance, unions, governance, policy, retraining, habit.
492
00:39:09.498 --> 00:39:12.500
It's not, you know, it's not the same.
493
00:39:14.120 --> 00:39:18.742
Third, there's a concept known as Moravec's paradox.
494
00:39:18.822 --> 00:39:24.745
In the 1980s, roboticist Hans Moravec pointed out the things that we find very hard, like chess, chess,
495
00:39:26.256 --> 00:39:40.885
Math, these are relatively easy for machines because they rely on brute compute, calculating different possibilities and options, brute force computing, just going through a lot of different possibilities and scenarios.
496
00:39:41.886 --> 00:39:51.552
These things are relatively easy for machines because you give them more compute, more power, more speed, and they can figure out things faster than a human can and more accurately.
497
00:39:54.174 --> 00:39:55.755
But the things that a toddler can do
498
00:39:57.880 --> 00:40:01.845
usually without thinking, like picking up a toy, walking across a messy room.
499
00:40:02.426 --> 00:40:06.932
These are incredibly hard for AI tools and robots.
500
00:40:09.215 --> 00:40:13.981
Think about like untangling a tangled up extension cord.
501
00:40:16.186 --> 00:40:23.312
If you see the videos on X all over social media of these robots that are going into people's homes and cleaning, I think figure three did some of these.
502
00:40:23.932 --> 00:40:25.574
There was another company that escapes me.
503
00:40:27.135 --> 00:40:27.876
I can't remember their name.
504
00:40:29.337 --> 00:40:31.358
They're charging $30 an hour per robot.
505
00:40:31.378 --> 00:40:32.960
These robots go into homes to clean.
506
00:40:35.744 --> 00:40:39.607
And if you've seen these videos, these robots move at a snail's pace.
507
00:40:39.627 --> 00:40:43.090
And there's usually a human operator on the other end trying to help and guide the robot.
508
00:40:43.791 --> 00:40:46.894
Now, I'm not saying eventually they're not going to get to a point where they're more useful.
509
00:40:49.176 --> 00:40:53.580
But the way that they fold, it's literally like they fold towels like this.
510
00:40:54.120 --> 00:40:57.143
They move very slowly.
511
00:40:57.263 --> 00:41:00.806
They are, they're not yet useful.
512
00:41:04.218 --> 00:41:08.161
These mundane things are the hardest things for robots to do.
513
00:41:13.905 --> 00:41:14.965
This study again proves it.
514
00:41:15.045 --> 00:41:22.430
Reasoning holds robots back on only 8% of tasks, while hands and dexterity holds them back on half of tasks.
515
00:41:23.931 --> 00:41:29.435
So AI can maybe write your legal memo for you, but a robot still struggles to untangle an extension cord.
516
00:41:31.216 --> 00:41:33.658
And that's the interesting and the fascinating paradox behind these things.
517
00:41:37.212 --> 00:41:48.494
And fourth, and this is the biggest issue that I keep having with these studies, breaking a job into a series of tasks is the wrong way to measure what a robot can do.
518
00:41:49.474 --> 00:41:52.015
Because the work actually happens in between the tasks.
519
00:41:52.435 --> 00:41:57.435
The study, and all studies like it, especially from academics, they treat jobs like a checklist.
520
00:42:00.016 --> 00:42:06.717
If a robot can do 97% of the checklist, therefore, 97% of your job is exposed.
521
00:42:08.753 --> 00:42:13.114
But as I consistently talk about, a job isn't a list of tasks.
522
00:42:13.234 --> 00:42:15.895
A job, it's a glue between the tasks.
523
00:42:17.496 --> 00:42:20.057
Think about a packer, for example, in a warehouse.
524
00:42:21.097 --> 00:42:23.458
The robot can do 97% of the tasks.
525
00:42:23.498 --> 00:42:24.298
But what about the other 3%?
526
00:42:24.598 --> 00:42:31.541
The jammed box, the damaged product, the order that doesn't match the label.
527
00:42:33.561 --> 00:42:34.602
Somebody has to be there for that.
528
00:42:34.622 --> 00:42:35.702
A robot doesn't know what to do.
529
00:42:38.000 --> 00:42:42.601
So you don't really replace the person, you pay for the robot and you keep a person nearby anyway.
530
00:42:42.621 --> 00:42:48.843
And so that last 3% that the robot can't do costs as much as the first 97%.
531
00:42:51.764 --> 00:43:00.086
So if you have robots that are doing these things, if a robot can't get stuck on a certain thing, it slows down everything.
532
00:43:02.517 --> 00:43:11.880
And so that 3%, we talk about it as if it's not a big deal, but it's a huge deal because that 3% jams up and costs the other 97%.
533
00:43:12.460 --> 00:43:13.701
You still got to have a person there.
534
00:43:14.541 --> 00:43:15.701
Or the robot has to wait.
535
00:43:16.822 --> 00:43:17.782
Everything shuts down.
536
00:43:17.842 --> 00:43:18.983
The person has to come in.
537
00:43:23.024 --> 00:43:31.867
The valuable part of most jobs is the judgment, the discernment, the switching, the exceptions, the context.
538
00:43:32.935 --> 00:43:35.277
A nurse, for example, doesn't just perform tasks.
539
00:43:37.019 --> 00:43:39.181
They notice when a patient might look a little off.
540
00:43:40.382 --> 00:43:43.284
Reassure a scared family member when they come in to bring in a patient.
541
00:43:44.986 --> 00:43:48.249
They decide what matters most in the next five minutes.
542
00:43:48.369 --> 00:43:50.491
You can't see that in a task list.
543
00:43:51.652 --> 00:43:52.272
You just don't.
544
00:43:56.956 --> 00:43:57.397
You can't.
545
00:43:59.197 --> 00:44:04.000
I mean, you look at any aspect of a job and how much of your job requires context.
546
00:44:05.060 --> 00:44:09.403
Knowing the people, the situation, what to do if something goes wrong,
547
00:44:10.968 --> 00:44:11.988
how to talk to somebody.
548
00:44:12.008 --> 00:44:13.569
Like there's a lot of context.
549
00:44:13.589 --> 00:44:25.752
So I always say, and I can't stand when these organizations do this, you cannot break down a job into a series of tasks, say how many of those tasks a robot can do, and then say, well, hey, 97% of that job is exposed.
550
00:44:25.932 --> 00:44:29.273
Therefore, 97% of jobs in that category are exposed.
551
00:44:29.293 --> 00:44:30.153
It's not how it works.
552
00:44:30.694 --> 00:44:31.674
It's not reality.
553
00:44:31.694 --> 00:44:32.414
It's not real.
554
00:44:34.335 --> 00:44:35.235
It doesn't make sense.
555
00:44:36.710 --> 00:44:41.112
And then it doesn't account for what actually has to be required for that to take shape.
556
00:44:42.272 --> 00:44:45.674
The rules, the policy, the guidelines, the trust, the data, the security.
557
00:44:45.694 --> 00:44:49.636
It's just an endless stream of things that have to happen.
558
00:44:53.477 --> 00:45:01.701
Finally, if you notice where the robots work, the robots only work well in environments that are specifically built for them.
559
00:45:03.348 --> 00:45:09.091
So most of the 74% that I talked about earlier, it requires a production line or an orderly warehouse.
560
00:45:10.352 --> 00:45:15.175
In other words, you build the surroundings for a robot and then the robot can do a good job.
561
00:45:15.975 --> 00:45:27.762
If I had a robot walking into this studio here where I have some lights, I have some moving blankets to dampen sounds, I have some random stuff here and there, a robot's not going to know what to do in here.
562
00:45:29.535 --> 00:45:30.196
What to clean up.
563
00:45:30.236 --> 00:45:36.120
It's not going to know that there's a little mixer over here, that I have like a little iPhone holder here, a portable mic here.
564
00:45:36.140 --> 00:45:37.801
It's not going to know what these cables are.
565
00:45:37.821 --> 00:45:39.602
I have like a ceiling fan remote.
566
00:45:39.622 --> 00:45:40.663
Where's it going to put these things?
