Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is alive
Speaker 1: from coast to coast with Caroline Hide in New York
Speaker 1: and ever though in sentences.
Speaker 2: Go this is Bloomberg Tech coming up. Wall Street can't
Speaker 2: get enough of SpaceX. With demand from big institutional investors
Speaker 2: and the biggest IPO in history, way over subscribed, class.
Speaker 3: Google backstops and thropping data centers, a Silicon valley raises
Speaker 3: to build AI infrastructure with ever more intertwined deals.
Speaker 2: An Oracle reports after the closing bell, it's a race
Speaker 2: between building data centers and booking AI cloud.
Speaker 4: Revenues, AI AI AI and space SpaceX's over subscribed IPO
Speaker 4: is where we have to start ed because the geographical
Speaker 4: reach of the level of demand.
Speaker 5: We've been mesmerized by this record breaking.
Speaker 2: Yeah, it's out of this world. I don't apologize for
Speaker 2: that for one bit. The state of players this right.
Speaker 2: The order book for institution investors closes four pm Eastern today,
Speaker 2: and as we've reported, there are several long only asset
Speaker 2: managers basically that want ten billion dollars worth of shares.
Speaker 5: It's seventy five billion dollars worth.
Speaker 2: So somebody is going to miss out. Now the retail
Speaker 2: investor can still place orders I think through Thursday on
Speaker 2: whatever platforms are available, but they're not guaranteed to get
Speaker 2: hold of those shares either. So that's the state of play.
Speaker 2: And believe it or not, there is a roadshow happening
Speaker 2: in the background, and.
Speaker 5: We're learning ever more on that roadshow. That's the entire point.
Speaker 3: We're understanding the transparency, the business model. We're learning much
Speaker 3: about those orbital data centers.
Speaker 2: Yeah, I think the focus in the pitch has still
Speaker 2: been let us explain orbital data centers. That brings us
Speaker 2: to today's big number, two hundred and fifty billion. That's
Speaker 2: a total amount of SpaceX IPO orders we've reported this morning,
Speaker 2: one to five billion of which is coming from Saudi Qate,
Speaker 2: other Middle East funds, sovereign funds. That's according to sources.
Speaker 2: That's the absolute latest. Joining us now to talk all
Speaker 2: things SpaceX. It's IPO. Also the general landscape Peter Singlehurst,
Speaker 2: head of private Companies are Bailey giff and we just know,
Speaker 2: you know SpaceX is a really important investment for you
Speaker 2: guys prior to the offering. Let's start there. You know,
Speaker 2: what does this the biggest IPO in history represent to
Speaker 2: you and to the firm and to I guess support
Speaker 2: the thesis when you first made the investment way back when.
Speaker 6: I think that the SpaceX IPO needs to be seen
Speaker 6: as the culmination of a trend which has been playing
Speaker 6: out now for fifteen years or longer, of companies staying
Speaker 6: private for longer. And this is something that we started
Speaker 6: to see in twenty twelve when we first started investing
Speaker 6: in private companies. Now, we didn't think the companies would
Speaker 6: get this big and stay private this long, But here
Speaker 6: we are with, you know, SpaceX going public at something
Speaker 6: like a one point eight trillion dollar valuation. That's nine
Speaker 6: hundred times larger and more valuable than Tesla was when
Speaker 6: it went public in twenty twelve. So, on the one hand,
Speaker 6: this is a story of a truly exceptional company which
Speaker 6: has compounded its growth at a very high rate. On
Speaker 6: the other hand, it's a story of a bigger structural
Speaker 6: trend of companies staying private longer and more and more
Speaker 6: return to accruing within the high growth private.
Speaker 5: Market and peter to that end.
Speaker 3: When you think about Tesla after it's gone public, it
Speaker 3: was a volatile ride, but it's twenty five thousand percent
Speaker 3: higher than when it listed, And so will we see
Speaker 3: a level of returns do you think in the public
Speaker 3: market or does that have to be in some ways
Speaker 3: pushed against the meat and bones of returns going to
Speaker 3: have happened to private investors.
