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Speaker 1: Welcome to the Bloomberg Surveillance Podcast. I'm Tom Keene with

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Speaker 1: David Gura. Daily we bring you insight from the best

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Speaker 1: of economics, finance, investment, and international relations. Find Bloomberg Surveillance

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Speaker 1: on Apple Podcasts, SoundCloud, Bloomberg dot Com, and of course,

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Speaker 1: on the Bloomberg David, Why don't you bring inter steam

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Speaker 1: guest from the Derivative Money Chris J. Carlos with us.

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Speaker 1: He's the Chairman of the CFTC, the Commodity Futures Trading

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Speaker 1: Commission here in our Bloomberg eleven three studios in New York.

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Speaker 1: And you've been on the job in the commissioner the

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Speaker 1: commission capacity for a couple of years, chairman capacity here

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Speaker 1: for a couple of months. Um, what are your goals here?

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Speaker 1: What do you hope to accomplish you in that role?

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Speaker 1: I think back on the crisis in the aftermath, in

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Speaker 1: the way the mandate of our recognition of the mandate

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Speaker 1: of the TC was expanded so much, and certainly our

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Speaker 1: recognition of how big this marketplace is as well. What

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Speaker 1: he hoped to change, what he hope to do here

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Speaker 1: in this new job. Sure, it's about building a forward

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Speaker 1: looking regulatory agenda. That balance is smart regulation, well tailored

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Speaker 1: regulation against economic growth and and broad based prosperity. How

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Speaker 1: do we get the balance right between regulating these vitally

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Speaker 1: important markets and doing it in a way that doesn't

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Speaker 1: impede strong and broad based economic growth in this country?

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Speaker 1: I said, the marketplace is big? How big is it?

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Speaker 1: And how global is this marketplace? In terms of your purview?

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Speaker 1: What is it? Sure? So so there's a lot of

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Speaker 1: different measures of the size of the of the over

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Speaker 1: the counter derivative market. Is that just did some numbers

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Speaker 1: that bring it out about four hundred and eighty trillions

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Speaker 1: before what's called compression where they net down those positions.

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Speaker 1: But it's a very, very large market, but it's it's

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Speaker 1: a market that has a vital economic purpose. The reason,

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Speaker 1: as I've said before, the reason why Americans enjoy as

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Speaker 1: the standard home ownership tool a thirty year fixed rate

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Speaker 1: mortgage is because these deep and liquid interest rate swamps

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Speaker 1: markets enable banks to provide, on one hand, a stable

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Speaker 1: rate over thirty year life. Why they hedge that risk

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Speaker 1: in these big markets? Great, then why do we get

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Speaker 1: in this trouble? Chairman Greenspan agrees with you years ago

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Speaker 1: he said, derivatives are constructive force with an American finance

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Speaker 1: Chris Whalen agrees with that as well. Then why do

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Speaker 1: we get in trouble? We're get in trouble because some

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Speaker 1: people I, E. A, I G. Back in the day

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Speaker 1: misuse these products that really were incompetent at hedging their risk.

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Speaker 1: But other other players in the market adequately hedge their

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Speaker 1: risks and were use these products safely. So there you know,

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Speaker 1: um failed usage of these products and cost trouble as

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Speaker 1: they can in any other marketplace. What makes these products

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Speaker 1: different is the amount of leverage. Every time you deal

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Speaker 1: with leverage and the degree of incompetence utilization, you can

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Speaker 1: have risks. Twice you said the key word can we

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Speaker 1: regulate dumb? Can we regulate income? Pence? I wish we

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Speaker 1: always could, hope springs eternal. We will continue to try

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Speaker 1: to do so. But that's why you have regulators. Mentioned

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Speaker 1: we have regulators. How big is your staff? Is it

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Speaker 1: big enough? There's been the constant complaint from you in

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Speaker 1: the SEC about the UH. Do you feel like the

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Speaker 1: budget is big enough to do all that you've been

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Speaker 1: charged with doing? Well? We we have asked for a

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Speaker 1: twelve percent increase in our budget this year, and we

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Speaker 1: need that that additional funds for three areas. One is

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Speaker 1: we need more examiners, which gets to this question of them, um,

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Speaker 1: we need more economists. Um. Once upon a time, the

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Speaker 1: CFTC was known as an econometrically based agency. Unfortunately recent

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Speaker 1: years we've become very much of a legal and a

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Speaker 1: lawyer based agency. We want to get some of that

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Speaker 1: balance back with strong economists economists, and then thirdly, we

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Speaker 1: really want to be very forward looking technologically, which is

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Speaker 1: why we've launched LAMB CFTC. It's Friday. We do gossip

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Speaker 1: on Friday. What was it like being the only guy

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Speaker 1: in the planet appointed by President Obama? Did you get

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Speaker 1: a phone call from President Trump saying, hey, I like you,

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Speaker 1: let's keep this thing going. Well, how did that happen? So,

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Speaker 1: so you know I was appointed by President Obama because

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Speaker 1: I was, I guess, one of the few Republicans to say, look,

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Speaker 1: whatever you might think about Dodd Frank, Title seven works.

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Speaker 1: The Congress got Title seven right. The CFDC may have

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Speaker 1: gotten some of the implementation wrong, and it's rushed to

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Speaker 1: roll out the rules, but Title seven works. Did you

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Speaker 1: have like breakfast with Steve Bannon or you know, how

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Speaker 1: did this happen? I think the Trump administration looked at

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Speaker 1: the things I was saying, looked at my approach and said, yeah,

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Speaker 1: this we we agree Title seven does work, but we

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Speaker 1: do need it implemented correctly. And Jihan Carlos the guy

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Speaker 1: to do it. Are they training in two be secretary stage?

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Speaker 1: Is that where this is going? Let's see what we

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Speaker 1: can do right here at anything else? Very quickly, just

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Speaker 1: remembering my DC geography, you're about a mile and a

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Speaker 1: half away from the SEC. When you look at the

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Speaker 1: regulatory landscape. How well are you working with the other

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Speaker 1: regulatory agency? Is that's still something that needs to change.