567
00:45:41.043 --> 00:45:43.385
There's an SD card floating around, a water bottle.
568
00:45:43.585 --> 00:45:45.306
It's not going to have, it's not going to know what to do.
569
00:45:46.327 --> 00:45:47.688
It's not going to be able to clean up this room.
570
00:45:49.309 --> 00:45:51.051
Maybe I can say vacuum the floor.
571
00:45:54.313 --> 00:45:55.474
But it doesn't have the context.
572
00:45:57.024 --> 00:46:00.466
unless you build the settings and the environment for the robot.
573
00:46:03.368 --> 00:46:09.071
Only 1% of work time happens in an unpredictable real world where robots can manage.
574
00:46:10.032 --> 00:46:13.014
And so the real barrier, I think, is not necessarily just building a better robot.
575
00:46:13.054 --> 00:46:15.175
It's how do you rebuild the world around robots?
576
00:46:16.716 --> 00:46:19.717
And that's slow and that's expensive and it's time consuming.
577
00:46:21.679 --> 00:46:25.581
And it doesn't make sense in every situation and scenario.
578
00:46:28.820 --> 00:46:31.842
So the takeaway for me isn't that robots are coming for everyone's job tomorrow.
579
00:46:33.263 --> 00:46:34.945
And it isn't that robots are never going to matter.
580
00:46:37.486 --> 00:46:40.288
It's that change is going to happen, I think, job by job.
581
00:46:41.549 --> 00:46:42.490
I think it's going to be slow.
582
00:46:42.610 --> 00:46:44.211
In a lot of areas, it's going to be gradual.
583
00:46:48.234 --> 00:46:57.341
In areas like maybe packing, skilled hands, judgment, messy situations, that's going to be the tough place.
584
00:46:59.445 --> 00:47:01.306
So let's not freak out when we see these things.
585
00:47:02.567 --> 00:47:04.189
Take all of these things with a grain of salt.
586
00:47:05.009 --> 00:47:09.873
The Doomer narrative, the robots, the jobs apocalypse that should have already been here, and it's not.
587
00:47:12.335 --> 00:47:15.097
Everything take with a grain of salt.
588
00:47:16.818 --> 00:47:20.341
Those are the top three stories of the day that I wanted to get to.
589
00:47:21.416 --> 00:47:22.557
Hope you enjoyed today's episode.
590
00:47:22.697 --> 00:47:26.780
If so, please rate and review on Apple and on Spotify.
591
00:47:27.500 --> 00:47:29.922
We recently switched podcast hosting providers.
592
00:47:29.962 --> 00:47:34.845
So if you're watching on Apple, the video version should be available on Apple as well.
593
00:47:34.905 --> 00:47:37.767
Starting today, you're not going to have to go to YouTube or Spotify for that.
594
00:47:38.567 --> 00:47:45.292
And then again, remember if you download Muse, which I highly, highly recommend that you do.
595
00:47:46.579 --> 00:47:50.862
You can use the promo code BYM5LS.
596
00:47:50.922 --> 00:47:53.603
You and I will both get a billion tokens out of this.
597
00:47:53.623 --> 00:47:59.486
And I can promise you, you're going to want the tokens when you see the things that Muse can actually do for you.
598
00:48:00.267 --> 00:48:05.690
The podcast, Future of Work podcast, the newsletter, Future of Work newsletter, and the CHRO group.
599
00:48:06.310 --> 00:48:09.271
futureofworkleaders.com.
600
00:48:09.311 --> 00:48:11.712
Have a wonderful rest of your day.
601
00:48:12.313 --> 00:48:14.994
And I will be back on Friday.
602
00:48:15.794 --> 00:48:20.156
And hopefully you'll have some fun times playing around with news and getting it to do some cool stuff for you.
603
00:48:20.696 --> 00:48:25.939
If you have any fun examples, email me, jacob at thefutureorganization.com.
604
00:48:26.239 --> 00:48:26.659
See you Friday.
1
00:00:00.915 --> 00:00:05.578
Over the past few days, I've been using a new AI assistant.
2
00:00:06.318 --> 00:00:07.238
I've been using Muse.
3
00:00:07.539 --> 00:00:12.441
And let me just unpack some of the things that Muse was actually able to do for me over the past few days.
4
00:00:13.622 --> 00:00:17.824
So number one, I was able to lower my phone bill by $50 a month.
5
00:00:18.164 --> 00:00:22.266
I was able to lower my internet bill by $50 a month.
6
00:00:22.947 --> 00:00:25.668
It got my home electricity bill reduced.
7
00:00:26.169 --> 00:00:30.451
And it found me a 20% discount on a Dyson vacuum cleaner.
8
00:00:31.168 --> 00:00:33.329
Now, I didn't write any prompts.
9
00:00:34.090 --> 00:00:39.312
I didn't give it very detailed instructions on what to do.
10
00:00:39.793 --> 00:00:47.737
I simply gave these high-level instructions to Muse, to this AI assistant, and I wanted to see what it would actually be capable of.
11
00:00:48.791 --> 00:00:56.517
And I believe that this is the truly first capable agentic assistant that we have seen.
12
00:00:56.697 --> 00:01:05.023
And it is absolutely mind-bogglingly powerful in terms of its capabilities, in terms of the impact that it can have.
13
00:01:05.043 --> 00:01:09.807
And I'm going to unpack a little bit more about that as we get into the rest of the podcast script.
14
00:01:10.367 --> 00:01:11.929
But I want you to imagine...
15
00:01:12.778 --> 00:01:21.510
What this would look like inside of an enterprise where everybody had this kind of a capable agentic personal assistant inside of an organization.
16
00:01:21.530 --> 00:01:28.700
If you have thousands of employees, what is it going to look like if you have thousands of these types of agentic agents?
17
00:01:29.636 --> 00:01:36.558
Now a quick note on Muse, I'm gonna mention an invite code here in case you want to use it.
18
00:01:36.858 --> 00:01:50.961
This is not sponsored, they didn't pay me to do this, they don't know that I'm doing this, but Muse right now is free, and if you use this invite code, you're gonna get a billion tokens on Muse, and I will get a billion tokens, so everybody benefits.
19
00:01:52.062 --> 00:01:56.883
Since they don't have a payment plan right now, I actually already went through my entire weekly budget
20
00:01:57.603 --> 00:02:10.968
And so when you sign in, if you download it, type in the code B-Y-M-5-L-S. Again, that's B-Y-M-5-L-S. You and I are both going to get a billion tokens.
21
00:02:11.828 --> 00:02:23.552
And the way that you do that is when you download Muse, you open up settings on the Muse app and there should be a little tab on the left side that says, yeah, you know, redeem promo code or redeem invite code.
22
00:02:24.052 --> 00:02:29.153
You put in BYM5LS and then it's going to say, congrats, you got a billion Muse tokens.
23
00:02:29.694 --> 00:02:35.655
Trust me, you're going to want to do it when you see the things that Muse is actually capable of.
24
00:02:36.395 --> 00:02:39.736
And like I said, I will go into a little bit more detail in just a minute.
25
00:02:40.176 --> 00:02:41.537
So welcome to Future Ready Today.
26
00:02:41.717 --> 00:02:44.758
It is Wednesday, September 30th, 2026th.
27
00:02:47.500 --> 00:02:49.200
I hope you all had a wonderful, wonderful weekend.
28
00:02:49.741 --> 00:02:51.761
Kind of weird saying that now we're at the middle of the week.
29
00:02:52.361 --> 00:02:53.762
My daughter had a chess tournament.
30
00:02:54.082 --> 00:02:56.703
She did fairly, you know, not terrible.
31
00:02:56.763 --> 00:02:58.083
She played in the open section.
32
00:02:58.603 --> 00:03:01.104
I think she was the only girl in that group.
33
00:03:01.604 --> 00:03:03.004
Maybe there was one other girl there.
34
00:03:03.724 --> 00:03:07.345
So she gained, I think, 20 rating points in that tournament.
35
00:03:07.525 --> 00:03:08.766
Two wins, three losses.
36
00:03:08.786 --> 00:03:08.886
Yeah.