Speaker 6: I think it's mathematically it's very hard to see how
Speaker 6: you could see SpaceX delivering the same kind of returns
Speaker 6: as a public company as Tesla did. But I think
Speaker 6: what this speaks to is a requirement for investors to
Speaker 6: have exposure to growth in both the private and the
Speaker 6: public markets. Has been set up to almost divide these
Speaker 6: things and say there's kind of private growth and there's
Speaker 6: public growth, and these things are different, and we've taken
Speaker 6: a different approach. We've sort of taken the view that actually,
Speaker 6: if you want to do growth equity investing, you want
Speaker 6: to do it properly, you have to do it in
Speaker 6: the private markets, and you have to do it in
Speaker 6: the public markets. And what our clients are and beneficiaries
Speaker 6: who are predominantly pension funds, what they need and what
Speaker 6: they ask from us is that we give them exposures
Speaker 6: to the world's best growth stage companies starting in the
Speaker 6: private markets. Earning the returns that we can generate there,
Speaker 6: and then also only those in the public markets from
Speaker 6: within our public funds to make sure that they're still
Speaker 6: capturing that growth even once companies transition into the public markets.
Speaker 2: Peter, I think it's important to pose the question why
Speaker 2: is SpaceX going public? And when Elon Musk spoke to
Speaker 2: Jamie Diamond, he eventually got to the answer, which is
Speaker 2: they need capital for this growth phase. But what we
Speaker 2: are seeing outside of just this fixation on IPOs is
Speaker 2: a race for capital through equity. How comfortable do you
Speaker 2: feel as a firm at Bailey gifed, whatever mechanism it
Speaker 2: is raising money at that volume, but it basically then
Speaker 2: goes directly into capital expenditure. That's what's happening here.
Speaker 6: You want to invest in companies that are able to
Speaker 6: deploy capital or high rates of return. So I don't
Speaker 6: think there's anything wrong, per se in investing in capital
Speaker 6: intensive businesses. In fact, what you want as a company
Speaker 6: that can deploy large amounts of capital, but where you
Speaker 6: can earn high returns on that capital. And ultimately that's
Speaker 6: what separates a good business from a bad business. It's
Speaker 6: return on equity. And so when we're looking at a company,
Speaker 6: whether it's SpaceX or Anthropic, what any other company that
Speaker 6: we invest in privately or publicly. Ultimately, what we're asking
Speaker 6: is how do you get to high returns on equity?
Speaker 6: And it's building those thesis that then leads us to
Speaker 6: invest in companies. And in the case of SpaceX, increasingly
Speaker 6: that thesis is going to have to rely on AI.
Speaker 6: They've shown that they can invest capital or high rates
Speaker 6: of return in rockets in starlink, and of course the
Speaker 6: next leg of that is going to be in AI
Speaker 6: data center build out, quite possibly in space but that's.
Speaker 3: Where it becomes so fascinating, particularly Peter for Bailey Gifford,
Speaker 3: which in the private markets backspace X on a thesis
Speaker 3: of SPACE, Backtindthropic on a thesis of AI, and now
Speaker 3: they're all overlapping in terms of business models. What is
Speaker 3: your perspective of commoditization or a winner takes all or
Speaker 3: is there room for all.
Speaker 5: Of these giant AI players? Do we winning in.
Speaker 3: The technology as well as perhaps in the public markets.
Speaker 6: I think what your question gets to is this very
Speaker 6: important question of where does value accrue in the AI stack? Now,
Speaker 6: hopefully lots of value is going to accrue to the
Speaker 6: end customers. That has to happen. Historically, we've seen value
Speaker 6: accruing to the chip manufacturers, initially with Nvidio, but now
Speaker 6: increasing league so memory manufacturers. But I think what we're
Speaker 6: also starting to see is value a crew at the
Speaker 6: foundational model level. And I suppose with the Grock acquisition,
Speaker 6: SpaceX is making a bet not just on the foundational
Speaker 6: model level, but also on the infrastructure level. And I
Speaker 6: think what we're seeing with the deal that they did
Speaker 6: recently with Anthropic is that they have options in terms
Speaker 6: of how they can monetize in the AI transition, both
Speaker 6: through their own models, but also importantly through the infrastructure itself.