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Speaker 1: We're working very well with j. Clayton and the SEC.

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Speaker 1: He and I speak probably weekly. We have a chairman

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Speaker 1: to chairman working group that is looking at were a

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Speaker 1: laundry list of outstanding issues that haven't been resolved for

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Speaker 1: a long time. UM with the FED. UH Governor Powell

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Speaker 1: and I speak probably weekly as well. We just had

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Speaker 1: a staff to staff meeting going through a range of issues. UH.

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Speaker 1: The Treasury Secretary Steve Manuchin Um has been doing a

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Speaker 1: great job with his recent reports and we're working very

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Speaker 1: well together as well, Chairman, thank you so much. We

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Speaker 1: want you to leave happy. So on a Friday for

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Speaker 1: Christian Carlo, we want them happy. What else but deliverance

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Speaker 1: and banjo music. All right, we're gonna talk with Ken

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Speaker 1: Senter here. We're waiting for Garrett Cone. He's gonna be

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Speaker 1: speaking to our colleagues on Blomberg Day. BAK America will

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Speaker 1: carry that conversation as soon as it begins, So we're

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Speaker 1: gonna begin here as we wait for that. With Ken Center.

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Speaker 1: He's a senior analyst at Wells Fargo Securities and he's

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Speaker 1: embarked on a fascinating project here. We talk a lot

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Speaker 1: about AI and the role that a I may play

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Speaker 1: in changing research. Of course, there's plenty of regulatory stuff

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Speaker 1: changing the research field as well. Kind of let's just

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Speaker 1: start with what you've done here. This is kind of

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Speaker 1: a more than a thought experiment. You brought in some

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Speaker 1: AI to to help her see what what a I

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Speaker 1: could do to research? What did you learn? I brought

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Speaker 1: out some brought in some the big guns beyond my air,

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Speaker 1: beyond what I what I can do. But um I

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Speaker 1: worked with a data scientist Brian Healy from UH formerly

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Speaker 1: of of Amazon who worked on the Alexa, and uh,

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Speaker 1: what we what we try to do is just figure out, well,

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Speaker 1: where are we in this cycle? Is a kind of

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Speaker 1: late stage, is it? Is it in the beginning and

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Speaker 1: think through, UM, well, how accessible are these tools that

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Speaker 1: are coming to market? And you know, and what does

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Speaker 1: that mean? And you know, could we take a role

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Speaker 1: like even you know, an equity research annelist and start

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Speaker 1: to find ways to you know, improve on it. And

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Speaker 1: so it was really done more as an experiment, almost

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Speaker 1: more of an exercise, just to see, you know, if

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Speaker 1: we could do it, how far we could get. But

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Speaker 1: he had confidence all the way along. I was pretty skeptical,

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Speaker 1: what can this thing do? So how do I produce

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Speaker 1: this ERA? ERA? Well, it's it's the artificially Intelligent Equity

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Speaker 1: Research anelist, right And so what it does is um

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Speaker 1: it can predict stocks UM right now, it's predicting stocks

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Speaker 1: a day out and a week out UM. And it

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Speaker 1: can it can consume a tremendous amount of information. And

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Speaker 1: I think that for us UM this is very different

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Speaker 1: than a robo advisor. It's not programmed. Um ERA learns

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Speaker 1: based on the data collection and the data that she

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Speaker 1: collects would be around forty articles per day that she

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Speaker 1: can crunch down to what's most important in terms of

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Speaker 1: the movement within the stock price right based on her training.

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Speaker 1: And so for me as as an ech RE research annelist,

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Speaker 1: the important thing is to be able to train, you know,

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Speaker 1: to learn first. You know, what are you know, what

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Speaker 1: is in neural network? Right? Why is it so important?

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Speaker 1: What is it doing for the companies that I'm covering

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Speaker 1: within Internet? And why is is? Why are we seeing

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Speaker 1: all of these applications of artificial intelligence so suddenly? Right?

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Speaker 1: So for me it was really an exercise and trying

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Speaker 1: to learn the tools that are coming to market, how

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Speaker 1: accessible they are, and how good they perform? Right, And

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Speaker 1: and it really did shape and I think highlight, um

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Speaker 1: many of the more important fundamental themes as then we

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Speaker 1: kind of work through and we did a big launch

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Speaker 1: piece on on on on all the Internet names UM

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Speaker 1: within our space, and it really framed for us UM

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Speaker 1: the pace of adoption around these tools and not only UM,

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Speaker 1: not only that but the sense of both UM. You know,

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Speaker 1: what do law of large numbers mean for some of

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Speaker 1: these Internet guys as you introduce this technology and how

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Speaker 1: scalable are their business models as you introduce this technology too.

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Speaker 1: Because the the interesting thing for a project like Era

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Speaker 1: is that again, we're not programming her right she she

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Speaker 1: she programs herself based on the data that she's collecting.

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Speaker 1: So it sets off a trend. I think that you know,

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Speaker 1: no data is enough right to the extent that everyone

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Speaker 1: can capture. You know, companies can capture data. These neural

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Speaker 1: nets can run over that data and find patterns and find,

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Speaker 1: you know, to draw conclusions that weren't possible before. And

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Speaker 1: so that is what I'm really trying to explain. So

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Speaker 1: you and Mike Mayo are on the Wells Fargo stage coach.

