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Speaker 1: Bloomberg Audio Studios, podcasts, radio news bringing out to that

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Speaker 1: special conversation that we mentioned at Bloomberg in Vest, Gary Coheneweischer,

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Speaker 1: IBM and Bloomberg's Matt Miller.

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Speaker 2: Let's watch and listen live. Yeah, we have right now.

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Speaker 1: During that period of time, you had to pre program

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Speaker 1: a huge database with all of the answers because we

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Speaker 1: didn't have the real time connectivity to the data around

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Speaker 1: the world. Today, if you were going to play Jeopardy,

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Speaker 1: you would completely do it differently. You would just allow

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Speaker 1: the AI machine to go get the answer instantaneously. Forty

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Speaker 1: years ago, you had to sort of put the answers

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Speaker 1: in the room and hope that Jeopardy asked the questions

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Speaker 1: in the categories you wanted. It was easier to program

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Speaker 1: all the chess moves. Today, you know, AI has evolved

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Speaker 1: dramatically and I think we're all living through this. I

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Speaker 1: would say evolution. I'm not sure it's a revolution evolution

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Speaker 1: of AI. You know, we've gone from the concept and

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Speaker 1: if you look at sort of CEO surveys or corporate surveys,

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Speaker 1: you know, a year or two ago, I think it

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Speaker 1: was ninety about ninety percent of CEOs said, they, you know,

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Speaker 1: clearly we're going to be involved in use AIAI. And

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Speaker 1: of those ninety percent, less than ten percent could actually

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Speaker 1: tell you what they were going.

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Speaker 2: To use it for.

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Speaker 1: They just felt like they needed to talk about using it. Today,

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Speaker 1: I think the amount of CEOs they are using AI

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Speaker 1: continues to grow, but more importantly, they actually know what

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Speaker 1: they're going to use it for, and the definitional outcomes

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Speaker 1: of where AI can enhance your business is becoming clear.

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Speaker 1: That's where they are today. I think it's going to

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Speaker 1: continue to evolve to where you can use AI in

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Speaker 1: your business and where the paybacks are and how you create.

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Speaker 2: Real productivity growth.

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Speaker 1: I won't geek out on productivity growth, but at the

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Speaker 1: end of the day, you know, if you want to

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Speaker 1: grow GDP and you want to grow your economy, it

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Speaker 1: is all about getting more productivity, getting more units of

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Speaker 1: work your labor force.

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Speaker 3: So I think about this a lot in a competitive sense.

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Speaker 3: If you're a big business and you haven't figured out

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Speaker 3: how you're going to.

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Speaker 2: Use AI yet, are you too late? Have you lost out?

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Speaker 1: You're never too late. You're never too late. You know

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Speaker 1: what you may be late is you may be at

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Speaker 1: a competitive disadvantage to your nearest competitor because they may

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Speaker 1: be able to do certain things more efficiently than you,

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Speaker 1: But you can catch up quickly, so you're not that

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Speaker 1: far behind. So you have to sort of figure out

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Speaker 1: where the opportunities are in your business. And it depends

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Speaker 1: on size, scope scale of your business, so like a

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Speaker 1: very obvious place where in a bigger company. And this

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Speaker 1: is something we did at IBM, and we've talked about it.

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Speaker 1: We like being client zero for all of our products.

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Speaker 1: You know, we did something relatively simple. It sounds simple,

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Speaker 1: but it was not that simple, an HR chat pot.

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Speaker 2: You think of what most people use.

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Speaker 1: HR for after the day one hiring experience of what

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Speaker 1: they want is, you know, they go buy an apartment,

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Speaker 1: they go rent an apartment, they get some they need

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Speaker 1: a reference letter, they need a reference leative. What day

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Speaker 1: did I start working? How long have I worked here?

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Speaker 1: What was my last year's conversation? What was my bonus?

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Speaker 1: And you want that scent like that minute. And you

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Speaker 1: know in a year, a year or two ago, used

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Speaker 1: to call someone in HR, used to give them the information.