Speaker 2: I have so many questions about this. You know, let's
Speaker 2: be honest. The hedge that SpaceX has put in place
Speaker 2: in the interim is to become a hyperscaler and sell
Speaker 2: compute played a blinder with that. We got the design,
Speaker 2: or at least the renderings of Orbital Data Center. I
Speaker 2: think the team are going to put them up on
Speaker 2: the screen now in that presentation that Elon must make,
Speaker 2: like there's the body, there's the solar arrays, there's the radiator,
Speaker 2: which part of the thesis, Peter is most important to you, right,
Speaker 2: It is a long way from the tam of twenty
Speaker 2: six point five trillion that they're basically packaging it as
Speaker 2: enterprise AI and in the interim, this plan for orbital
Speaker 2: data center like it needs to work. That's what they're
Speaker 2: telling people on the road show.
Speaker 6: So there's absolutely no question that the orbital Data center
Speaker 6: strategic that they're making widens the range of outcomes for SpaceX.
Speaker 6: On the one hand, if it works, it increases the
Speaker 6: potential upsides for the business. On the other hand, if
Speaker 6: this doesn't work, it's going to increase the downside for
Speaker 6: this company. And investing ultimately is about ranges of outcomes.
Speaker 6: It's about probabilities, and it's about payoffs in those range
Speaker 6: of outcomes. What we've seen with SpaceX over the years
Speaker 6: is that they have continuously tested and validated a series
Speaker 6: of outlandish hypotheses. The very notion of the business starting
Speaker 6: off as a private rocket company was a self an
Speaker 6: outlandish hypothesis that they've validated. Then the idea that you
Speaker 6: could have reusable rockets was also an outlanded hypothesis, and
Speaker 6: they validated it. They did the same with starlink with
Speaker 6: satellite based broadband. They've done the same with rockets on
Speaker 6: the scale of Starship and the orbital data centillate is
Speaker 6: that is the next hypothesis that they are seeking to validate.
Speaker 6: But everybody should be totally aware of the risks that
Speaker 6: are involved in this. It is unproven. In the event
Speaker 6: that they prove it, the payoffs will be large. But
Speaker 6: as we've already touched on the amount of cattle that
Speaker 6: is going into this means that the event that they
Speaker 6: don't validate it, it's going to increase the scope of
Speaker 6: downside in the investments as well, and investors just need
Speaker 6: to understand the range of outcomes and the payoffs that
Speaker 6: go with that.
Speaker 3: Can I ask about payoffs, Peter, because I don't want
Speaker 3: to go into the granularity of how much SpaceX E
Speaker 3: suppose you have, et cetera, but how long do you
Speaker 3: think you'll hold it and how much do you think
Speaker 3: it's a warrior or an anxiety that all these other
Speaker 3: big public companies are selling equity into this market at
Speaker 3: the same time Alphabet trying to fund its own capex
Speaker 3: in the equity market, Meta might be doing as well,
Speaker 3: And does that take oxygen out the room.
Speaker 6: I think that's probably part of the thinking that's going
Speaker 6: on for these different companies trying to raise these large
Speaker 6: amounts of cattle. They're sort of trying to soak up
Speaker 6: what available castle there is. But to your first question,
Speaker 6: different funds within Bailey Gifford are going to be in
Speaker 6: very different positions. For those funds that have owned SpaceX
Speaker 6: since twenty eighteen, since it was a thirty billion dollar company,
Speaker 6: those funds have very very large exposure, large positions in SpaceX.
Speaker 6: Now it might make sense post lock up for those
Speaker 6: funds to start selling down, even if they want to
Speaker 6: maintain a meaningful exposure, because ultimately, we are beholding to
Speaker 6: our clients and we have to provide them with a
Speaker 6: level of diversification within their funds. And then, of course
Speaker 6: funds that don't own it, funds that are solely public funds,
Speaker 6: they then faced with a question of whether to buy
Speaker 6: it for their funds. So it might well be that
Speaker 6: you see different funds within Bailey Gifford doing different things
Speaker 6: over the coming months, and that will be a function
Speaker 6: of the history and the portfolio context. I think there's
Speaker 6: universe agreement that SpaceX has been an exceptional company. The
Speaker 6: real question from here is what is the right price
Speaker 6: and what is the right position size in SpaceX.