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Speaker 1: It's like the movie stage coach, you see, like throwing

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Speaker 1: you off the stage coach or you throwing him off

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Speaker 1: last night? You good, you're on speaking terms. That's a

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Speaker 1: surveillance bre exclusive the inflatable stagecoach toy that he sent. Yes,

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Speaker 1: but what do you do with traditional research? What's Mike

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Speaker 1: Mayo's job if you are prescient enough to look forward

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Speaker 1: any number of days, weeks, or quarters. Well, I think

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Speaker 1: what caught his attention most last night when we were talking,

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Speaker 1: as I said, Well, I said, you could look at

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Speaker 1: the research reports that Era is writing right now where

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Speaker 1: she breaks down her you know, her predictions and provides

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Speaker 1: the source articles and writes about the information that she's

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Speaker 1: collecting and say, well, it's you know, she's probably at

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Speaker 1: the level of a junior associate analyst, right, And I think,

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Speaker 1: you know, I think lights went off for him when

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Speaker 1: when when I explained to him that way. But what

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Speaker 1: we're doing for our clients is we're showing them her output, right,

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Speaker 1: and we're trying to help our clients get involved with

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Speaker 1: her progression and get clients more comfortable with some of

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Speaker 1: these terms so that we're not categorizing everything under artificial intelligence.

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Speaker 1: Their specific frameworks that are coming to market, they're driving

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Speaker 1: specific applications and services that companies are adopting, and we

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Speaker 1: just feel that his technology becomes so important to this

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Speaker 1: overall space. The people understand what I mean to interrupt,

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Speaker 1: but her her research. Okay, Sally Crawchuk was once a

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Speaker 1: junior analyst. How does Sally Crawchuk becomes Sally Crawchuk. It's

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Speaker 1: Sanford Bernstein. If she starts out as a black box, well,

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Speaker 1: see she's not seeing this, She's are is not a

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Speaker 1: black box. If we took I would say ERA is

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Speaker 1: less than you know, one of the things that one

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Speaker 1: of the part, the part, the important part about explaining

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Speaker 1: error to our our investors is explaining that UM neural

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Speaker 1: networks are not all that complicated on the at their basics, right,

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Speaker 1: you're talking about compute nodes and connections per node. Um

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Speaker 1: ERA is less than a hundred notes. Google's neural networks

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Speaker 1: go up to the hundreds and billions of nodes. We understand, right, UM,

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Speaker 1: they become much more black box than what we're doing.

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Speaker 1: What we're doing is we're just showing how machine learning

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Speaker 1: could draw better, you know, can can pull in more information,

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Speaker 1: draw conclusions, maybe eliminate bias, and and so what We're

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Speaker 1: not trying to necessarily become opaque. We're trying to be

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Speaker 1: actually very transparent I think with with clients as far

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Speaker 1: as how the technology works. I mean, if I take

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Speaker 1: somebody legend or like ls BB Longley, who was just

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Speaker 1: genormous in household products and doing researcher, I believe ms Longley,

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Speaker 1: uh did By hold cell right? Does air do By?

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Speaker 1: Hold Sarah does? But Eric, what's the BA test? Well,

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Speaker 1: I would say that for the hour slots right now,

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Speaker 1: she's doing over um for the the weak slots, she's

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Speaker 1: doing better than fifty. So the folks, that's like Aaron

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Speaker 1: Judge and he doesn't strike out. But the reason but

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Speaker 1: but what's important about this is that Area is constantly learning.

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Speaker 1: She's learning long the come on, I mean, what do

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Speaker 1: you mean constantly it's iterative? Well, no, so she as

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Speaker 1: long as she's collecting more data. The she's training, she's

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Speaker 1: training on basically on every result. It trains how she

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Speaker 1: waits the inputs right. So the more results that she

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Speaker 1: can see better, she can understand that inputs that come

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Speaker 1: in a couple of minutes left her. Kenston has been

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Speaker 1: patient here with Wills far Bec. As we look at

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Speaker 1: the research wherever revolution. You know, you've you've got great

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Speaker 1: experience on the Internet and the technology stocks. The cf

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Speaker 1: A Institute doesn't do much with technology. What does the

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Speaker 1: cf A Institute needs to know from Kencenta about how

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Speaker 1: technology folds into Graham, Dot and Coddle. Well, I think

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Speaker 1: how decisions get made. Ken you've done this before. The

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Speaker 1: mic is over here first, Sorry, um gosh, I mean

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Speaker 1: I think that we're all going to have to have

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Speaker 1: a We're all going to have to have a deeper

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Speaker 1: appreciation I think for what this technology means. I think

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Speaker 1: it's going to impact every platform. I think it will

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Speaker 1: impact every industry. I think it will impact every geography.

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Speaker 1: And you know, for me, as I work on this research,

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Speaker 1: it's really much bigger about what I'm doing an equity

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Speaker 1: research and takes it takes a bias. It's, well, it's

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Speaker 1: bigger than equity research. It's bigger. It's what can this

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Speaker 1: technology do now that it couldn't do three or four

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Speaker 1: years ago? And and how does that flow through businesses

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Speaker 1: and industries and ultimately governments. And I think that there

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Speaker 1: are important questions that come out of this research. And

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Speaker 1: for me it was you know, I write it and

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Speaker 1: I think that, you know, I'm writing about the world

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Speaker 1: now changing, and I believe it, and I would really,

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Speaker 1: I would stake my new career on it, and I

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Speaker 1: think it's so important. We're gonna get you and your

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Speaker 1: colleague pre uh salary back on the internet stocks as well,

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Speaker 1: Ken Senta with Wells Fargo. Yes, we'll do bio so

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Speaker 1: and the Fangs and the others with Kenn Senta, but

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Speaker 1: right now on air and the idea of what we

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Speaker 1: can do with artificial intelligence, bringing in all the news

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Speaker 1: flow and trying to look a little bit for Mr

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Speaker 1: Senna is with Wells Fargo. This is an important interview,

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Speaker 1: David with Gary Cone. Yeah, you know, you look at

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Speaker 1: who's in front here. I've noticed listening to the President

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Speaker 1: talk about tax for him, he is wrapping himself in

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Speaker 1: the proposal that was released Wednesday. We've talked leading up

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Speaker 1: to it about the Big Six and the role of

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Speaker 1: Big six was playing the president six is not the

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Speaker 1: accounting firm's guest skies and Oxford suits. Uh, let's go

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Speaker 1: down to David Westerner, colleague anchor of Bloomberg day Break America.