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Speaker 1: You said, tell them where you want it sent, and

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Speaker 1: you hope and pray they send it out quickly. Today

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Speaker 1: you're at IBM, you will type it into a HR

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Speaker 1: chatbot and in a minute you said hit send, the

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Speaker 1: letter will go out. So people are actually very happy.

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Speaker 1: Oh my god, I get what I want. I get

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Speaker 1: all this information. You have information about your benefit and

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Speaker 1: information about your healthcare. We do it all real time.

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Speaker 1: We cut six hundred people out of our HR department.

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Speaker 1: Now we didn't fire them, we redeployed them to places

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Speaker 1: where they can be more useful.

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Speaker 2: And our satisfaction scores for.

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Speaker 1: Our employees using HR has gone up dramatically because they

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Speaker 1: get real time answers. So it depends what you're trying

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Speaker 1: to achieve in your company. You know, there's a lot

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Speaker 1: of people using AI today and.

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Speaker 2: Code assist in writing new code.

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Speaker 1: So if you're in a big code writing shop or

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Speaker 1: writing new code is important to you, and you're at

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Speaker 1: a disadvantage.

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Speaker 2: Because your competitors are using a.

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Speaker 1: Code assist program and you're you're at a competitive disadvantage.

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Speaker 1: You take a lot of people here on financial services industry,

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Speaker 1: most banks happen to know what banks do. Most banks

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Speaker 1: historically build a technology infrastructure on a sort of as

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Speaker 1: needed basis, and if you go pull back the covers.

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Speaker 1: They've got lots of different systems in a variety of

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Speaker 1: different languages, and when you try and integrate them and

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Speaker 1: have them talk to each other, it's very difficult. So

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Speaker 1: every bank during the course of the last decade or

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Speaker 1: twenty years tried to think, how do I get all

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Speaker 1: of my software? How do I get all my systems

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Speaker 1: on a common language. It was an arduous task that

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Speaker 1: was cost prohibitive. Today it's not cost prohibitive. Today you

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Speaker 1: can get a code assist bot to basically port one

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Speaker 1: language over to another pro another language, and it's pretty

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Speaker 1: flawless and how it happens. So there's a lot of

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Speaker 1: places evolving today where AI is very useful, and it.

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Speaker 2: Depends on your I think your circumstances.

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Speaker 3: I want to ask you about the cost because I

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Speaker 3: think the reason it was so daunting in my mind,

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Speaker 3: especially for a information technology company, is I was looking

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Speaker 3: at open AI and thinking, you've got to invest one

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Speaker 3: hundred billion dollars to create a large language model, and

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Speaker 3: only a few of these giant hyperscalers in Silicon Valley

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Speaker 3: can do it.

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Speaker 2: Now.

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Speaker 3: Obviously, Deep Seek, although there are questions about how exactly

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Speaker 3: they did it how much it costs, shows that you

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Speaker 3: can do it for far less.

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Speaker 1: Well, thank you the Chinese Deep Seek for proving out

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Speaker 1: what a lot of us knew in this country but

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Speaker 1: no one wanted to listen to. So if you look

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Speaker 1: at what we've been doing at IBM, you look at

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Speaker 1: what met has been doing, you look at a lot

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Speaker 1: of companies here in the United States been doing. We've

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Speaker 1: been building small models.

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Speaker 2: What we have found and Deep Seek sort of proved

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Speaker 2: this out. The small models are much.

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Speaker 1: More efficient, they compute faster, they take less power, but

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Speaker 1: they're not a model that can do everything. They're sort

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Speaker 1: of targeted models. So the things that I was talking

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Speaker 1: to you about, we have small models that are used

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Speaker 1: for specific targeted goals. I think that is the future.

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Speaker 1: We think that is the future of AI. We think

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Speaker 1: most companies will engage with as many small models as

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Speaker 1: they need to get done what they need to get

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Speaker 1: done where they're using AI, and it will be vastly

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Speaker 1: more efficient, vastly more cost effective for them. And I

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Speaker 1: think this is the future where we're going. Look, will

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Speaker 1: there be some large language models out there that are effective, Yes,

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Speaker 1: but they will sort of be the catch all models

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Speaker 1: for everything, so.