Speaker 3: From thirty billion to potentially one point eight trillion this
Speaker 3: week Peter Senglehurst A Bailey Gifford A joy to have
Speaker 3: you on about all things SpaceX and the IPO landscape.
Speaker 3: More broadly, let's get though, also to political tensions. They
Speaker 3: are continuing to whipsaw markets. We are down a percentage
Speaker 3: point again on the NASA one hundred s and p
Speaker 3: is under pressure. You're seeing a really hardware of by
Speaker 3: two percent if you're looking at the semiconductor index. President
Speaker 3: Trump is saying that Iran would pay the price.
Speaker 5: For delaying peace negotiations.
Speaker 3: Let's get you up to speakably the most Tyler Kendall,
Speaker 3: the latest sim of the White House.
Speaker 7: What do we need to know, hey, Caroline, Well, at
Speaker 7: this point, President Trump is renewing his threat, really just
Speaker 7: underscoring that this White House has mounting frustration with the
Speaker 7: ongoing negotiations, as the US has repeatedly maintained that they
Speaker 7: were trying to prioritize a diplomatic solution to end the conflict. Now,
Speaker 7: President Trump's remarks aren't totally clear if this means that
Speaker 7: we're going to see an end to the ceasefire agreement
Speaker 7: after we saw the worst flare up in fighting between
Speaker 7: the sides just overnight with both the US and Iran
Speaker 7: exchanging strikes after an American military helicopter was shot down.
Speaker 7: After the strike from the US then on Iranian military assets,
Speaker 7: we saw Iran put forward some strikes and attempts to
Speaker 7: hit American military assets. It's really been escalating from here.
Speaker 7: But our own analysts at Bloomberg Economics, perhaps this is
Speaker 7: a bid to escalate in a bid to de escalate.
Speaker 7: In one positive sign for the negotiation front, Irani and
Speaker 7: state media reported within the last hour that a Katari
Speaker 7: delegation has landed in Tehran in a bid to keep
Speaker 7: diplomacy on track. But Ed and Caroline, I want to
Speaker 7: highlight this renewed risk that we are seeing moments ago
Speaker 7: flashing across the Bloomberg terminal. India is now condemning an
Speaker 7: apparent attack on a commercial vessel near the Strait of
Speaker 7: her Moves off the coast of Oman. So definitely still
Speaker 7: very high intentions contributing to the situation that we're seeing
Speaker 7: on the ground.
Speaker 2: Goodvoks Tyler Kendall, Thank you very much. So coming up,
Speaker 2: Google steps up to backstop a massive thirty five billion
Speaker 2: dollar financing deal from Propic. We had the details. Next,
Speaker 2: this is Bloomberg Tech. Super Micro is targeting public markets
Speaker 2: for a massive seven billion dollar equity raise. Sales to
Speaker 2: super Micro servers fitted with video chips have surged for
Speaker 2: AI workloads. The server manufacturer is moving quickly to fund
Speaker 2: a staggering thirty nine billion dollars in orders, using the
Speaker 2: fresh cash injection to pay for the equipment needed to
Speaker 2: make the servers. That's greasing the wheels. Google is backstopping
Speaker 2: a massive thirty five billion dollar financing deal from Fropic,
Speaker 2: The creator of Claud, is leasing AI chips across five
Speaker 2: different data centers with help from the long established tech
Speaker 2: giant Doomscott Carpenter joins us now to break down the mechanics, like,
Speaker 2: you know, to our audience, what does backstopping mean? I
Speaker 2: think what we're saying is guaranteeing the funds for it
Speaker 2: and the event that something goes wrong.