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Speaker 1: He's sitting down with the head of the NBC, that

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Speaker 1: is form Gary Cone, formerly of Goldman's Tax from the

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Speaker 1: White House. Welcome back to Bloomberg, Garty, Good to have

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Speaker 1: you here. So there's a lot, As I said, you're

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Speaker 1: at the center of this. Now. There's a lot of

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Speaker 1: talk about this new plan, a lot of things to discuss,

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Speaker 1: but one question has been really what are the effects

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Speaker 1: on the national economy, And specifically we're being told that

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Speaker 1: we could pay for these tax cuts because of growth.

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Speaker 1: At the same time, we both know you're not gonna

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Speaker 1: pay on day one. How far in the red will

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Speaker 1: we go before we turn cash positive on this plan? David,

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Speaker 1: first of all, thank you very much for having me here,

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Speaker 1: and thanks for the great question. Remember, when we're talking

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Speaker 1: about taxes, everyone is using a ten year number. So

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Speaker 1: all the numbers that are being talked about, whether they're

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Speaker 1: the deficit or they're the pay for is they're being

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Speaker 1: scheduled on a ten year number. So when we talk

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Speaker 1: about economic growth, and we talked about growing g d P,

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Speaker 1: which we believe our tax plan will will do, we

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Speaker 1: talk about creating economic growth over a ten year cycle.

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Speaker 1: If you can grow the GDP by one percent, which

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Speaker 1: we're confident our tax plan can, we can pay down

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Speaker 1: three trillion dollars of the deficit by one percent change

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Speaker 1: in US d D g DP over a ten year number.

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Speaker 1: So we're very excited about our tax plan. We think

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Speaker 1: it's very stimulant for the U. S economy by bringing

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Speaker 1: back American companies to America, having them produced products back

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Speaker 1: in America, and hiring American workers. So so fair enough, Gary,

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Speaker 1: and I know that this is what the President has

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Speaker 1: sought to do, which you are trying to do you're

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Speaker 1: trying to grow the economy, all things that we all want.

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Speaker 1: At the same time, when you ever have a startup company,

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Speaker 1: you have to invest at the beginning. You go into

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Speaker 1: the red, as you know, so well before you turn

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Speaker 1: cash positive. So when would this turn cash positive for

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Speaker 1: the U. S. Government And how far in the red

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Speaker 1: would be go before we come back to positive. They

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Speaker 1: we understand that, we understand the investment psycho. We're acutely

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Speaker 1: attuned to that. If you look at some of the

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Speaker 1: provisions that we've put into the tax code, we're absolutely

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Speaker 1: encouraging front end investment. We've allowed companies for the first

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Speaker 1: five years in our tax plan to invest and take

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Speaker 1: a hundred percent right off for any capital investment they

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Speaker 1: make for the first five years. That's exactly what we want.

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Speaker 1: We want people to invest capital up front so they

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Speaker 1: can hire people, they can build plants, they can build equipment,

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Speaker 1: they can hire people, and we get paid back over

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Speaker 1: a long term, not just over the ten years, but

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Speaker 1: well beyond the ten years. I can't sit here and say, hey,

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Speaker 1: it's day sixty five, it's day three sixty five, it's

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Speaker 1: year five. The quicker we get going, the quicker we

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Speaker 1: get the tax plan implemented, the quicker we get get

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Speaker 1: a surety of where we are in the economy, the

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Speaker 1: quicker we're gonna get the tax plan implemented, and the

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Speaker 1: quicker we're gonna get returned on our investment. You know that,

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Speaker 1: I know that that's the way companies look at this.

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Speaker 1: They look at return on capital and they look at

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Speaker 1: it as quickly as possible. Totally right, totally right. At

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Speaker 1: the same time, you must agree, I assume Gary, that

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Speaker 1: this would actually hurt revenues to the U. S. Government

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Speaker 1: in the short term. I mean, even to the Reagan

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Speaker 1: tax plan, they lost over two hundred billion dollars in

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Speaker 1: revenues over the first four years. David, Look, we are

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Speaker 1: putting incentives in place for companies to spend money in

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Speaker 1: the United States, to bring jobs back to the United States,

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Speaker 1: to bring businesses back to the United States, and we're

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Speaker 1: allowing them to expense to that front. Yes, this is

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Speaker 1: going to cost some money in the beginning, but like

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Speaker 1: every company that's ever been built in this in this country,

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Speaker 1: you invest upfront to create greater returns over the long term.

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Speaker 1: We as a country have to make an investment in

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Speaker 1: this country. We have to invest in ourselves. We're creating

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Speaker 1: a tax plan that it encourages you to invest in

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Speaker 1: this country and invest in the future of our country.

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Speaker 1: So let's talk about how you're gonna get this done,

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Speaker 1: how you're get this through the Congress. One of the

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Speaker 1: things that you're you're proposing is really curtailing the deductions

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Speaker 1: for state and local taxes in order to pay for

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Speaker 1: some of these tax cuts, so we don't go too

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Speaker 1: far in to the red. Already, we have Mr. Roska,

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Speaker 1: who is responsible tax policies in the subcommittee, the House

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Speaker 1: Ways of Insommittee, saying, you know what, we're gonna have

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Speaker 1: to make some accommodations for some of the members who

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Speaker 1: come from high tech states. By the way, Mr Roskins

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Speaker 1: included in that, because his own district takes something like

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Speaker 1: three and a half billion dollars in deductions. Are you

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Speaker 1: gonna have to modify that. You've seen our blueprint, You've

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Speaker 1: seen our plan. Our plan at this point does not

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Speaker 1: allow for deductions of state and local taxes. We set

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Speaker 1: out to achieve a couple main goals. Number one was

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Speaker 1: lower rates for everyone. Number two was simplification. By creating simplification,

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Speaker 1: we were trying to get rid of all of the

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Speaker 1: loopholes and all of the deductions that mostly wealthy people use. Remember,

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Speaker 1: only twenty five percent of families in America used the

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Speaker 1: itemized deductions. So when he's talking about that deduction, he's

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Speaker 1: talking about twenty five percent of families in America. He's

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Speaker 1: talking about the wealthier twenty five percent of the families

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Speaker 1: that use that deduction. We spend enormous amount of time

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Speaker 1: talking about that. When you lower the rate but get

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Speaker 1: rid of that deduction, for many American taxpayers, they actually

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Speaker 1: end up in a at our place. Is that a

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Speaker 1: red line for you? It's not a red line for us.