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Speaker 3: It still costs a lot of money to invest. I

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Speaker 3: think Moody's estimated it's going to be two billion dollars

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Speaker 3: in investment over the next five years, which doesn't seem

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Speaker 3: that far off when I look at the kapex plans

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Speaker 3: of these mag seven companies. You and I both lived

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Speaker 3: through the Internet bubble and watched so many companies put

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Speaker 3: money into paying down fiber cable and build the infrastructure

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Speaker 3: the data centers of Web one point zero for a

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Speaker 3: lot of CAPEX when they weren't getting a lot of

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Speaker 3: return on investment, at least not for years if they

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Speaker 3: ever did.

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Speaker 2: Do you see real parallels here? Yeah? Yes.

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Speaker 1: Look, as you're building technological infrastructure and backbone, you're building

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Speaker 1: it based on the available what's available today.

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Speaker 2: In what you know today, the whole.

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Speaker 1: AI backbone infrastructure, compute technology, it's moving very fast. But

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Speaker 1: you can't wait for version two point zero or version

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Speaker 1: three point zero, version four point oh, you have to

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Speaker 1: build based on what's build what's available today. What's available

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Speaker 1: today will be obsolete a lot of it before it

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Speaker 1: gets built, and it will get replaced.

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Speaker 2: By newer and newer versions.

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Speaker 1: So there is a bunch of money being spent today,

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Speaker 1: they'll have very low returns to no returns. But if

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Speaker 1: you don't start investing, you'll never start.

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Speaker 2: So you sort of have this.

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Speaker 1: Prisoner's dilemma that I have to start building this data

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Speaker 1: center and I think I'm going to be putting x

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Speaker 1: y z chip in, but by the way it gets

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Speaker 1: built in two years, I may not be putting most

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Speaker 1: likely will not be putting that chip in, And the

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Speaker 1: amount of power I think I need to run that

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Speaker 1: data center may be dramatically less because the next chip

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Speaker 1: I bet is going to be a lot more efficient

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Speaker 1: than the chip we have today. So a lot of

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Speaker 1: this build you can't wait for the efficient frontier because someone's.

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Speaker 2: Always going to be out there.

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Speaker 1: You have to keep building, and there will be a

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Speaker 1: lot of waste to get there. I just think it's inevitable.

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Speaker 1: And then you know, these AI models themselves are going

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Speaker 1: to build upon themselves, and they're gonna get smarter and smarter,

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Speaker 1: and they're going to optimize and re optimize, and everything's

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Speaker 1: going to get better and faster and quicker, and ultimately

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Speaker 1: the AI of this of the data center is going

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Speaker 1: to tell you how to build better data center, how

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Speaker 1: to build better connectivity, how to optimize the optimization.

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Speaker 3: What do you do though as an investor, I mean,

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Speaker 3: you know, as an IBM or as a Microsoft or

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Speaker 3: as you point out, they don't have any choice, They've

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Speaker 3: got to build now. But what do you do as

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Speaker 3: a as a private investor or what do you do

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Speaker 3: as someone who's looking at markets?

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Speaker 2: How do you analyze that? Well, it's interesting if you

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Speaker 2: look at the.

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Speaker 1: History and the history of technology is not that old,

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Speaker 1: and if you look at you know, sort of what's

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Speaker 1: going on here. Ultimately, the bigger companies are going to have.

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Speaker 2: A real big impact in this now.

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Speaker 1: It's really and then I'll point out that all the

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Speaker 1: bigger companies that we talk about today, whether it be

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Speaker 1: the Metas or the Googles. You know, I remember when

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Speaker 1: Facebook started, it was a really small company.

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Speaker 2: And I'm not that old. I'm old enough, but I'm

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Speaker 2: not that old.