37
00:03:09.866 --> 00:03:11.987
Some of those losses could have been wins.
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You know, we're going to work on that a little bit, but still a fun tournament that happened in Glendale.
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And yeah, so that was pretty much my weekend.
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We did a movie night with the kids, kind of hung out, relaxed.
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And I honestly spent hours and hours and hours and hours playing around with Muse.
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And that is what brings me to the first story of the day.
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So let me briefly give you some context around Muse.
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If you haven't used it, if you haven't played around with it,
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They launched early September, September 8th.
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They call it the world's first personal AI agent built for everyone.
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Built by Meta Super Intelligence Labs.
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This is the division run by Alexander Wang.
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He's the one who founded the company Scale AI.
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And Meta invested about $14.3 billion in the company for a 49% stake.
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And for a while, everybody was basically saying, what the hell's going on with this acquisition?
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You know, what's happening here?
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And here we go.
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All of a sudden, shablam, you get Muse.
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Now, let me just unpack how it works.
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And then I'll walk you through some of the things I was able to do with it.
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So you message Muse just like you would a person.
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You have the app, you have the desktop version, and you just talk to it.
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And when you first log into Muse on the desktop version, they already have a preset list of things that you might want to try out that are pretty cool.
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Negotiating a bill, take a picture of this, do this.
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It has some prompts.
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It keeps working even after you close the app and it goes back and forth with you almost the way that you would when you're texting a personal assistant.
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And when I first heard about it, I was sort of like, yeah, yeah, we have whatever.
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I have Claude, I have ChatGPT already, I have Grok, I have Gemini.
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I already have all this stuff.
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What the hell am I gonna need another AI tool for?
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That was my initial thought going into this.
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I'm reading here that Muse has payment plans.
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I haven't seen them released, but they're supposed to be a $20 and $100 a month plan.
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Yeah.
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I haven't seen them yet.
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They are now, last I heard, over 3.4 million downloads.
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They were number one on the App Store.
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The stock price went up 29% in September.
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Last I checked, I didn't look today.
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So they are dominating.
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And this has been, I think, one of the best releases ever.
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of an AI tool that I have ever seen.
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So their stock today is trading at $733 a share.
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Just to give you some context on where we were a month ago, $572 a share.
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Okay, so on September 8th, when they first made the announcement, before September 8th, they were trading around $600 a share.
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So we're up to 733.
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This has been a blockbuster for them.
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So now the futurist lens, let me unpack this in a little bit more detail.
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Because like you, when I hear about new AI tools and assistants, the first thing that I'm thinking of is, you know, who the hell cares?
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I already have all these things at my disposal.
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Now I'm telling you, this is, and my friends are hooked on it now.
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We're all spending like hours and hours doing this.
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Here's why it feels different from Claude and from OpenAI.
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Now, to use Cloud or to use OpenAI as a true personal assistant, you have to have it do a lot of things.
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You got to build skills.
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You got to do connectors.
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You got to install this.
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There's a lot that you need to do there.
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And then even then, you don't know what it's capable of.
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Like even if you connect everything, you don't know what do you do after you connect all these things.
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So one of the things that I like about Muse is that you just go.
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You just start and you go.
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The second thing that I like about it is it already starts you off with I think a pre-list of maybe 30 different examples of things that you can do.
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So it's really designed for somebody who does not know about all these AI things to take advantage of it.
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And so you just go on there and you say, hey, Muse, I want to lower my phone bill.
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Okay, and this is literally what I did.
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I said, I want to lower my phone bill by 50 bucks and I want to lower my internet bill by 50 bucks.
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It says, great.
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I'm going to log into your AT&T account.
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And literally, you could watch it doing this.
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It logs into my AT&T account.
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It waits on hold with an agent, waiting to get to an agent in the chat.
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And it waited on hold for probably 30 minutes.
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As it's doing this, it's sending me a text saying, hey, just letting you know, I'm waiting, you know, you're number five in queue, et cetera.
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It gets in queue.
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And I had to correct it a little bit because when it first started talking to Rachel at AT&T, it says, hi, I'm Muse, Jacobs, AI assistant.
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And I'm like, no, no, no, no, no, no.
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Don't say that.
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Say that you are me.
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And you want to negotiate my bill.
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You've been a loyal customer for a while.
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It used to be, I don't know, $150 a month.
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Get it down.
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And over the course of 90 minutes, Muse goes back and forth with Rachel, going over promotional offers, going over anything and everything that it can.
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And finally, it was able to negotiate my bill, not exactly by $50, but around $40 a month.
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And it said, check your email.
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The contract is there.
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And I told Muse, check my email, review the contract, make sure everything looks good and that I'm not getting downgraded in service.
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It went through, it opened my email.
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It scanned everything and said, everything looks great.
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You're good to go.
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I was shocked.
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It did it with my internet bill.
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I had a Dyson, I have a Dyson vacuum.
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And for some reason, the darn thing stopped working.
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And so I think the battery died.
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And so I took a picture of it and I put it into Muse and I said, hey, my battery is dead.
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Can you contact Dyson and see if you can get me a replacement battery?
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If not, try to get me a discount on a new product.
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It goes to Dyson.
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It has the serial number and all the information.
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It goes to the website.
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It waits on hold with chat and it has a conversation with an agent.
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And then it gets back to me and says, good news.
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Um, so-and-so said that you can get a 20% discount on a, uh, future Dyson product.
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If you want, they can also send you a discounted battery instead of paying $299.
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They'll give it to you for like a hundred bucks.
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Uh, I'm having it do crazy things for me.
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on LinkedIn as far as who to connect with and cleaning up my connections, going over through all of my monthly subscriptions that I have and trying to audit them and see where I can find better rates and pricing on them.
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It's staggeringly good at what it does.
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And you can watch it do these things if you're on the desktop app.
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And again, there's not a lot of like, if it goes to a website like AT&T.com, it'll ask you in a secure form, what's your username and password?
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Once you do that, you are done.
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You don't build a skill.
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You don't have to do a connector.
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You don't have to watch a tutorial.
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You taught, the way that you would have a personal assistant if they like came to your house and said, hey, Jacob, what can I do for you today?
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And you give them a list of things.
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That is what this thing does.
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I've never seen anything, anything, anything like this.
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It is that good and that useful and that valuable.
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Now, I still use Claude for deep dives around research and analysis and things like that.
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I don't view Claude as a personal assistant.
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I look at Claude and OpenAI as more of, I don't know, research assistants or work assistants or analyzers in that kind of capacity.
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But Muse is actually, I view it as a personal assistant.
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So if you haven't tried it, I cannot stress enough how much you should try it.
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And again, the code is BYM5LS.
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You and I will both get a billion free tokens, which is enough for a long, long time.
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So thinking of Muse, I'm trying to imagine this in the workplace because we don't have this in the workplace yet.
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Now we talk about agents in the workplace, but when we talk about agents in the workplace, we usually talk about it in the form of agents completing a very specific task, like filling out a form or maybe responding to somebody, putting a calendar invite, things like that.
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That is the tip of the iceberg.
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I mean, imagine literally everybody at your company.
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Let's say you have 5,000 employees.
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Imagine everybody at your company now has a personal agentic assistant that can do anything.
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Anything.
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Negotiate salary for you.
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Try to get better benefits for you.
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Try to resolve a conflict for you.
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Yes, respond to your emails for you.
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But it acts like a person.
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You want it to order things for you, to buy things for you, anything.
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You can throw anything at it.
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And so I think that we're going to see three shifts coming very soon in the workplace.
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And in our recent CHRO group meeting that we had a couple of days ago, we were talking about this.
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And by the way, the URL, of course, futureofworkleaders.com, our next event, January 13th and 14th in the Bay Area in person.
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I think we're going to have around 15 or 20 chief human resource and chief people officers there.
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So shift number one.
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when this enters the enterprise, because it's not there yet.
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Not, not like this.
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We have agentic capabilities in the enterprise, not like this.
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And when you use Muse and test it out, you'll know exactly what I'm talking about.
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Shift number one, everyone's going to become a manager.