Speaker 8: But but just go with the basics, right, So, first
Speaker 8: of all, Broadcom is providing a huge guarantee on the
Speaker 8: chips themselves, as the biggest part of the thirty five years. Yes,
Speaker 8: these are Google's TPUs that are going to be involved,
Speaker 8: So Broadcom is backstopping the debt itself.
Speaker 2: Now, the chips, when.
Speaker 8: They are delivered and they have to be manufactured, are
Speaker 8: going to be used in these five data centers that
Speaker 8: we identify in the story. The leases on those five
Speaker 8: data centers are backstopped by Google. So you could think
Speaker 8: of it as two different forms of guarantees being involved
Speaker 8: in this to put together this deal. There's the Broadcom
Speaker 8: one and there's the Google ones underneath.
Speaker 3: So in a way, Broadcom's saying almost Alphabet's going to
Speaker 3: get its money from Anthropic for buying the chips.
Speaker 5: Is that right?
Speaker 3: Meanwhile, who's getting the money for the leases and who
Speaker 3: therefore is Alphabet saying like, you're good for the money,
Speaker 3: don't worry. Is that the people actually constructing the data
Speaker 3: centers owning the land.
Speaker 8: It's the leases are to Fluid Stack, which is a
Speaker 8: company that Anthropic has said it's been working with to
Speaker 8: develop these data seen. So you see how it's it's complicated, right,
Speaker 8: there's many.
Speaker 2: See how it's complicated.
Speaker 8: Yeah, yeah, I mean to pull off a deal of
Speaker 8: this magnitude, which I think is the largest private credit
Speaker 8: deal in history, definitely the largest chip deal. There's a
Speaker 8: lot of moving pieces. One of the key things is
Speaker 8: that these chips are not I mean, they need to
Speaker 8: be created.
Speaker 2: They don't exist right now. But yeah, there's a lot
Speaker 2: that goes into this.
Speaker 3: Who's the manufacturer of questions around Intel? VISs TSMC absolutely fascinating.
Speaker 3: Scott Carpenter. He broke it down so clearly, we so
Speaker 3: appreciate it. Meanwhile, let's turn our attention to soft Bank.
Speaker 3: It's attempt to leverage its massive AI bets. It's hitting
Speaker 3: a bit of a wall. Sources told Bloomberg that talks
Speaker 3: are stalled with potential creditors to raise at least six
Speaker 3: billion dollars from a margin loan backed by its opening
Speaker 3: eye steak. So it's unclear why the pores comes just
Speaker 3: weeks after soft Bank slashed it's fundraising target from ten
Speaker 3: billion dollars, and soft Bank shares have tumbled nearly ten percent.
Speaker 3: On the news today, A says the firm ways alternative
Speaker 3: funding options coming up.
Speaker 5: We are going to be speaking a.
Speaker 3: Saphia Noble, Professor, director of the Center of Resilience and
Speaker 3: Digital Justice, at the UCLA to discuss bias discrimination within
Speaker 3: this world of AI.
Speaker 5: We keep talking more on that next as a Blueberg tech.
Speaker 3: This week, as Open AI filed its S one confidentially,
Speaker 3: CEO Samaltman was also out with a sweeping long term
Speaker 3: vision for generative AIS alignment with humanity and warned that
Speaker 3: transformative technologies such as AI can concentrate power, stating Open
Speaker 3: AIS quote clear ride about the risks as it aims
Speaker 3: to build powerful systems that remain safe subject to human control.
Speaker 3: But critics have long voiced concerns about AI risks such
Speaker 3: as algorithmic bias in equality joining us now, Sofia Noble,
Speaker 3: UCLA professor author of the acclaimed book Algorithms of Oppression
Speaker 3: clear Ride, is that enough. Are we seeing some of
Speaker 3: the risks being digested and answered for within these models?
Speaker 9: I don't think so. I don't think we're anywhere near
Speaker 9: a call for or an ability to realize safe AI.
Speaker 9: What we see, in fact, are chapbot technologies and large
Speaker 9: language models that for the most part don't have markets.
Speaker 9: I mean, they were built for Corporate America to reduce
Speaker 9: labor costs, but corporate America is moving away from them
Speaker 9: because they're very expensive, they're not really reliable, they are
Speaker 9: incredibly negatively impactful on the environment.