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Speaker 1: That is not a red line. We've told you we're

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Speaker 1: our red lines are are red lines are is the

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Speaker 1: business tax rate, both on the pass through entities and

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Speaker 1: the corporatedies cannot go higher than it is in our

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Speaker 1: initial proposal, and that there has to be a tax

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Speaker 1: cut for hard working, middle income Americans. We are willing

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Speaker 1: to work with the tax writers on the other dials

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Speaker 1: that we have in the system. So, Garrett, one of

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Speaker 1: the things you're proposing is simplifying the way we file

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Speaker 1: tax records. And we've heard about the postcard. It was

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Speaker 1: not postcard. Maybe one page give us a sense of

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Speaker 1: so we like, we like postcard, postcard. Let's stick with postcard.

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Speaker 1: In order to get that postcard done, you're gonna have

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Speaker 1: to eliminate a lot of deduction. Some you said you

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Speaker 1: will not touch. What are some of the other ones

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Speaker 1: that will be eliminated? For example, deduction for real estate taxes.

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Speaker 1: That's actually a fair amount of money over five years.

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Speaker 1: As you look at it's like something like over a

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Speaker 1: hundred billion dollars. Is that going to go away? But

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Speaker 1: we're looking to get rid of the itemized the aduction line.

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Speaker 1: There are all different types of unique deductions that have

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Speaker 1: worked their way into the itemized deduction bucket over decades

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Speaker 1: of tax planning. What typically happened, And if you go

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Speaker 1: back and look at the history, and I won't go

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Speaker 1: through the history, I'll I'll spare everyone that pain. Is

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Speaker 1: as we tried to modify taxes year after year after year.

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Speaker 1: If we needed a vote here, they needed a vote here.

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Speaker 1: You know, a certain House member, a certain Senate members

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Speaker 1: said hey, I could use this in my district or

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Speaker 1: my state and you could get my vote. So we

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Speaker 1: created all these one off, little unique deductions to get

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Speaker 1: the final vote we needed. We need to just get

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Speaker 1: rid of them all. Let's just clean them all out,

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Speaker 1: get rid of them all, get rid of that huge

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Speaker 1: bucket of deductions, and really simplify the tax return where

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Speaker 1: you basically take your income, you take your standard deduction,

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Speaker 1: and you end up what and you see your taxible

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Speaker 1: income and you pay your tax based on that. That's

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Speaker 1: really our objective. Simplification postcard easier to do. You don't

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Speaker 1: have to go out and hire a tax prepare you

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Speaker 1: don't have to go out and buy software. You can

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Speaker 1: sit at your kitchen table and prepare your own tax return. Gary,

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Speaker 1: how do you define middle class? Look, we define middle

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Speaker 1: classes sort of the middle sevent fifty percent of Americans,

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Speaker 1: if people earning between sixty and a hundred and sixty

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Speaker 1: thousand dollars. It's not a simple definition in this country.

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Speaker 1: As you know, we have different living standards based on

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Speaker 1: cost of living in all the different states and counties

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Speaker 1: in America. But we've taken a very wide definition of

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Speaker 1: middle class. It's a very final question. We know that

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Speaker 1: roughly ten percent of American tax payers pay about eighty

430
00:22:34,800 --> 00:22:37,199
Speaker 1: percent of the taxes. If this plan goes forward just

431
00:22:37,280 --> 00:22:39,359
Speaker 1: the way you wanted to be. Will that number go

432
00:22:39,520 --> 00:22:44,159
Speaker 1: up or go down? The tem percent? Well, will the

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00:22:44,200 --> 00:22:46,320
Speaker 1: ten percently paying more than any percent of the total

434
00:22:46,359 --> 00:22:49,920
Speaker 1: taxes or less? We think that the teen percent will

435
00:22:49,960 --> 00:22:52,679
Speaker 1: be paying about the same amount of the taxes. We

436
00:22:52,680 --> 00:22:56,040
Speaker 1: think that's great. Thank you so much. Gary, really appreciate it.

437
00:22:56,160 --> 00:23:00,000
Speaker 1: Gary Cone, He's White House National Economic Council Director, David

438
00:23:00,119 --> 00:23:03,440
Speaker 1: Weston uh talking with Gary Kone Alex Steel. I think

439
00:23:03,520 --> 00:23:06,880
Speaker 1: David Gura there with the key question is for all Americans,

440
00:23:06,880 --> 00:23:09,200
Speaker 1: So all of our listeners coast to coast, what is

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00:23:09,280 --> 00:23:14,040
Speaker 1: middle class? I passed through is don't affect the middle class?

442
00:23:14,600 --> 00:23:18,320
Speaker 1: I I don't you know. I mean tax reform of

443
00:23:18,320 --> 00:23:21,600
Speaker 1: eighty six. McKey was brilliant the other day, that nostalgic

444
00:23:21,680 --> 00:23:24,919
Speaker 1: look back at a lengthy process. One of the biggest

445
00:23:24,920 --> 00:23:27,560
Speaker 1: mistakes I made out of college was trying to memorize

446
00:23:27,800 --> 00:23:31,960
Speaker 1: the Tax Reform Act of nineteen seventies. I could only

447
00:23:32,040 --> 00:23:37,040
Speaker 1: shave like three days a week, and I figured out

448
00:23:37,040 --> 00:23:40,120
Speaker 1: about six months So dumb, I figured out six months later.