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Everybody inside of your organization is become, is going to become a manager.
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And the reason why I say that is because even with Muse, I still had to manage it.
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Like it would give me updates on what it was doing and I had to tell it what I wanted to do.
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Hey, Jacob, I wasn't able to get $50 off.
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I could get $40.
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Do you want to take this or not?
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Yeah, I'll take it.
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But can you verify that I'm not actually losing any service and I'm not downgrading anything?
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Sure.
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Let me get back to you on that.
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So you still have to manage it the way that you would a person.
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But it gives you updates as it's working through.
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So everyone is going to become...
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a manager in some capacity.
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And again, nothing like this exists in the enterprise yet, but it's coming.
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So if your day, like you start off your morning, your goal, you're going to set off, start your day by setting goals for your agent.
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Hey, here are five things that I need you to accomplish for me today.
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Here's what I need to help with.
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I need you to do X, Y, and Z.
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So you're going to review the work.
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You're going to approve the decisions that your AI agent is going to make.
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These are managerial qualities and capabilities.
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Muse is literally built around audits, around approvals, around audit trails.
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And so what happens when a 26-year-old analyst inside of your organization is going to be running maybe several agents as a part of a small team?
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And what's going to happen is these employees are now going to ask you all of a sudden, hey, I'm literally managing five agents the same way that I would a person.
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There's no emotional baggage or emotional stress or discussion there, but I'm managing them the same way that I would a person.
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I have to delegate.
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I have to set goals.
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I have to check in.
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I have to review their work.
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I have to make approvals.
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So if I'm doing this and I'm basically managing a small team, why is my title inside of your organization, Jacob Morgan, Analyst One?
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Why am I not a manager?
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Why am I not a financial analyst manager inside the organization?
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Why am I just analyst?
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Why am I paid like an individual contributor when I'm not an individual contributor?
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I'm clearly a manager inside this organization doing the things that a manager does.
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So you can expect job titles like agent lead, AI operations manager, things of that nature.
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They are going to spread.
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They're going to spread the way that the word digital got added to every job title in 2010.
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Digital marketing analyst,
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digital marketer, digital transformation lead, digital blah, blah, blah, blah, blah.
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You're going to see this with agents.
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You're going to see this.
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I think we're already starting to see this in job descriptions.
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So that's going to be shift number one.
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Everyone inside your organization, whether you like it or not, is going to become a manager in some capacity.
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Number two, there is going to be a fight over what managing an agent is worth.
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And the reason why this is going to happen is because for decades, most companies have set manager pay based on scope.
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Usually, what that means is that the number of people that report to you.
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So the more people who report to you, the more people you're managing.
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And so you get paid more.
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And managing people usually means you do what?
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You have coaching sessions with them.
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You go over conflict.
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You go over career trajectory.
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You go over team building.
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You go over alignment to values.
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Managing an agent skips all of that sort of stuff.
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You're purely just focused on the work.
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And so employees will say that managing agents is like managing a team, but employers, employers, I think, are going to push back and they're going to say, wait a minute.
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Managing agents is lighter work.
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It doesn't deserve managerial pay or managerial title because you don't have to do the coaching.
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You don't have to talk about careers.
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You don't have to talk about alignment.
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You don't have to talk about values.
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You don't have to do a lot of the human stuff that is so important for managerial work.
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So there's going to be a little bit of a fight between what it means and what the value is over managing an agent.
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Employees are going to, again, they're going to push back and they're going to say, hey, I'm managing agents
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You know what used to be a team of five.
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This would take five people to do this work that I'm doing.
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So pay me for the output of what it would take to manage a team of five people.
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And I think both sides here are going to have an interesting argument and an interesting debate.
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And a lot of companies are going to have this argument in performance reviews before I think they have a policy in place.
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You can expect before policies exist, you're going to have some employees during your review process who are going to say, Hey, Jacob, you know, over the past couple of months, I've been managing a team of like three to five agents.
264
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I'm...
265
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really curious here.
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What are we going to do in terms of compensation, in terms of benefits, in terms of job title, if I'm managing agents, what are we doing here?
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That's going to come up if it hasn't already inside your organization.
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And you're going to need to know, and you're going to need to prepare for how do you deal with this?
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How do you have this conversation?
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Are you really just going to tell your employees, Hey, wait a minute.
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Uh, you know, it's just a tool.
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It's just a technology.
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Your employees are going to push back and they're going to say,
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then don't ask me to use it.
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Or they're going to say, it's not just a tool and a technology because I have to do the things that a manager does.
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In shift number three, I think pay is going to be re-evaluated.
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It's going to move from headcount to output.
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Once agents are involved and we're starting to see that already, how many people report to you is going to say very little about how much you control.
279
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Companies are going to have to find new ways to measure contribution, to measure value, to measure impact, to measure output.
280
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So this is going to be another big area of debate and conversation.
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Because there's going to be a gap between an employee who uses an agent well and another employee who uses an agent poorly.
282
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So somebody who uses an agent well or poorly, they're going to just say, here's access to my inbox, respond to all the emails for me.
283
00:20:11.285 --> 00:20:16.148
But that agent doesn't have the real world context around who that agent is talking to.
284
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Did you get lunch with this person?
285
00:20:18.386 --> 00:20:20.147
Did you see this person at a meeting recently?
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00:20:20.187 --> 00:20:22.789
Was there a conflict that you recently resolved?
287
00:20:22.809 --> 00:20:24.310
Did you go to college with this person?
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Have you been working together for a while?
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An agent doesn't have that information.
290
00:20:29.253 --> 00:20:34.336
And so the way an agent is going to just manage your emails, it's going to be in a very templated, basic, hey, thanks for responding.
291
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Jacob is available at this time.
292
00:20:35.797 --> 00:20:36.397
Are you available?
293
00:20:36.878 --> 00:20:38.639
Is that how you're going to talk to your good friend?
294
00:20:40.640 --> 00:20:43.222
Or your boss or a team member at your organization?
295
00:20:43.242 --> 00:20:43.342
Yeah.
296
00:20:45.652 --> 00:20:51.615
So managing an agent well is going to mean that you're going to need to give them the context, the information so that they can do their jobs effectively.
297
00:20:53.795 --> 00:20:54.596
And now the chaos.
298
00:20:55.916 --> 00:21:02.499
The chaos is going to happen when my agent is going to talk to your agent.
299
00:21:05.300 --> 00:21:07.461
And I think scheduling is going to be the first thing that's going to break.
300
00:21:08.402 --> 00:21:13.964
Because I'm going to have a rule that says, hey, I don't want to schedule any meetings before 10 a.m. Pacific Standard Time.
301
00:21:15.872 --> 00:21:19.254
You're going to tell your agent, schedule a meeting with Jacob at 8 a.m.
302
00:21:20.375 --> 00:21:31.301
Your agent's going to tell my agent, hey, John wants to schedule this meeting at 8 a.m. My agent's going to say, I'm sorry, Jacob doesn't do meetings before 10 a.m. And your agent's going to say, this is really, really important.
303
00:21:31.321 --> 00:21:31.902
You need to be there.
304
00:21:32.422 --> 00:21:40.987
And our two agents are going to go back and forth ad infinitum arguing over how this meeting is going to get scheduled.
305
00:21:42.551 --> 00:21:43.592
And scheduling is going to break.
306
00:21:44.213 --> 00:21:46.976
Because we're going to set rules for our agents.
307
00:21:46.996 --> 00:21:49.179
And we're going to tell our agents, don't break these rules.
308
00:21:52.442 --> 00:21:57.729
And so, bad things and chaos, I think, is going to happen.
309
00:21:59.471 --> 00:21:59.651
Right?
310
00:21:59.691 --> 00:22:03.075
Today, finding a time for a meeting involves some...
311
00:22:04.393 --> 00:22:05.914
very friendly back and forth.
312
00:22:06.714 --> 00:22:07.515
It's a human thing.
313
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Does this work for you?
314
00:22:08.415 --> 00:22:08.535
No.
315
00:22:08.555 --> 00:22:09.456
Does this work for you?