Speaker 3: Moving away from them, you think corporate America is moving
Speaker 3: away from lagenguge porp.
Speaker 2: I do.
Speaker 9: We've been seeing studies where companies are saying that it's
Speaker 9: more expensive for them to use these chatbots because human
Speaker 9: beings have to check the efficacy and the reliability. There
Speaker 9: are so many errors, factual errors that are proliferating through
Speaker 9: these technologies. So if the technology itself is that flawed
Speaker 9: and it's being now on the public as some type
Speaker 9: of solution, I think we're in trouble. And of course
Speaker 9: we know that we have the racial bias, the gender bias,
Speaker 9: the kind of geographic and political concerns about what comes
Speaker 9: out of these technologies. I think that we are moving
Speaker 9: into very dangerous territory trying to bolster our society on
Speaker 9: large language models fascinatable.
Speaker 2: The large body of your work looked at the data
Speaker 2: issues for commercial search engines. Basically the net result is
Speaker 2: that the search engines, as per your books title, reinforce racism. Yes,
Speaker 2: what was the underlying issue in the search engine case study?
Speaker 2: And what is different or the same about the large
Speaker 2: language models that I think you're saying yield a similar
Speaker 2: result they do.
Speaker 9: So what we've seen over the last fifteen twenty years
Speaker 9: is that all of the discrimination that's in our society,
Speaker 9: all of the kind of stereotyping, all of the inequality,
Speaker 9: just gets packaged up and then used to train models.
Speaker 2: So it's within the data.
Speaker 9: Within the data, but it's also the people who are
Speaker 9: designing the models are really not aware. They don't understand
Speaker 9: the kind of social, historical, economic processes. These are software
Speaker 9: engineers who don't even think about they don't even ask
Speaker 9: the kinds of questions that let's say, a sociologist like
Speaker 9: I would ask. And so we have discriminatory data that
Speaker 9: is training models. But what's different now is that these
Speaker 9: models obfuscate the inequality. They appear to be factual and reliable.
Speaker 9: And if you don't know, if you don't have deep expertise,
Speaker 9: you're not going to know that the kinds of things
Speaker 9: that are being served up in these products are actually faulty.
Speaker 9: And imagine putting your whole business enterprise, your public institutions,
Speaker 9: your schools, your libraries, making that the backbone. I mean
Speaker 9: that is to me quite dangerous Sophia.
Speaker 3: We can talk at length about the risks and the problems.
Speaker 3: What about the solutions here because we saw but at
Speaker 3: least two years ago, I think it was when alphabet
Speaker 3: was struggling to ensure that some of the images AI
Speaker 3: generated images didn't overcompensate for some of the worries about
Speaker 3: racism and bias within the algorithm.
Speaker 5: So what have you been done that works. Let's not
Speaker 5: just beautify the problem, let's give us the solution.
Speaker 9: Well, I think that we don't want to give up
Speaker 9: what it means to have human expertise, human journalists, fact checkers, teachers, thinkers.
Speaker 10: This is our most powerful asset.
Speaker 9: These human beings are people, and we can't replace people
Speaker 9: with these kinds of machines. So to me, you know,
Speaker 9: having deep knowledge in the humanities and social sciences, these
Speaker 9: are the things that are going to really be important
Speaker 9: as we go forward in society. And we're over investing,
Speaker 9: I think, in the wrong things. We need to be
Speaker 9: investing in putting resources into pro social, pro rights respecting technology.
Speaker 9: There's a whole world of small language models and different
Speaker 9: kinds of very interesting kinds of technologies that women are
Speaker 9: thinking about that people of color are working on and
Speaker 9: these are the least invested in, but they are I
Speaker 9: think the kinds of technologies that are going to help
Speaker 9: us find a way forward.
Speaker 2: Sophea, How conscious of and open about are the companies
Speaker 2: on the issue, And you know, in research and writing
Speaker 2: your book, but your ongoing work, how much do they
Speaker 2: engage with you on it?