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Speaker 1: So all these big companies, I remind people, start out

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Speaker 1: as really small companies. So there are a bunch of

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Speaker 1: small companies so that are going to be really big

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Speaker 1: companies in less in that path, one of these bigger

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Speaker 1: companies can buy them out. You know, We've now got

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Speaker 1: companies that can go spend on hundred million dollars in

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Speaker 1: buy out a company you know, relatively easily today. So

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Speaker 1: if you were looking at what you think is the

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Speaker 1: most likely path, I think the most likely path is

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Speaker 1: the big I don't know if it's the mag seven,

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Speaker 1: but the bigger companies who are spending the most in

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Speaker 1: CAPEX are the most likely winners, and they will you know,

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Speaker 1: they many will.

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Speaker 2: Fund some of these startups.

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Speaker 1: And almost all of the big companies have huge venture

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Speaker 1: arms because they're trying to spend money in these startups.

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Speaker 2: They're trying to get.

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Speaker 1: Opportunities to let creative people create, and then as those

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Speaker 1: products become really valuable, they're just going to buy them

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Speaker 1: and merge them into the bigger companies. So, you know,

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Speaker 1: I guess my investment advice is, like, you can own

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Speaker 1: some of these small companies and most are private, and

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Speaker 1: they're probably interesting to own a portfolio of them. But

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Speaker 1: if you ask me where I think this is going

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Speaker 1: to be a decade from now, I think the bigger

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Speaker 1: companies are going to dominate the space.

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Speaker 3: Is that part of what you're doing at IBM? I mean,

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Speaker 3: do you help Arvin Krishna maybe zone in on some targets.

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Speaker 2: We definitely have a ventures portfolio.

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Speaker 1: We're always looking for something that's on the cutting edge

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Speaker 1: that fits within our parameters.

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Speaker 2: You know, where there's.

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Speaker 1: Hybrid cloud, open AI, quantum, you know.

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Speaker 2: But we're very much in the open source.

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Speaker 1: So we're gonna buy open source company if it fits

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Speaker 1: within our parameters and we think it's got an interesting opportunity.

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Speaker 1: It's in our shareholder's best interest for us to get

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Speaker 1: involved in the company early, not late.

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Speaker 3: By the way, a little bit detached from the specific

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Speaker 3: AI conversation, what do you think about the M and

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Speaker 3: I M and A landscape right now?

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Speaker 2: When we came into.

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Speaker 3: This year, me and Shanali and Katie, we're joking about

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Speaker 3: an opinion writer who said, maybe there won't be that

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Speaker 3: many deals because everybody said this is going to be

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Speaker 3: the year you know that the regulators drop their guard

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Speaker 3: and everybody can get together. Now it's starting to look

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Speaker 3: like that may not be the case.

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Speaker 1: Well, that was the total market euphoria from November election

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Speaker 1: January twentieth. Was deregulation, ease of doing business, the FDC,

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Speaker 1: the CFDC, the SEC, everything C is going to get

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Speaker 1: easier and be much more pro business and we're going

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Speaker 1: to have this huge influx, this huge, massive explosion of

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Speaker 1: deals of M and A, deals of IPO deals.

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Speaker 2: Look, we're forty five or forty six days into this.

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Speaker 1: I don't want to judge this book by its cover,

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Speaker 1: but the first forty five days we've not had an

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Speaker 1: explosion of deals. In fact, probably based on what's going

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Speaker 1: on in markets right now, in market.

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Speaker 2: Multiples, we've probably lost deals.

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Speaker 1: So I would say the calendar from January twenty today

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Speaker 1: is smaller than it was. That's not a regulatory statement,

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Speaker 1: that's a valuation statement. And you also have to be

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Speaker 1: reminded that some of these commissioners are not in place

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Speaker 1: heads of SEC. Even the FDC doesn't have a full

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Speaker 1: Republican slate, it's got two Republicans too Democrats, so it's

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Speaker 1: hard to get things through on to too. These things

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Speaker 1: take more time, So I think some of that euphoria

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Speaker 1: is still there, but I think more people are taking

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Speaker 1: the approach of Okay, you're going to have to show me.