316
00:22:09.476 --> 00:22:11.797
You know, try to schedule a lunch, a dinner, a meeting, whatever it is.
317
00:22:11.817 --> 00:22:13.638
There's a lot of friendly kind of casual back and forth.
318
00:22:15.559 --> 00:22:23.903
But when every person's agent is going to start to negotiate on your behalf, the agents, you know, they're going to schedule with agents all day.
319
00:22:28.346 --> 00:22:32.568
And that sounds efficient until you realize that nobody actually decided if this meeting is even worth having.
320
00:22:33.800 --> 00:22:36.441
Meetings could multiply because setting them up costs nothing.
321
00:22:41.224 --> 00:22:43.545
So, and this is just for meetings, imagine for emails.
322
00:22:46.367 --> 00:22:52.490
Meetings, emails, presentations, ordering things, canceling things, submitting things, reviewing things.
323
00:22:53.990 --> 00:23:02.395
At a certain point, it is going to break inside of an organization if you have thousands of these agents.
324
00:23:03.292 --> 00:23:06.413
that are communicating with each other as personal assistants.
325
00:23:06.954 --> 00:23:09.375
And this is going to pose a very big challenge for organizations.
326
00:23:09.495 --> 00:23:11.396
Do we even want this capability to exist?
327
00:23:12.356 --> 00:23:17.899
Are we going to allow employees inside of organizations to have these types of personal assistants?
328
00:23:18.279 --> 00:23:32.285
Or are we going to handcuff agents to very specific and narrow tasks, such as reviewing documents, or just using Claude to help with presentations and analysis and reviewing things and preparing things?
329
00:23:36.677 --> 00:23:38.038
And then there's the role of accountability.
330
00:23:40.200 --> 00:23:43.822
When your agent and my agent are going back and forth, who's going to be accountable for what?
331
00:23:44.863 --> 00:23:51.168
If my agent commits to a certain deadline in a conversation with your agent, does that mean that I committed?
332
00:23:51.828 --> 00:23:52.649
Who's responsible?
333
00:23:56.612 --> 00:23:59.014
So there's going to be a lot of information leaks.
334
00:24:01.055 --> 00:24:04.678
Your personal assistant, your agent, is going to remember everything that you tell it.
335
00:24:07.728 --> 00:24:18.512
And if you're using it for work or for personal or for whatever it might be, this information ends up living inside of your organization.
336
00:24:19.912 --> 00:24:20.572
Can it be leaked?
337
00:24:20.652 --> 00:24:21.593
Can it, you know, who knows?
338
00:24:24.413 --> 00:24:26.534
I mean, even today, Muse randomly sent me this note.
339
00:24:26.554 --> 00:24:31.496
It's like so-and-so has a, you know, their promotional offer expires at midnight.
340
00:24:31.516 --> 00:24:32.596
And I'm like, why are you telling me this?
341
00:24:33.836 --> 00:24:35.257
I didn't ask you for this information.
342
00:24:36.546 --> 00:24:46.209
And it said, well, we've been going back and forth on money-saving tips and your phone bill and negotiating this, and I thought maybe this would be helpful.
343
00:24:47.270 --> 00:24:48.690
And I thought, please don't.
344
00:24:48.730 --> 00:24:51.811
I told her, I said, please don't send me these kind of spam promotional offers anymore.
345
00:24:57.033 --> 00:25:00.254
So there's going to be a lot of wild stuff happening inside of organizations.
346
00:25:01.875 --> 00:25:04.916
I don't think it's a reason to panic, but I think it's going to be a reason to have very...
347
00:25:06.427 --> 00:25:11.248
Serious conversations with your legal teams, with your IT teams, with your chief human resource officer teams.
348
00:25:14.169 --> 00:25:15.410
Madness is coming.
349
00:25:15.450 --> 00:25:16.830
And I think eventually we'll figure it out.
350
00:25:17.030 --> 00:25:19.551
But there is going to be a time of chaos.
351
00:25:19.791 --> 00:25:21.912
And that chaos begins now.
352
00:25:23.972 --> 00:25:26.493
Second story of the day, Anthropics leaked numbers.
353
00:25:26.513 --> 00:25:28.394
This was pretty wild.
354
00:25:28.414 --> 00:25:29.114
This was pretty wild.
355
00:25:30.316 --> 00:25:36.998
A draft of Anthropics IPO prospectus obtained by Reuters gives the first detailed look at its finances.
356
00:25:37.198 --> 00:25:42.200
The company posted a net loss of about $42 billion in 2025.
357
00:25:42.240 --> 00:25:51.062
Roughly $34 billion of that was an accounting charge tied to financing that could turn into shares, not actually cash spent on running the business.
358
00:25:52.163 --> 00:25:58.705
Operating loss was $8 billion, up from $2.98 billion the year before that.
359
00:25:59.603 --> 00:26:03.767
on revenue of about $4.6 billion, which is 12 times the prior year.
360
00:26:05.669 --> 00:26:16.900
Anthropic ended 2025 with $20.28 billion in cash and plans to spend $518 billion on computing and infrastructure in the coming years.
361
00:26:18.441 --> 00:26:22.225
About 80% of that must be paid whether it uses the capacity or not.
362
00:26:24.960 --> 00:26:33.722
About 80 of the prospectus' 261 pages are devoted to risk factors compared to 48 pages on the business itself.
363
00:26:35.283 --> 00:26:42.285
For comparison, SpaceX's recent prospectus devoted about 38 out of its 277 pages to risks.
364
00:26:42.445 --> 00:26:51.687
Among them, Anthropic warns advanced AI could pose catastrophic or existential risks to humanity in that models could try to resist shutdown or manipulate information.
365
00:26:54.100 --> 00:27:02.769
On the growth side, second quarter, 2026 revenue topped $11.5 billion, more than double all of 2025.
366
00:27:03.630 --> 00:27:12.039
According to the Financial Times, investors believe Anthropics' annualized revenue could reach $100 billion to $120 billion by the end of 2026.
367
00:27:12.679 --> 00:27:16.844
And they are targeting a valuation of above $2 trillion.
368
00:27:19.129 --> 00:27:24.313
More than double its 965 billion valuation in May.
369
00:27:26.434 --> 00:27:36.722
Meanwhile, OpenAI seeking at least 30 billion at a valuation of about 1.4 trillion with a reported revenue run rate of 68 billion.
370
00:27:37.202 --> 00:27:38.283
Okay, now the futurist lens here.
371
00:27:38.303 --> 00:27:39.624
Let's unpack some of these numbers a little bit.
372
00:27:42.697 --> 00:27:48.380
Now, if you strip out this $42 billion accounting charge, the real loss here is about $8 billion.
373
00:27:48.700 --> 00:27:55.264
So what this means is that they lose around $1.75 for every dollar of revenue.
374
00:27:57.185 --> 00:28:03.088
Now, it's bad, but you can make the argument that it's somewhat typical for an organization that's growing that fast.
375
00:28:03.568 --> 00:28:06.450
But the numbers that should worry investors, I think, are three other numbers.
376
00:28:07.990 --> 00:28:16.837
First, there's the $518 billion in commitments, most of which cannot be canceled, against $20 billion in the bank.
377
00:28:18.739 --> 00:28:27.266
And this to me is like signing a 10-year lease on a ginormous skyscraper when you have enough cash on hand for the first few months of rent.
378
00:28:28.326 --> 00:28:31.769
And you're betting that your revenue is going to grow fast enough to cover the rest of it.
379
00:28:33.451 --> 00:28:35.152
Second, customer concentration.
380
00:28:35.292 --> 00:28:36.093
Two clients...
381
00:28:37.555 --> 00:28:39.576
are about 12% each of the business.
382
00:28:42.237 --> 00:28:49.359
If one of them builds its own or switches providers, you get a huge hole opening up in Anthropic overnight.
383
00:28:52.500 --> 00:28:56.902
Most big customers can walk away because they are not on long-term contracts here.
384
00:28:58.302 --> 00:29:00.803
Now, the interesting thing here is this whole Doomer Paradox narrative.