Speaker 9: The companies for the most part want to deny, deny
Speaker 9: the most dangerous dimensions of their products, and of course
Speaker 9: they are only interested in regulation that they're writing. We've
Speaker 9: just saw the landmark ruling against Meta, where they knew
Speaker 9: that their products were harmful, especially to girls and to women.
Speaker 9: And of course this includes all of the kind of
Speaker 9: deep fake technologies that these companies are invested toute.
Speaker 2: That right, and we covered that case in detail on
Speaker 2: the program.
Speaker 9: But yeah, well, I think you know what we have
Speaker 9: is more and more litigation against these companies because there's
Speaker 9: evidence of harm.
Speaker 2: Sophear Noble, Professor and director of the Center and Resilience
Speaker 2: and Digital Justice. You see, La, thank you very much
Speaker 2: for joining us coming up on the show. The excitement
Speaker 2: around SpaceX's IPO is putting pressure on market operators to
Speaker 2: make sure this goes smoothly, we get really in the weeds,
Speaker 2: very technical about what pulling off the biggest IPO and
Speaker 2: history means for the market. That's next, That's what markets
Speaker 2: look like. Stay with us. It's half time and this
Speaker 2: is Bloomberg Tech.
Speaker 5: Welcome back to Bloomberg Tech.
Speaker 3: We check in on these market which are under pressure
Speaker 3: as we await the biggest IPO in history. Then as
Speaker 3: that one hundred is off five a percentage point, there's
Speaker 3: geopolitical tensions, risks and terms about yet further conflict in
Speaker 3: the Middle East between Iran the United States.
Speaker 5: We see the semic conduct To Index hardware.
Speaker 3: Once again, having risen so much, gets pulled back somewhat
Speaker 3: of by two percent.
Speaker 5: Magnificent seven also down, but some aren't. For men. ASML
Speaker 5: just finishing trading.
Speaker 3: In Europe record high I since nineteen ninety five. We're
Speaker 3: up another percentage point on ASML and its European trading
Speaker 3: on the day.
Speaker 5: But we really do shine light and what's.
Speaker 3: Been happening more broadly in the American indices and there is.
Speaker 5: Some concern there.
Speaker 2: Yeah, tech trians are driving us lower, but SpaceX there
Speaker 2: is going to be an element of volatility whatever happens
Speaker 2: and outside demand for SpaceX shares has market operators stress
Speaker 2: testing their systems to ensure smooth trading for the largest
Speaker 2: IPO in history. Bloomberg yzabel Lee has been speaking with
Speaker 2: some of those firms. And the way that you put
Speaker 2: it third paragraph of a critically important story is when
Speaker 2: this IPO hits, you're talking millions and millions of orders,
Speaker 2: and with those orders comes millions and millions of messages
Speaker 2: and transactions. This becomes a technology story. How does that work?
Speaker 2: What is it that they're stress testing right now?
Speaker 11: Thank you for reading, and that's proof that you read
Speaker 11: this story. But indeed, I think much has been said
Speaker 11: about the excitement surrounding SpaceX IPO, but what is often
Speaker 11: left un said is the plumbing that powers this IPO,
Speaker 11: because for the IPO to be successful, the plumbing has
Speaker 11: to work smoothly.
Speaker 10: And we talked to a couple of those players.
Speaker 11: The TCC, for one, they're the Depository Trust and Clearing Corporation.
Speaker 11: They like to say they're the most important company that
Speaker 11: no one has.
Speaker 10: Ever heard of.
Speaker 11: Think of them as the central plumbing that is basically
Speaker 11: in charge of virtually all transactions that processes, clears and
Speaker 11: settles us financial assets in the US. We also talk
Speaker 11: to the S andp's equity book builder. They think of
Speaker 11: them as like the financial technology used by global investment
Speaker 11: banks to really power a lot of these underwritings like IPO.
Speaker 11: So they have been preparing for weeks for this SpaceX
Speaker 11: a IPO for DTCC.
Speaker 10: They're going to have a watch party over the weekend.