449
00:23:40,160 --> 00:23:42,320
Speaker 1: Why did I waste my time doing that? Is there

450
00:23:42,320 --> 00:23:46,160
Speaker 1: a middle class left? Well, that's the point. Mathematically there

451
00:23:46,280 --> 00:23:49,040
Speaker 1: is a well emotionally, it's pretty fragile. And you know,

452
00:23:49,080 --> 00:23:51,920
Speaker 1: I don't fought Mr Cohne for the way he uh

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00:23:52,200 --> 00:23:55,720
Speaker 1: framed mathematically the middle class, but I really wonder where

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00:23:55,720 --> 00:23:58,320
Speaker 1: they are in policy. What I really take issue with, David,

455
00:23:58,359 --> 00:24:00,439
Speaker 1: you're better at this than I am, is do you

456
00:24:00,440 --> 00:24:02,600
Speaker 1: look at one tax or do you look at like

457
00:24:02,640 --> 00:24:07,080
Speaker 1: every household does, which is the sum of your taxes? No,

458
00:24:07,240 --> 00:24:09,280
Speaker 1: I think the latter. I'm I'm with you on that.

459
00:24:09,400 --> 00:24:12,200
Speaker 1: And then it was struck by what Gryne had to say, uh,

460
00:24:12,240 --> 00:24:14,119
Speaker 1: you know, on on on the issue of local taxes.

461
00:24:14,200 --> 00:24:16,480
Speaker 1: You know, they've they've issued their plan. This goes to

462
00:24:16,520 --> 00:24:18,119
Speaker 1: Congress now, and I think it's gonna be interesting to

463
00:24:18,160 --> 00:24:20,359
Speaker 1: talk with the congressman from the twenty three district and

464
00:24:20,400 --> 00:24:22,320
Speaker 1: up states a little bit later about that how they

465
00:24:22,359 --> 00:24:35,000
Speaker 1: take this proposal and start putting some meat on the bone.

466
00:24:38,280 --> 00:24:40,440
Speaker 1: So I'm gonna let David Garrow do this interview because

467
00:24:40,440 --> 00:24:44,520
Speaker 1: he's actually lived in the good congressman's district. All I

468
00:24:44,520 --> 00:24:48,360
Speaker 1: can say, David is this district is the most conservative district.

469
00:24:48,480 --> 00:24:51,560
Speaker 1: And then off to the upper right is the People's

470
00:24:51,600 --> 00:24:54,919
Speaker 1: Republic of Ithaca, and that's all there was. We can

471
00:24:55,000 --> 00:24:58,560
Speaker 1: guess where I live. It's just, you know, the People's

472
00:24:58,600 --> 00:25:01,800
Speaker 1: Republic of Ithaca just really upsets Congressman Read on a

473
00:25:01,880 --> 00:25:04,120
Speaker 1: day to day basis. Yes, I did spend some time

474
00:25:04,119 --> 00:25:07,159
Speaker 1: on East till no problem with that. Very good Congress,

475
00:25:07,480 --> 00:25:08,679
Speaker 1: Thank you very much for taking the time to be

476
00:25:08,680 --> 00:25:09,960
Speaker 1: with us on our phone lines. Do you sit on

477
00:25:10,000 --> 00:25:11,960
Speaker 1: the House Ways and Means Committee. I noted that you

478
00:25:11,960 --> 00:25:13,760
Speaker 1: had an audience with the President a little bit earlier

479
00:25:13,800 --> 00:25:16,480
Speaker 1: this week before we got the framework from the Big Six.

480
00:25:16,680 --> 00:25:18,239
Speaker 1: What did you discuss at the White House? What did

481
00:25:18,240 --> 00:25:20,480
Speaker 1: the President say about how he wants this rollout to

482
00:25:20,480 --> 00:25:24,399
Speaker 1: go forward. Well, he definitely supports what we're trying to do,

483
00:25:24,440 --> 00:25:26,919
Speaker 1: and that that's the lower the tax burden on on

484
00:25:27,000 --> 00:25:30,160
Speaker 1: hard working Americans, that's the middle class in particular, and

485
00:25:30,160 --> 00:25:32,600
Speaker 1: and wants to focus on creating jobs and and that's

486
00:25:32,600 --> 00:25:34,560
Speaker 1: what we're trying to do with the immediate expensing and

487
00:25:34,560 --> 00:25:36,760
Speaker 1: the other provisions when it comes to business activity. So,

488
00:25:37,080 --> 00:25:38,720
Speaker 1: you know, getting into the weeds a little bit, we're

489
00:25:38,920 --> 00:25:40,800
Speaker 1: really trying to target this relief to where we think

490
00:25:40,800 --> 00:25:42,680
Speaker 1: you can have the biggest game for the people back home.

491
00:25:42,920 --> 00:25:45,159
Speaker 1: What's this process going to be like, as you see it,

492
00:25:45,160 --> 00:25:47,439
Speaker 1: we've got nine pages. That's seven pages more than we

493
00:25:47,480 --> 00:25:49,280
Speaker 1: had before from from the White House. But we're gonna

494
00:25:49,320 --> 00:25:50,760
Speaker 1: have to get two hundreds, if not more than a

495
00:25:50,800 --> 00:25:54,199
Speaker 1: thousand pages of legislation dealing with the tax code. Is

496
00:25:54,240 --> 00:25:56,000
Speaker 1: your committee going to be taking the lead on that.

497
00:25:56,040 --> 00:25:58,879
Speaker 1: Who's going to be writing the actual bill? Yeah, we

498
00:25:59,160 --> 00:26:02,160
Speaker 1: will be, obviously, being a tax bill has to originate

499
00:26:02,160 --> 00:26:04,000
Speaker 1: in the House, and it's clear to me that we

500
00:26:04,119 --> 00:26:06,119
Speaker 1: on the Ways Means Committee will be going through that

501
00:26:06,200 --> 00:26:09,840
Speaker 1: process of hearings in regular order to develop that legislation.