385
00:29:01.942 --> 00:29:12.484
Anthropic is asking investors for $2 trillion while simultaneously telling them under legal liability that its product could pose an existential risk to all of humanity.
386
00:29:14.365 --> 00:29:17.405
And so Wall Street, they're calling this a head scratcher.
387
00:29:17.425 --> 00:29:20.366
They're basically saying, how can both these things be true?
388
00:29:20.426 --> 00:29:21.146
I don't understand here.
389
00:29:21.806 --> 00:29:27.107
You're telling me your company has a chance of ending all of human life on planet Earth.
390
00:29:27.687 --> 00:29:30.248
Meanwhile, you're asking investors for $2 trillion.
391
00:29:31.578 --> 00:29:33.660
Or saying that your company's valued at $2 trillion.
392
00:29:35.623 --> 00:29:37.265
And I think there are a couple of ways that you can read this.
393
00:29:39.147 --> 00:29:46.195
Now, first is that companies usually on these prospectuses, they're supposed to list every conceivable risk so that they can't be sued later for hiding it.
394
00:29:47.417 --> 00:29:48.337
The second is marketing.
395
00:29:49.297 --> 00:29:52.918
So whether intended or not, our technology is so powerful it could be dangerous.
396
00:29:52.958 --> 00:29:54.319
I went over this in previous episodes.
397
00:29:55.019 --> 00:29:57.139
This is the strongest capability claim that you can make.
398
00:29:57.659 --> 00:30:04.521
In fact, OpenAI recently, they shelved their recent Astra 6.1 model because they said it was too dangerous.
399
00:30:06.421 --> 00:30:11.863
We all know that Anthropics, Dario Amadei and others, they called for a slowdown.
400
00:30:11.903 --> 00:30:12.823
They wanna be regulated.
401
00:30:15.875 --> 00:30:19.016
And so you need to remember that investors, they pay for growth.
402
00:30:19.136 --> 00:30:22.097
They want to give you money because they are investing in growth.
403
00:30:23.597 --> 00:30:27.899
And so the first question is, will investors want a discount?
404
00:30:30.779 --> 00:30:36.121
I think almost certainly on the IPO price because IPOs are usually priced below what the market expects.
405
00:30:36.221 --> 00:30:39.142
And so the stock ends up usually rising on day one.
406
00:30:40.489 --> 00:30:43.931
That's how bankers, you know, they keep their big investors happy.
407
00:30:43.951 --> 00:30:45.232
You see these big pops that happen.
408
00:30:45.252 --> 00:30:46.333
That's what happened with SpaceX.
409
00:30:49.274 --> 00:30:54.237
Across decades of U.S. listings, the average first day gain has been in the high teens.
410
00:30:57.739 --> 00:30:59.120
And so investors might say, wait a minute.
411
00:30:59.941 --> 00:31:06.985
If you're telling us that you want to slow down and get regulated, then I want a discount on that $2 trillion valuation you're telling me about.
412
00:31:11.667 --> 00:31:20.693
So if Anthropic really does hit, and I think they're supposed to IPO in November, OpenAI sometime, maybe Q1, 2027.
413
00:31:21.193 --> 00:31:28.998
If Anthropic really hits $100 billion or more in annual revenue by year end, then a $2 trillion valuation is about 20 times revenue.
414
00:31:30.199 --> 00:31:35.543
And that's roughly where OpenAI is priced today at 1.4 trillion on around 68 billion.
415
00:31:36.704 --> 00:31:40.266
Now in that kind of a world, the price is not crazy.
416
00:31:40.286 --> 00:31:40.426
Okay.
417
00:31:43.594 --> 00:32:02.669
But the bear case on this is that if you value Anthropic on its most recent reported quarter, which is about $46 billion a year, not $68 billion, but $46 billion on a $2 trillion valuation, that's more than 40 times revenue.
418
00:32:04.210 --> 00:32:05.932
And that's pretty high.
419
00:32:08.314 --> 00:32:11.356
And this valuation really only holds if everything goes right for Anthropic.
420
00:32:13.406 --> 00:32:17.009
So I think there's going to be a little bit of a disagreement here between the private markets and the public markets.
421
00:32:17.689 --> 00:32:20.872
Private investors have been pricing these companies on future potential.
422
00:32:21.712 --> 00:32:23.234
SpaceX, future potential.
423
00:32:23.714 --> 00:32:25.115
Anthropic, future potential.
424
00:32:25.255 --> 00:32:26.576
OpenAI, future potential.
425
00:32:27.017 --> 00:32:35.123
Public investors, they're going to price them every single day on the numbers.
426
00:32:36.164 --> 00:32:37.665
Quarterly hits or misses.
427
00:32:41.138 --> 00:32:43.981
And, you know, obviously SpaceX was a mega IPO.
428
00:32:44.001 --> 00:32:53.350
I think you can make the argument that, you know, Anthropic and OpenAI, these are kind of the first duopoly IPOs that we're going to see.
429
00:32:57.795 --> 00:33:02.700
So lots of crazy stuff happening in the AI world here.
430
00:33:04.250 --> 00:33:10.154
The last story of the day that I wanted to get to, also from Anthropic, they published something right before I was going to go live here.
431
00:33:10.174 --> 00:33:11.695
And so I added it quickly to the script.
432
00:33:13.816 --> 00:33:24.783
They published something on the Anthropic blog, huge post, what work can robots do?
433
00:33:26.024 --> 00:33:28.225
And they're not talking about just LLMs.
434
00:33:28.245 --> 00:33:29.786
They're talking about physical robots.
435
00:33:30.795 --> 00:33:37.800
So today, anthropic economists Russell Legate Yang and Maxim Masinkoff, they used CLOD.
436
00:33:38.061 --> 00:33:40.222
So they published something called a robot exposure index.
437
00:33:40.242 --> 00:33:46.127
They used CLOD to assess about 19,000 tasks around across 900 occupations.
438
00:33:47.308 --> 00:33:55.654
For each task, the model researched real robots, judged what they have been shown to do and in what setting, and estimated task and time costs.
439
00:33:57.997 --> 00:34:08.739
The headline, counting any level of capability, 74% of physical work or 34% of all work can be done by robots in some circumstances.
440
00:34:09.739 --> 00:34:12.300
But setting matters enormously.
441
00:34:12.360 --> 00:34:26.203
23% of work time involves tasks that need purpose-built environments like production lines, 10% can be done in orderly human workplaces like warehouses, and just 1% can be done in unpredictable settings like public roads.
442
00:34:27.410 --> 00:34:32.973
Taxi drivers are the most exposed here, followed by agricultural equipment operators and light truck drivers.
443
00:34:33.974 --> 00:34:40.978
For recycling and reclamation workers, robots can perform more than three quarters of tasks in structured workplaces.
444
00:34:42.138 --> 00:34:48.602
Nursing and general repair jobs score low because today's robots can do little of that work, even in controlled environments.
445
00:34:52.095 --> 00:35:04.423
The workers most exposed to robots are more likely to be men, less likely to have a college degree, and lower paid, which is a very different group from the office workers most exposed to AI language models.
446
00:35:06.885 --> 00:35:18.072
If you combine robots with language models, then technology can technically reach tasks making up 81% of work time, according to this.
447
00:35:19.700 --> 00:35:20.781
They also talk about costs.
448
00:35:20.801 --> 00:35:22.541
I'm not going to break down all the costs here.
449
00:35:22.581 --> 00:35:24.602
It was a long, thousands and thousands of words.
450
00:35:26.563 --> 00:35:27.644
So now the futurist lens here.
451
00:35:30.325 --> 00:35:39.330
Do you remember in 2013, two Oxford researchers estimated that 47% of U.S. jobs were at high risk of automation within a decade or two?
452
00:35:39.350 --> 00:35:45.453
47% of jobs were at high risk of automation.
453
00:35:47.691 --> 00:35:50.672
high risk of automation between one to two decades.
454
00:35:52.072 --> 00:35:53.633
They did the exact same thing.
455
00:35:53.673 --> 00:35:57.614
I mean, they didn't have Claude obviously at the time, but they did the same, took the same approach.
