Speaker 1: Bloomberg Audio Studios, podcasts, radio news joining us right now,
Speaker 1: there's the former chair of the CFTC, the former chair
Speaker 1: of the SEC, former banker at Goldman, and now a
Speaker 1: professor lecturing on quite an array of things in the
Speaker 1: world of Wall Street and technology.
Speaker 2: Great to see you, Gary Ginsler.
Speaker 3: Great to be with your Romain. Sorry Katie couldn't be
Speaker 3: with us as well.
Speaker 2: She could not.
Speaker 1: She sends her best and I'm actually going to start
Speaker 1: with a question that she actually raised. And this surrounds
Speaker 1: the SpaceX IPO and the corporate governance structure which we
Speaker 1: learned from the S one and we are waiting an
Speaker 1: updated S one filing later today that'll give us a
Speaker 1: little bit more clarity about what this is going to
Speaker 1: look like. I do wonder, given your past at the SEC,
Speaker 1: given the work you did on Sarbanes Oxley, would this
Speaker 1: IPO have seen the light of day under the previous
Speaker 1: regulatory regime.
Speaker 3: I haven't looked at it that with that in mind,
Speaker 3: But you know, we have a great capital markets in
Speaker 3: the US, and we basically say that at we're merit neutral.
Speaker 3: Whether it's under Democrats or Republicans, it doesn't matter. It's
Speaker 3: merit neutral as long as there is the full disclosure
Speaker 3: about the material risk, and there are a lot of
Speaker 3: material risks when you're trying to offer something to the
Speaker 3: public at around one hundred times revenues. That's not one
Speaker 3: hundred times earnings, it's one hundred times revenues. So it's
Speaker 3: really about whether there's all the material disclosures, and particularly
Speaker 3: around the governance.
Speaker 2: As you said, well, I mean, you.
Speaker 1: Know, in fairness, the disclosures are there. I mean, it's
Speaker 1: a pretty intertwined ecosystem. Maybe elond Musk is interests and properties,
Speaker 1: so fair enough to that. But then when you start
Speaker 1: to go through some of the regulatory sections out there
Speaker 1: with regards to independent auto Committee, the independent pay compensation committees,
Speaker 1: self dealing loans, things like that, there do seem to
Speaker 1: be a lot of issues with regards to what we've
Speaker 1: seen in that S one file and why it's able
Speaker 1: to get to market without those being addressed.
Speaker 3: And many companies have had control shareholders take things public,
Speaker 3: but this is one where it's really even more one
Speaker 3: might say, dominated by that control shareholder, and so anybody
Speaker 3: investing in it has to sort of really take that risk.
Speaker 2: In mind.
Speaker 1: Are we reading too much into this one particular ipo.
Speaker 1: We've had several other IPOs this year that have run
Speaker 1: into these issues.
Speaker 3: I think it's a remarkable year when we see that
Speaker 3: SpaceX Anthropicists apparently now file out Open AI wants to
Speaker 3: tap in all these i'll call mega IPOs. And yet
Speaker 3: Google also is tapping the market for eighty billion dollars
Speaker 3: of fundraising. So we've found.
Speaker 2: Eighty five billion. They've actually raised it because of demand too.
Speaker 3: There we go, there we go. Look, we are in
Speaker 3: this AI boom, and the question, to an investor's mind,
Speaker 3: is it a bubble? But we're in this AI boom
Speaker 3: where we're spending as a nation seven hundred and fifty
Speaker 3: billion dollars on AI infrastructure and that's tripled in just
Speaker 3: two years. And I think that each of these three companies, SpaceX,
Speaker 3: Open AI anthropic want to tap into that enthusiasm. And
Speaker 3: if I were their bankers back at Coman Sachs, I
Speaker 3: might say, you know, you want to tap in when
Speaker 3: the mood is there. But you just talked about private
Speaker 3: credit earlier, and there's deep valuation questions and concerns in
Speaker 3: private credit and private equity, in part because if AI
Speaker 3: is disruptive, it's going to probably destroy some value. And
Speaker 3: here these three big companies still have to figure out
Speaker 3: a revenue model.
Speaker 1: Yeah, and look, I mean we've had numerous IPOs over
Speaker 1: the years in the US where companies have come to
Speaker 1: market with maybe not a fully articulated long term business model,
Speaker 1: and that hasn't stopped them.
Speaker 2: And some companies that have gone on to great things.
Speaker 1: I think of Amazon met a lot of questions around
Speaker 1: at those companies when they came public, and they've proven
Speaker 1: themselves over time. When you look at the AI boom,
Speaker 1: or at least the interest in it right now, do
Speaker 1: you look at the technology itself, just set aside the
Speaker 1: money for a second, the technology itself as being really
Speaker 1: transformative of our society, economy, whatever.
Speaker 3: Im an mit, Yesimon to teach a class on ALI
Speaker 3: and I teach a class to AAI money. Simon Johnson
Speaker 3: and I have this Power and Consequences podcast. We talk
Speaker 3: about AI like every four or six episodes because it
Speaker 3: just Yes, I think it's really transformative. But remain if
Speaker 3: you look at the last two hundred years of transformative technologies,
Speaker 3: you pick your favorite eight to ten from canals to this.
Speaker 3: You usually have a boom period. We spend a lot.
Speaker 3: We usually peek around two to three percent of gross
Speaker 3: domestic product on the build. We often then have a retreat,
Speaker 3: sometimes a recession, sometimes big depressions like that eighteen seventy
Speaker 3: depressionts around railroads. You weren't around, weren't around.
Speaker 2: I've read about it.