502
00:26:09,880 --> 00:26:12,600
Speaker 1: And and and this was done intentionally in the sense

503
00:26:12,640 --> 00:26:14,359
Speaker 1: of we didn't want to come out and say here's

504
00:26:14,359 --> 00:26:15,680
Speaker 1: the bill, let's vote it up or down in the

505
00:26:15,720 --> 00:26:17,440
Speaker 1: middle of the night. We want to have input, we

506
00:26:17,480 --> 00:26:19,639
Speaker 1: want to make your own attended consequences are addressed. And

507
00:26:19,640 --> 00:26:23,159
Speaker 1: that's where we're kicking this off rather quickly. Your district

508
00:26:23,240 --> 00:26:27,360
Speaker 1: sprawls from Lake Erie across just before Binghamton, New York.

509
00:26:27,400 --> 00:26:31,440
Speaker 1: You go up to Hobarton uh William Smith College in Geneva.

510
00:26:31,640 --> 00:26:34,040
Speaker 1: The legacy of House Ways of Means is Barbara B.

511
00:26:34,160 --> 00:26:37,879
Speaker 1: Connable from just outside your district in Rochester, are you

512
00:26:37,880 --> 00:26:41,200
Speaker 1: going to get a process untaxed reform that Barbara b.

513
00:26:41,320 --> 00:26:44,560
Speaker 1: Connable or Frank Horton would be familiar with, or is it,

514
00:26:44,600 --> 00:26:49,440
Speaker 1: as John McCain gonna say, be just a bastardized process. No,

515
00:26:49,640 --> 00:26:51,640
Speaker 1: this is gonna be a sincere process. I mean, we've

516
00:26:51,680 --> 00:26:53,680
Speaker 1: been working on Factory Form for seven years plus on

517
00:26:53,720 --> 00:26:56,080
Speaker 1: the committee that I've been there. So the foundational work

518
00:26:56,119 --> 00:26:58,040
Speaker 1: is there. A lot of what we did, we've been

519
00:26:58,040 --> 00:27:00,200
Speaker 1: debating and discussing, is out there. But we're going to

520
00:27:00,240 --> 00:27:03,120
Speaker 1: go through the legislative process to make this sausage quote

521
00:27:03,160 --> 00:27:05,600
Speaker 1: unquote in an open and honest way and get the

522
00:27:05,600 --> 00:27:08,639
Speaker 1: input from people and get them and hearings and markups

523
00:27:08,680 --> 00:27:11,080
Speaker 1: and the whole process that we should be doing. And

524
00:27:11,080 --> 00:27:13,040
Speaker 1: we're also going to reach I coach of the Problem

525
00:27:13,080 --> 00:27:16,359
Speaker 1: Solvers Caucus, and there are good faith Democratic members that

526
00:27:16,359 --> 00:27:18,240
Speaker 1: work with me in that caucus that want to solve

527
00:27:18,320 --> 00:27:20,640
Speaker 1: this problem of the broken American tax code. And that's

528
00:27:20,640 --> 00:27:22,960
Speaker 1: what we're working on. Find those kind of those kind

529
00:27:22,960 --> 00:27:25,159
Speaker 1: of post cross the aisle that are sincere about you

530
00:27:25,200 --> 00:27:27,200
Speaker 1: know what, let's fix us for the people back home.

531
00:27:27,520 --> 00:27:29,679
Speaker 1: What's your sense of of what the middle classes in

532
00:27:29,720 --> 00:27:31,320
Speaker 1: this country. We had Gary Kuhn on the on the

533
00:27:31,320 --> 00:27:33,919
Speaker 1: show a little earlier. I talked about a pretty wide

534
00:27:34,040 --> 00:27:36,760
Speaker 1: range of incomes that would would qualify for that. Do

535
00:27:36,840 --> 00:27:38,520
Speaker 1: you does the committee have a sense of of what

536
00:27:38,560 --> 00:27:41,120
Speaker 1: you're going for when you talk about the middle class? Well,

537
00:27:41,119 --> 00:27:42,639
Speaker 1: I can tell you what I look at I mean,

538
00:27:42,640 --> 00:27:44,960
Speaker 1: obviously I look at it from the first perspective of

539
00:27:45,080 --> 00:27:47,080
Speaker 1: our district. And so when you've got a family of

540
00:27:47,119 --> 00:27:50,760
Speaker 1: four from the fifty thousand and seventy uh folks, that

541
00:27:51,119 --> 00:27:53,320
Speaker 1: to me is a lot of hard working Americans that

542
00:27:53,359 --> 00:27:55,680
Speaker 1: are working paycheck to paycheck that are struggling. Then when

543
00:27:55,680 --> 00:27:57,639
Speaker 1: you get down towards the city, you know, obviously you

544
00:27:57,680 --> 00:27:59,680
Speaker 1: get a firefighter and teacher. They're making anywhere from a

545
00:27:59,760 --> 00:28:02,480
Speaker 1: hunter fifty to two and a quarter a year. That

546
00:28:02,480 --> 00:28:04,840
Speaker 1: that that relief that they need is something that I

547
00:28:04,920 --> 00:28:07,320
Speaker 1: know here in their voices that we want to bring

548
00:28:07,359 --> 00:28:09,600
Speaker 1: to the table because they're they're living paycheck to paycheck,

549
00:28:09,600 --> 00:28:11,720
Speaker 1: and when they go to Salter Collegeship, for example, phill

550
00:28:11,760 --> 00:28:14,240
Speaker 1: a tuition assistance for college, they're told, you make too

551
00:28:14,280 --> 00:28:16,440
Speaker 1: much money. We got a reward and went work, not

552
00:28:16,560 --> 00:28:19,359
Speaker 1: penalized it. Some would say that Tom read is the

553
00:28:19,480 --> 00:28:23,359
Speaker 1: last centriest standing at least of the few Republicans in

554
00:28:23,400 --> 00:28:25,199
Speaker 1: the Northeast. I don't know if that's true or not.

555
00:28:25,359 --> 00:28:29,840
Speaker 1: But how is your district and their support of the president.