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Speaker 1: It's a different environment than just trust me. I think

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Speaker 1: we've gone from trust me to show me.

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Speaker 3: I want to get back to regulation in a minute,

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Speaker 3: as it pretends to AI, But you mentioned productivity. You

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Speaker 3: don't want to geek out on it. We haven't seen

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Speaker 3: much of an impact yet from.

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Speaker 2: A on productivity.

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Speaker 3: I actually have a chatbot now open next to my

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Speaker 3: Bloomberg screens every morning, so make you more productive. It's

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Speaker 3: I'm actually getting a lot more detail into my newscast

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Speaker 3: than I would otherwise.

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Speaker 2: I use it as more of a glorified search.

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Speaker 3: But we haven't really seen it boost the numbers economically,

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Speaker 3: And I wonder when you think we're going to see

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Speaker 3: that and if it's going to actually have an effect

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Speaker 3: on jobs. You mentioned that you've moved HR people out

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Speaker 3: because you can replace them with in that role with AI.

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Speaker 3: Are we going to see you know, an uptick in

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Speaker 3: or a loss in non farm payrolls because of it?

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Speaker 1: So we've also created you know, artificial workers, you know,

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Speaker 1: So we now can create artificial workers in certain in

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Speaker 1: our consulting business. We now have artificial workers who you know,

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Speaker 1: will work twenty four hour days, which is.

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Speaker 2: Kind of nice, just like Doge. Yeah, so.

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Speaker 1: I don't know if I'm going to make that you

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Speaker 1: made that comparison, I will not own that comparison. So

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Speaker 1: let let's talk about your question. I think your question is,

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Speaker 1: you know, if AI is going to be successful, are

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Speaker 1: we going to see a real impact to labor and

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Speaker 1: employment numbers around the world?

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Speaker 2: Let leonne in the United States.

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Speaker 1: So my pretty bold answer is, and I always say

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Speaker 1: this to rooms, to people like this who usually first

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Speaker 1: question I get is isn't this devastating the economy? I say, look,

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Speaker 1: I'll give you the rest of the time I'm up here,

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Speaker 1: which is very short at this point, so it's not

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Speaker 1: a lot of time. I'll give you the rest time

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Speaker 1: I'm up to come up with a major technological advancement

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Speaker 1: that destroyed jobs, and you can go.

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Speaker 2: Back and you can take anyone you want.

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Speaker 1: And so like, let's just take the internal combustion engine

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Speaker 1: that was supposed to destroy jobs because all those people

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Speaker 1: they cleaned stalls and put.

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Speaker 2: Shoes on horses, they were going to lose their jobs.

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Speaker 1: Well, but all of a sudden, the next day when

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Speaker 1: we started putting internal combustion engines on the streets. We

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Speaker 1: needed mechanics, we needed gas stations, we needed we needed

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Speaker 1: different skill sets, different people. But all of a sudden,

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Speaker 1: there were more jobs created because people could go you know,

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Speaker 1: fifty miles a day or a hundred miles a day

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Speaker 1: versus two miles a day, so their landscape, their business

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Speaker 1: model expanded. It's no different than when the internet. Like

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Speaker 1: I worked at a bank before we had email. We

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Speaker 1: used to have every floor head had a person that

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Speaker 1: printed the memos and put it in your mailbox and

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Speaker 1: told you what time the meeting was or told you

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Speaker 1: what time to go. What happened to that person who

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Speaker 1: delivered the mail on the floor and printed the memos.

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Speaker 1: You know, they sort of disappeared overnight.

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Speaker 2: Now we didn't have less people.

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Speaker 1: In fact, every bank, I guarantee you since the invention

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Speaker 1: of email.

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Speaker 2: Is double its size.

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Speaker 1: Why because we got more productive. We got email coverage,

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Speaker 1: so we could cover more clients. We could cover more

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Speaker 1: people around the world. We could communicate quicker, we could

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Speaker 1: communicate faster. I think AI is the next leg in

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Speaker 1: this evolution of making us more productive. So look, will

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Speaker 1: there be a change in skill sets?