456
00:35:59.234 --> 00:36:06.336
More than a decade later, U.S. employment is higher than it was then.
457
00:36:08.296 --> 00:36:12.798
In the mid 2010s, several car makers promised fully self-driving cars by around 2020.
458
00:36:14.263 --> 00:36:15.584
Today, we're barely seeing them.
459
00:36:16.824 --> 00:36:24.408
I mean, we're seeing, well, I guess it depends on how truly self-driving you see the robo-taxis, which released, came out in Austin recently.
460
00:36:25.749 --> 00:36:28.470
So that, you know, 2026 just came out almost a decade later.
461
00:36:30.431 --> 00:36:36.975
So forecasts about what machines and what technology and what AI can do tend to be right about the direction, but very, very wrong about the timing.
462
00:36:37.915 --> 00:36:39.476
Often very badly, by the way.
463
00:36:40.638 --> 00:36:42.700
I think the second thing here is capabilities, not adoption.
464
00:36:42.880 --> 00:36:46.562
So again, number one, we've been with these predictions before, and so far, their predictions have been wrong.
465
00:36:47.643 --> 00:36:49.965
We saw, what's his name?
466
00:36:51.426 --> 00:36:55.929
Jeffrey Hinton, if I'm not butchering his name, known as the father of AI.
467
00:36:56.770 --> 00:37:04.375
He said, what was this, 15 years or so ago, he's on record saying, well, if you're a radiologist, he gave the analogy.
468
00:37:04.415 --> 00:37:06.497
He said, like, you remember Roadrunner and Coyote?
469
00:37:07.652 --> 00:37:09.753
the roadrunner, coyote trying to chase the roadrunner.
470
00:37:09.813 --> 00:37:16.938
He said that if you're a radiologist, you're sort of the coyote that already ran off the cliff and you just haven't looked down yet to see that you're off the cliff.
471
00:37:18.839 --> 00:37:23.382
And he decried that if you're in this space, it's terrible and job loss and blah, blah, blah, blah, blah.
472
00:37:23.683 --> 00:37:24.703
Look at radiologists today.
473
00:37:26.664 --> 00:37:33.529
One of the fastest growing occupations, not just in terms of demand, but also in terms of salary growth.
474
00:37:36.295 --> 00:37:39.237
Jensen Huang pointed this out recently in several interviews that he's done.
475
00:37:39.257 --> 00:37:41.699
We've been wrong consistently about the predictions.
476
00:37:42.800 --> 00:37:44.901
There was also a Wall Street Journal article that came out today.
477
00:37:44.942 --> 00:37:51.967
I didn't cover it, but it said that the, you know, all the stories about college, college graduates having higher unemployment.
478
00:37:52.647 --> 00:37:58.312
They said that it's, you know, they're debunking some of that saying, well, it's really due to seasonal effects.
479
00:37:59.312 --> 00:38:02.395
A lot of it happening over summertime, June, July, August.
480
00:38:03.202 --> 00:38:06.204
They're saying that correlation does not imply causation.
481
00:38:06.644 --> 00:38:08.926
So they're debunking already some of these things.
482
00:38:09.866 --> 00:38:11.487
We've been wrong about the job loss numbers.
483
00:38:11.587 --> 00:38:13.128
We've been wrong about the radiologist thing.
484
00:38:13.168 --> 00:38:16.450
We've been wrong about the self-driving cars.
485
00:38:17.091 --> 00:38:25.676
We've been wrong about robots taking... We've been wrong collectively in every way that you can think.
486
00:38:29.198 --> 00:38:31.760
So we have been here before with all of these types of predictions.
487
00:38:33.390 --> 00:38:34.791
Second, capabilities not adoption.
488
00:38:34.951 --> 00:38:38.713
Anthropics own researchers showed this earlier this year.
489
00:38:38.893 --> 00:38:50.020
AI could theoretically, theoretically, I love that word, theoretically could handle 94% of tasks in computer and math jobs, but was actually being used in just 33% of them.
490
00:38:52.001 --> 00:38:58.305
So something a machine can do in a demo is very different than when a business actually deploys it.
491
00:38:59.334 --> 00:39:06.757
These things are separated by cost, by trust, by safety, rules, insurance, unions, governance, policy, retraining, habit.
492
00:39:09.498 --> 00:39:12.500
It's not, you know, it's not the same.
493
00:39:14.120 --> 00:39:18.742
Third, there's a concept known as Moravec's paradox.
494
00:39:18.822 --> 00:39:24.745
In the 1980s, roboticist Hans Moravec pointed out the things that we find very hard, like chess, chess,
495
00:39:26.256 --> 00:39:40.885
Math, these are relatively easy for machines because they rely on brute compute, calculating different possibilities and options, brute force computing, just going through a lot of different possibilities and scenarios.
496
00:39:41.886 --> 00:39:51.552
These things are relatively easy for machines because you give them more compute, more power, more speed, and they can figure out things faster than a human can and more accurately.
497
00:39:54.174 --> 00:39:55.755
But the things that a toddler can do
498
00:39:57.880 --> 00:40:01.845
usually without thinking, like picking up a toy, walking across a messy room.
499
00:40:02.426 --> 00:40:06.932
These are incredibly hard for AI tools and robots.
500
00:40:09.215 --> 00:40:13.981
Think about like untangling a tangled up extension cord.
501
00:40:16.186 --> 00:40:23.312
If you see the videos on X all over social media of these robots that are going into people's homes and cleaning, I think figure three did some of these.
502
00:40:23.932 --> 00:40:25.574
There was another company that escapes me.
503
00:40:27.135 --> 00:40:27.876
I can't remember their name.
504
00:40:29.337 --> 00:40:31.358
They're charging $30 an hour per robot.
505
00:40:31.378 --> 00:40:32.960
These robots go into homes to clean.
506
00:40:35.744 --> 00:40:39.607
And if you've seen these videos, these robots move at a snail's pace.
507
00:40:39.627 --> 00:40:43.090
And there's usually a human operator on the other end trying to help and guide the robot.
508
00:40:43.791 --> 00:40:46.894
Now, I'm not saying eventually they're not going to get to a point where they're more useful.
509
00:40:49.176 --> 00:40:53.580
But the way that they fold, it's literally like they fold towels like this.
510
00:40:54.120 --> 00:40:57.143
They move very slowly.
511
00:40:57.263 --> 00:41:00.806
They are, they're not yet useful.
512
00:41:04.218 --> 00:41:08.161
These mundane things are the hardest things for robots to do.
513
00:41:13.905 --> 00:41:14.965
This study again proves it.
514
00:41:15.045 --> 00:41:22.430
Reasoning holds robots back on only 8% of tasks, while hands and dexterity holds them back on half of tasks.
515
00:41:23.931 --> 00:41:29.435
So AI can maybe write your legal memo for you, but a robot still struggles to untangle an extension cord.
516
00:41:31.216 --> 00:41:33.658
And that's the interesting and the fascinating paradox behind these things.
517
00:41:37.212 --> 00:41:48.494
And fourth, and this is the biggest issue that I keep having with these studies, breaking a job into a series of tasks is the wrong way to measure what a robot can do.
518
00:41:49.474 --> 00:41:52.015
Because the work actually happens in between the tasks.
519
00:41:52.435 --> 00:41:57.435
The study, and all studies like it, especially from academics, they treat jobs like a checklist.
520
00:42:00.016 --> 00:42:06.717
If a robot can do 97% of the checklist, therefore, 97% of your job is exposed.
521
00:42:08.753 --> 00:42:13.114
But as I consistently talk about, a job isn't a list of tasks.
522
00:42:13.234 --> 00:42:15.895
A job, it's a glue between the tasks.
523
00:42:17.496 --> 00:42:20.057
Think about a packer, for example, in a warehouse.
524
00:42:21.097 --> 00:42:23.458
The robot can do 97% of the tasks.
525
00:42:23.498 --> 00:42:24.298
But what about the other 3%?
526
00:42:24.598 --> 00:42:31.541
The jammed box, the damaged product, the order that doesn't match the label.