556
00:28:29,920 --> 00:28:33,360
Speaker 1: How has it changed from Jamestown to Geneva, to Ithaca

557
00:28:33,359 --> 00:28:37,280
Speaker 1: and Elmira in the sense that President Obama his first

558
00:28:37,359 --> 00:28:40,920
Speaker 1: vote did nicely, second vote barely made it, and then

559
00:28:40,960 --> 00:28:45,440
Speaker 1: President Trump won resoundingly against Secretary Clinton. How is the

560
00:28:45,440 --> 00:28:50,360
Speaker 1: Trump support changed over the first two some days. Well,

561
00:28:50,400 --> 00:28:52,600
Speaker 1: I will tell you, as you look at the votelity

562
00:28:52,640 --> 00:28:55,200
Speaker 1: from those prior elections in the district, we've always still

563
00:28:55,200 --> 00:28:58,040
Speaker 1: said right and then the presidential candidate. I don't think

564
00:28:58,080 --> 00:29:01,160
Speaker 1: Obama lost it by I think that the numbers are

565
00:29:01,200 --> 00:29:03,440
Speaker 1: right by a couple of half a point or something

566
00:29:03,440 --> 00:29:06,360
Speaker 1: like that. But anyway, the point is is, I think

567
00:29:06,360 --> 00:29:08,200
Speaker 1: what you see representing in our district is what you

568
00:29:08,240 --> 00:29:10,600
Speaker 1: see kind of with the Trump based the Trump voters.

569
00:29:10,680 --> 00:29:12,640
Speaker 1: You know, one of one of the reasons I supported

570
00:29:12,680 --> 00:29:14,920
Speaker 1: the president early on as one of the first early

571
00:29:15,360 --> 00:29:17,600
Speaker 1: dorsers of him on the Republican side, was I heard

572
00:29:17,600 --> 00:29:20,640
Speaker 1: from our people that he was tapping into an energy

573
00:29:20,800 --> 00:29:22,440
Speaker 1: that they were just sick and tired of the status

574
00:29:22,480 --> 00:29:24,920
Speaker 1: quo in Washington. So in that base is still there,

575
00:29:24,960 --> 00:29:27,400
Speaker 1: that support is still there. But we are we are

576
00:29:27,440 --> 00:29:29,800
Speaker 1: blessed with a diverse district, There's no doubt about it.

577
00:29:30,080 --> 00:29:32,600
Speaker 1: And I will take our message and have the conversation

578
00:29:32,640 --> 00:29:35,520
Speaker 1: with anybody in downtown Ithaca, as we've done town halls there,

579
00:29:35,960 --> 00:29:38,200
Speaker 1: to the hinterlands of the western side of the district

580
00:29:38,200 --> 00:29:41,000
Speaker 1: and in hard Right Territory too. If we were to

581
00:29:41,040 --> 00:29:44,000
Speaker 1: have a Pines Burger Glenwood Pines, one of my favorite

582
00:29:44,000 --> 00:29:45,880
Speaker 1: things to get there on Cayuga Lake, and I were

583
00:29:45,880 --> 00:29:47,320
Speaker 1: to ask you, what what do you say to folks

584
00:29:47,400 --> 00:29:49,560
Speaker 1: who who are of the middle class, who worried that

585
00:29:49,600 --> 00:29:53,320
Speaker 1: this thing is too geared toward upper class Americans. I say,

586
00:29:53,360 --> 00:29:56,200
Speaker 1: stay tuned, because there's a lot of misinformation already be

587
00:29:56,200 --> 00:29:58,840
Speaker 1: impeddled out there. And I tell you, I'm committed to

588
00:29:58,960 --> 00:30:01,280
Speaker 1: relieve in the tax burnt on those middle class, hard

589
00:30:01,280 --> 00:30:04,800
Speaker 1: working people. And I'll tell people across the spectrum that

590
00:30:04,840 --> 00:30:08,000
Speaker 1: I'm working with uh they join arms in that and

591
00:30:08,000 --> 00:30:09,720
Speaker 1: that that's where the relief needs to be. The top

592
00:30:09,720 --> 00:30:11,880
Speaker 1: one person I was there when the President said to

593
00:30:11,920 --> 00:30:14,360
Speaker 1: the national press for the first time top one per cent,

594
00:30:14,600 --> 00:30:16,720
Speaker 1: and the folks there, we're not looking to help them

595
00:30:16,720 --> 00:30:18,280
Speaker 1: out where they may even have to pay a little

596
00:30:18,320 --> 00:30:20,360
Speaker 1: bit more. At the end of the day, I think

597
00:30:20,400 --> 00:30:22,640
Speaker 1: that opens up the path to working with folks who

598
00:30:22,760 --> 00:30:24,560
Speaker 1: sincerely want to govern and get it done for the

599
00:30:24,600 --> 00:30:28,920
Speaker 1: American people. Tom, are you, thank you so much, greatly appreciated.

600
00:30:28,960 --> 00:30:32,280
Speaker 1: The Congressional District of New York are really what a

601
00:30:32,440 --> 00:30:36,480
Speaker 1: historic district? I mean, back to the nineteenth century, uh, David,

602
00:30:36,520 --> 00:30:38,200
Speaker 1: I mean, you know, we make jokes about Ithaca and

603
00:30:38,960 --> 00:30:42,880
Speaker 1: all that, but it's a really interesting history and the

604
00:30:43,120 --> 00:30:47,800
Speaker 1: great challenges of the last forty years economically. Tom Read

605
00:30:48,000 --> 00:30:59,800
Speaker 1: of New York, thanks for listening to the Bloomberg Surveillance

606
00:30:59,800 --> 00:31:05,680
Speaker 1: PI podcast. Subscribe and listen to interviews on Apple Podcasts, SoundCloud,

607
00:31:06,040 --> 00:31:09,880
Speaker 1: or whichever podcast platform you prefer. I'm on Twitter at

608
00:31:09,880 --> 00:31:14,560
Speaker 1: Tom Keene. David Gura is at David Gura. Before the podcast,

609
00:31:14,880 --> 00:31:18,280
Speaker 1: you can always catch us worldwide. I'm Bloomberg Radio