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Speaker 2: Yes? Will people do jobs that they hate?

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Speaker 3: No.

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Speaker 1: The machine can input data. The machine can take codes

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Speaker 1: out of an operating room in a hospital and input

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Speaker 1: them into the insurance reimbursement system. By the way, that

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Speaker 1: job in a couple of hospitals has like forty fifty

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Speaker 1: percent turnover rates because people hate doing that, they shouldn't

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Speaker 1: do it. Those people in that hospital can now do

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Speaker 1: a job where they try and work on recoveries and

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Speaker 1: the hospital gets paid back. So I think you will

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Speaker 1: see like this natural evolution of people out of jobs

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Speaker 1: that they don't like, jobs with load dissatisfaction ratings, jobs

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Speaker 1: that have higher satisfaction rates, that are more productive and

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Speaker 1: grows the economy.

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Speaker 2: That's the history of every.

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Speaker 1: Other technological advancement we see. It's not overnight. That's why

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Speaker 1: I call this an evolution, not a revolution. All right,

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Speaker 1: and all this, by the way, we probably won't have time,

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Speaker 1: but AI I believe is just a stepping stone to

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Speaker 1: the real outcome, which is.

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Speaker 3: Quantum, which is I'm guessing you also think ten to

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Speaker 3: fifteen years off, No sooner, sooner, and how soon.

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Speaker 2: Five years.

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Speaker 1: The strides that is being made in quantum right now

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Speaker 1: are large.

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Speaker 2: I mean there's a lot more.

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Speaker 1: Look again, there's a lot of private capital going in

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Speaker 1: to quantum.

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Speaker 2: It's big companies and small companies. It's all about error correction.

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Speaker 1: How quickly you can correct their how quickly you can

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Speaker 1: compute the data is pretty interesting right now. So you know,

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Speaker 1: as we have evolve, you know, everything in this technology

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Speaker 1: space is speeding up.

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Speaker 2: So I'll you know, five years all right, fascinating.

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Speaker 3: I wanted to ask about regulation because you mentioned AI

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Speaker 3: is going to help us create better AI, and I

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Speaker 3: wonder what else AI is.

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Speaker 2: Going to decide to do for us. Do we need

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Speaker 2: to regulate it?

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Speaker 3: Do we need to have a heavy hand here, or

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Speaker 3: is it okay to have a few South African billionaires

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Speaker 3: just say like, go, I don't.

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Speaker 2: Know, I don't know what you're talking about.

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Speaker 1: I'll go to chat EPT and ask that what that is.

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Speaker 2: So look, the answer is yes.

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Speaker 1: And I sit on a couple of these big regulatory

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Speaker 1: boards around the world, so I'll do this relatively quickly.

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Speaker 1: If it's a regulated activity today, if you use a

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Speaker 1: it should be a regulated activity using AI. So an

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Speaker 1: example of that is if I'm going to call my

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Speaker 1: doctor and tell my doctor that I got a scratchy throat,

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Speaker 1: a headache, a swelling of this d D and he

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Speaker 1: or she puts it in a machine and they're going

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Speaker 1: to diagnose my disease and prescribe me medicine. That's a

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Speaker 1: highly regulated business today. The AI machine that does that

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Speaker 1: and the program that does should be highly regulated. On

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Speaker 1: the flip side, if when I'm working out tomorrow morning,

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Speaker 1: Spotify chooses a bad song for me and I have

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Speaker 1: to hit skip, which they do a lot, you.

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Speaker 2: Know, I'm not sure that needs to be a regulated activity.

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Speaker 1: I can get through my workout in the morning listening

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Speaker 1: to one bad song and skipping a bad song. So

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Speaker 1: my view is, Look, we've decided what businesses are regulated.

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Speaker 1: We know what business should not be regulated. We should

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Speaker 1: not use AI as a wedge to regulate more businesses.

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Speaker 1: On the flip side, we shouldn't use AI to take

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Speaker 1: businesses today that deserve regulation and take them out of regulation.

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Speaker 3: All right, Well, we have one audience question here on tariffs.