527
00:42:33.561 --> 00:42:34.602
Somebody has to be there for that.
528
00:42:34.622 --> 00:42:35.702
A robot doesn't know what to do.
529
00:42:38.000 --> 00:42:42.601
So you don't really replace the person, you pay for the robot and you keep a person nearby anyway.
530
00:42:42.621 --> 00:42:48.843
And so that last 3% that the robot can't do costs as much as the first 97%.
531
00:42:51.764 --> 00:43:00.086
So if you have robots that are doing these things, if a robot can't get stuck on a certain thing, it slows down everything.
532
00:43:02.517 --> 00:43:11.880
And so that 3%, we talk about it as if it's not a big deal, but it's a huge deal because that 3% jams up and costs the other 97%.
533
00:43:12.460 --> 00:43:13.701
You still got to have a person there.
534
00:43:14.541 --> 00:43:15.701
Or the robot has to wait.
535
00:43:16.822 --> 00:43:17.782
Everything shuts down.
536
00:43:17.842 --> 00:43:18.983
The person has to come in.
537
00:43:23.024 --> 00:43:31.867
The valuable part of most jobs is the judgment, the discernment, the switching, the exceptions, the context.
538
00:43:32.935 --> 00:43:35.277
A nurse, for example, doesn't just perform tasks.
539
00:43:37.019 --> 00:43:39.181
They notice when a patient might look a little off.
540
00:43:40.382 --> 00:43:43.284
Reassure a scared family member when they come in to bring in a patient.
541
00:43:44.986 --> 00:43:48.249
They decide what matters most in the next five minutes.
542
00:43:48.369 --> 00:43:50.491
You can't see that in a task list.
543
00:43:51.652 --> 00:43:52.272
You just don't.
544
00:43:56.956 --> 00:43:57.397
You can't.
545
00:43:59.197 --> 00:44:04.000
I mean, you look at any aspect of a job and how much of your job requires context.
546
00:44:05.060 --> 00:44:09.403
Knowing the people, the situation, what to do if something goes wrong,
547
00:44:10.968 --> 00:44:11.988
how to talk to somebody.
548
00:44:12.008 --> 00:44:13.569
Like there's a lot of context.
549
00:44:13.589 --> 00:44:25.752
So I always say, and I can't stand when these organizations do this, you cannot break down a job into a series of tasks, say how many of those tasks a robot can do, and then say, well, hey, 97% of that job is exposed.
550
00:44:25.932 --> 00:44:29.273
Therefore, 97% of jobs in that category are exposed.
551
00:44:29.293 --> 00:44:30.153
It's not how it works.
552
00:44:30.694 --> 00:44:31.674
It's not reality.
553
00:44:31.694 --> 00:44:32.414
It's not real.
554
00:44:34.335 --> 00:44:35.235
It doesn't make sense.
555
00:44:36.710 --> 00:44:41.112
And then it doesn't account for what actually has to be required for that to take shape.
556
00:44:42.272 --> 00:44:45.674
The rules, the policy, the guidelines, the trust, the data, the security.
557
00:44:45.694 --> 00:44:49.636
It's just an endless stream of things that have to happen.
558
00:44:53.477 --> 00:45:01.701
Finally, if you notice where the robots work, the robots only work well in environments that are specifically built for them.
559
00:45:03.348 --> 00:45:09.091
So most of the 74% that I talked about earlier, it requires a production line or an orderly warehouse.
560
00:45:10.352 --> 00:45:15.175
In other words, you build the surroundings for a robot and then the robot can do a good job.
561
00:45:15.975 --> 00:45:27.762
If I had a robot walking into this studio here where I have some lights, I have some moving blankets to dampen sounds, I have some random stuff here and there, a robot's not going to know what to do in here.
562
00:45:29.535 --> 00:45:30.196
What to clean up.
563
00:45:30.236 --> 00:45:36.120
It's not going to know that there's a little mixer over here, that I have like a little iPhone holder here, a portable mic here.
564
00:45:36.140 --> 00:45:37.801
It's not going to know what these cables are.
565
00:45:37.821 --> 00:45:39.602
I have like a ceiling fan remote.
566
00:45:39.622 --> 00:45:40.663
Where's it going to put these things?
567
00:45:41.043 --> 00:45:43.385
There's an SD card floating around, a water bottle.
568
00:45:43.585 --> 00:45:45.306
It's not going to have, it's not going to know what to do.
569
00:45:46.327 --> 00:45:47.688
It's not going to be able to clean up this room.
570
00:45:49.309 --> 00:45:51.051
Maybe I can say vacuum the floor.
571
00:45:54.313 --> 00:45:55.474
But it doesn't have the context.
572
00:45:57.024 --> 00:46:00.466
unless you build the settings and the environment for the robot.
573
00:46:03.368 --> 00:46:09.071
Only 1% of work time happens in an unpredictable real world where robots can manage.
574
00:46:10.032 --> 00:46:13.014
And so the real barrier, I think, is not necessarily just building a better robot.
575
00:46:13.054 --> 00:46:15.175
It's how do you rebuild the world around robots?
576
00:46:16.716 --> 00:46:19.717
And that's slow and that's expensive and it's time consuming.
577
00:46:21.679 --> 00:46:25.581
And it doesn't make sense in every situation and scenario.
578
00:46:28.820 --> 00:46:31.842
So the takeaway for me isn't that robots are coming for everyone's job tomorrow.
579
00:46:33.263 --> 00:46:34.945
And it isn't that robots are never going to matter.
580
00:46:37.486 --> 00:46:40.288
It's that change is going to happen, I think, job by job.
581
00:46:41.549 --> 00:46:42.490
I think it's going to be slow.
582
00:46:42.610 --> 00:46:44.211
In a lot of areas, it's going to be gradual.
583
00:46:48.234 --> 00:46:57.341
In areas like maybe packing, skilled hands, judgment, messy situations, that's going to be the tough place.
584
00:46:59.445 --> 00:47:01.306
So let's not freak out when we see these things.
585
00:47:02.567 --> 00:47:04.189
Take all of these things with a grain of salt.
586
00:47:05.009 --> 00:47:09.873
The Doomer narrative, the robots, the jobs apocalypse that should have already been here, and it's not.
587
00:47:12.335 --> 00:47:15.097
Everything take with a grain of salt.
588
00:47:16.818 --> 00:47:20.341
Those are the top three stories of the day that I wanted to get to.
589
00:47:21.416 --> 00:47:22.557
Hope you enjoyed today's episode.
590
00:47:22.697 --> 00:47:26.780
If so, please rate and review on Apple and on Spotify.
591
00:47:27.500 --> 00:47:29.922
We recently switched podcast hosting providers.
592
00:47:29.962 --> 00:47:34.845
So if you're watching on Apple, the video version should be available on Apple as well.
593
00:47:34.905 --> 00:47:37.767
Starting today, you're not going to have to go to YouTube or Spotify for that.
594
00:47:38.567 --> 00:47:45.292
And then again, remember if you download Muse, which I highly, highly recommend that you do.
595
00:47:46.579 --> 00:47:50.862
You can use the promo code BYM5LS.
596
00:47:50.922 --> 00:47:53.603
You and I will both get a billion tokens out of this.
597
00:47:53.623 --> 00:47:59.486
And I can promise you, you're going to want the tokens when you see the things that Muse can actually do for you.
598
00:48:00.267 --> 00:48:05.690
The podcast, Future of Work podcast, the newsletter, Future of Work newsletter, and the CHRO group.
599
00:48:06.310 --> 00:48:09.271
futureofworkleaders.com.
600
00:48:09.311 --> 00:48:11.712
Have a wonderful rest of your day.
601
00:48:12.313 --> 00:48:14.994
And I will be back on Friday.
602
00:48:15.794 --> 00:48:20.156
And hopefully you'll have some fun times playing around with news and getting it to do some cool stuff for you.
603
00:48:20.696 --> 00:48:25.939
If you have any fun examples, email me, jacob at thefutureorganization.com.
604
00:48:26.239 --> 00:48:26.659
See you Friday.