Speaker 3: But you've read also about the Internet, and you were,
Speaker 3: you were reporting when we sort of had that retreat.
Speaker 3: So it's quite plausible we'll have that reckoning at some
Speaker 3: point in time. When you're spending seven eight hundred billion
Speaker 3: dollars on all the data centers and the native AI
Speaker 3: revenues right now or maybe one hundred to one hundred
Speaker 3: and fifty billion, that's got to right itself. And so
Speaker 3: how does open AI make money. They got to raise
Speaker 3: more revenues.
Speaker 2: Raise more revenue, and it' certainly possible they can do that.
Speaker 2: I think absolutely.
Speaker 1: At the market, I mean, there's been a lot of
Speaker 1: statistics showing the percentage of our population that's really using
Speaker 1: these tools, which is still relatively small. So if you
Speaker 1: believe this is going to be a big thing, you
Speaker 1: can say, Okay, there's an addressable market there that's yet
Speaker 1: to be All of.
Speaker 3: Their challenges is amongst themselves. They have competitors. Anthropic may
Speaker 3: pass right open AI at Google, and it's China as well,
Speaker 3: because China is taking another strategy where they're using AI
Speaker 3: and they're putting a lot of this app and what's
Speaker 3: called open weight models where you can get this secret sauce,
Speaker 3: you can get the weights and use it. And I
Speaker 3: think that Chinese want to diffuse that around the globe.
Speaker 3: And I'll say this, US companies will take a look
Speaker 3: at Chinese models if the US models don't stay sufficiently ahead.
Speaker 1: Did you get a chance at all to look at
Speaker 1: any of the contours of the executive order that Trump
Speaker 1: signed this week on kind of providing some sort of
Speaker 1: AI oversight.
Speaker 2: I mean yes, I know. It was very contentious for
Speaker 2: both reasons. Some people thought it doesn't go far enough.
Speaker 1: Something in the tech community thinks it's unnecessary, and a
Speaker 1: lot of those people will point to the progress that
Speaker 1: China has made in that fear.
Speaker 3: Look, it's just fascinating time. Anthropic has this new computer
Speaker 3: coding model. It's their fourth generation. It's called Mythos, and
Speaker 3: it's really successful. Can find thousands of problems in computer
Speaker 3: code that we humans haven't found and so anthropics has
Speaker 3: held it back and so forth, and the US government
Speaker 3: has taken note. What's fascinating is what the President just
Speaker 3: did is said, all right, let's have a voluntary system
Speaker 3: where these big model companies will for thirty days give
Speaker 3: the US government a look c Well. President Biden, who
Speaker 3: I worked with, had done something a little bit more robust,
Speaker 3: but was like, give the Department of Commerce a chance
Speaker 3: to look see. And when President Trump came in, he
Speaker 3: tore that all up. And now I think there's a
Speaker 3: debate inside his administration in the national security crowd is
Speaker 3: saying we need to be more careful. This AI too late.
Speaker 1: Though, I mean it's been a year and a half
Speaker 1: and you see the pace of this technology.
Speaker 3: This is again one of those things Simon and I
Speaker 3: just recently debated can AI behave responsibly and can we
Speaker 3: humans do deploying it to behave responsibly. I think that's
Speaker 3: going to be the challenge for our times. I think
Speaker 3: that is the challenge in the next couple of decades.
Speaker 3: But it's really here right now. We allocate jobs using AI,
Speaker 3: we allocate credit using AI. You probably decide what shows
Speaker 3: you put on this TV.
Speaker 2: Yeah, we do a lot with AI.
Speaker 3: With AI, absolutely, but that's okay. But now when militaries
Speaker 3: use it, when you cyber attackers, I tell the students
Speaker 3: that MIT, I say cyber attack remember the threat actors
Speaker 3: are using AI as well.
Speaker 1: I am curious just from your perspective, as as someone
Speaker 1: who's at a university. I know MIT is kind of
Speaker 1: probably a little bit of an Outliered asked this question,
Speaker 1: but we heard a lot of anxiety amongst graduating students
Speaker 1: this year, particularly with some of the commencement speeches kind
Speaker 1: of extolling the potential of AI and some of the
Speaker 1: heckling that those speakers got.
Speaker 2: In return, A students.
Speaker 1: Relatively receptive to I guess what's out there now and
Speaker 1: what may come.
Speaker 3: They're great students across the university, undergraduates to PhD students
Speaker 3: at MIT. I think that they'll do really well. But
Speaker 3: I tell them, I said, you can't let your guard
Speaker 3: down because you individually have to train the most important
Speaker 3: process of your own mind, and you don't want to
Speaker 3: have some cognitive offloading become like, oh my god, I
Speaker 3: can't reason and write and think on my own. And
Speaker 3: whether you go to a state school or MIT, that's
Speaker 3: what I advise people to do is still train this
Speaker 3: and you'll do all right, But there is going to
Speaker 3: be disruption. Every general purpose technology leads to big disruptions
Speaker 3: and the job forces and social chains, and so now
Speaker 3: you see a lot of Americans not only worry about jobs,
Speaker 3: the worried about addiction to these platforms. And so I
Speaker 3: think the political center is moving on this president, and
Speaker 3: this executive order was just a little bit of a
Speaker 3: leaning in that direction.
Speaker 1: And you're also saying starting to see it become a
Speaker 1: bit of a debate with regards in the midterm elections
Speaker 1: and how rank and file voters. You have Gary a
Speaker 1: great conversation Gary Gensler there. He's the former chair of
Speaker 1: the SECS now at MIT. He's got a great podcast
Speaker 1: with Simon Johnson that everybody should check out.