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Speaker 2: It's amazing how you got an other question on your card.

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Speaker 3: Does this strategy make sense to you as a way

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Speaker 3: to deter illegal immigration and fent and all smuggling from Canada? No, Seriously,

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Speaker 3: we've seen for a couple of days in a row,

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Speaker 3: markets really come down. We've seen rates come down from

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Speaker 3: four eighty to four twenty. President Trump has put twenty

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Speaker 3: five percent tariffs on our two closest trading partners after

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Speaker 3: he negotiated possibly the greatest trade deal in history with them, right, yeah, yes,

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Speaker 3: does it make sense to you?

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Speaker 2: I mean you were there.

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Speaker 1: So look, I think what we have to understand and

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Speaker 1: I'm not saying I understand this, but I think what

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Speaker 1: we have to try and figure out is what.

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Speaker 2: Is the objective of the tariffs?

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Speaker 1: And that, to me, I think that's where we're a

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Speaker 1: lot of us are struggling. Is the tariffs meant to

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Speaker 1: make foreign products more expensive so the domestic product is cheaper.

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Speaker 2: Okay, that's a good use of a tariff.

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Speaker 1: The problem is a lot of the things that we

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Speaker 1: import we actually don't make here in the United States.

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Speaker 1: So you can make the foreign product more expensive, it

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Speaker 1: doesn't make the domestic product cheaper because we don't have it.

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Speaker 1: So that's a question I have is the But if

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Speaker 1: we put that tariff on, does that incentivize CAPEX to

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Speaker 1: build the capability to make that product? You could argue, Okay,

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Speaker 1: that's not a bad long term outcome, because look, if

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Speaker 1: we took nothing else away from COVID, we should have

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Speaker 1: taken away from COVID. There are some strategic needs of

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Speaker 1: this country that we are just too dependent on the

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Speaker 1: rest of the world for and we better figure out

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Speaker 1: how to be more self sufficient on necessity goods and items.

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Speaker 2: So you know, when.

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Speaker 1: It's coming to buying personal protective equipment, we have to

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Speaker 1: import it from China. We need sterile gloves and masks

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Speaker 1: from China. Probably something we should manufacture in the United States.

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Speaker 1: If we're worried about you know, toys and games, we

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Speaker 1: can live it at them. I don't think it's a

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Speaker 1: necessity items. So I think we have to figure out

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Speaker 1: what the objective is. If the objective is to raise

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Speaker 1: money for the government, like make that the objective, I'm

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Speaker 1: not sure how you do that, and how do you

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Speaker 1: do that with weighing the effects of inflation on the

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Speaker 1: other side, And I think you have to think about that.

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Speaker 1: So if we're just using it as a revenue raiser,

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Speaker 1: it creates a higher cost of goods. It's a really

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Speaker 1: regressive way to raise revenue.

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Speaker 2: Because I hate to say it, like it's the.

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Speaker 1: Reality of it is, poor people or less highly paid

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Speaker 1: people consume one hundred percent of their paychecks. Wealthier people

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Speaker 1: consume a very small percentage of their paychecks, and they

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Speaker 1: have to consume their paychecks. So the things that are

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Speaker 1: being tariffed, they are things that every day people buy

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Speaker 1: and it becomes a really regressive tax system. I don't

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Speaker 1: think we want a regressive tax system. I'm the first

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Speaker 1: guy here that's going to argue for a progressive tax

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Speaker 1: system that be rich people need to pay more in

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Speaker 1: this country. They should pay more, and they do pay

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Speaker 1: more today. I don't think we want to with that

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Speaker 1: around you, terraff. So to me, the question is what

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Speaker 1: is the objective here? Like there may be a real robust,

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Speaker 1: bona fide reason to have terriffs.

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Speaker 2: I just don't know what we're trying to achieve, and

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Speaker 2: you would know.

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Speaker 3: I feel like anyway there's a thoughtful answer, and I

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Speaker 3: appreciate your time.

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Speaker 2: Thank you, thank you, thanks so much. Possible