Speaker 11: For the SMP, they're using AI to make sure that
Speaker 11: all systems are smooth.
Speaker 3: One are the biggest fas is it just slowness or
Speaker 3: is it anything that could go more deeply awry here
Speaker 3: as well?
Speaker 11: For DTCC, they said, it's not one big risk, but
Speaker 11: it's just how interconnected everything is, so it could be
Speaker 11: one small broker dealer or maybe one small market maker
Speaker 11: that may not be as prepared and it will be
Speaker 11: just a huge domino effect that will really affect everything.
Speaker 11: Because they really are kind of worried about what happened
Speaker 11: in Facebook in twenty twelve, and a lot of retail
Speaker 11: investors were left in the dark as well as investment
Speaker 11: bankers because it was marred by a lot of technical
Speaker 11: failures that left some traders really uncertain. So I think
Speaker 11: they've learned from that. It's been more than a decade
Speaker 11: since technology has grown leaps and bounds, or they're really
Speaker 11: preparing for it. At SMP, they have what they call
Speaker 11: a pre mortem, which is the opposite of post mortem,
Speaker 11: so they're really ensuring that everything is really going to
Speaker 11: go smoothly. For example, they made sure that the tripling
Speaker 11: of order handling capacity is going to be possible and
Speaker 11: fourfold improvement in response time, so it really allows to digest.
Speaker 11: But I want to join our watch party over the
Speaker 11: weekend they're going to be online twenty four to seven.
Speaker 10: They said, we'll.
Speaker 3: Start our own as VALI, who has just been so
Speaker 3: watchful on this IPO for us, thank you very much. Indeed, Meanwhile,
Speaker 3: the AI economy, well it's moving fast and now we
Speaker 3: may have a clearer way to track how it's changing
Speaker 3: the way people work. ADP has partnered with the Stanford
Speaker 3: Digital Economy Lab to launch the Canaries Dashboard.
Speaker 5: It's a real time indicator.
Speaker 3: Designed to show how AI is reshaping different occupations based
Speaker 3: on actual labor market data. Joining us some More is
Speaker 3: one of the researchers involved in this project. Nila Richardson,
Speaker 3: chief economist at ADP, former senior economist actually at Bloomberg
Speaker 3: and NILA present. You are monitoring the present, not the past.
Speaker 3: But what data do you take in?
Speaker 5: How are you monitoring this?
Speaker 10: Well?
Speaker 12: First of all, we are thrilled because AI is said
Speaker 12: to be one of the most consequential technologies the world
Speaker 12: has ever seen, and yet we have very limited ways
Speaker 12: of measuring impact. And so this is why I'm so
Speaker 12: excited to partner with Stanford Digital CONNOMU Lab led by
Speaker 12: Eric Burne Jolson on these AI indicators and namely the
Speaker 12: Canaries Dashboard, which tracks the impact of AI on occupations
Speaker 12: in almost real time. This is about moving the conversation
Speaker 12: about AI's impact from what we think to what we
Speaker 12: know and what we can measure with the data Nila.
Speaker 2: There was a section of the labor market that really
Speaker 2: just jumps off the screen at me, and that is
Speaker 2: the early career workers. These are people aged twenty two
Speaker 2: to twenty five. And there is a I don't know
Speaker 2: how you would put it, a bifurcation, right, industries that
Speaker 2: have exposure to AI, industries that have nothing to do
Speaker 2: with it whatsoever. What is the data telling us there?
Speaker 2: And if you can the why the why?
Speaker 12: Well, the important part of this data series is that
Speaker 12: it is able to categorize over seven hundred occupations by
Speaker 12: AI exposure in a very granular way.
Speaker 10: But it's more than.
Speaker 12: That because it's not just how the AI is affecting occupations,
Speaker 12: but it's about how AIS is affecting workers people young career.
Speaker 12: So we're able to use the demographics and the ADP
Speaker 12: payroll data and segment it by early career twenty two
Speaker 12: to twenty six, and look, AI, you know this as
Speaker 12: well as anyone has two different roles in a business context,
Speaker 12: it can augment. That's the old school story. That's the
Speaker 12: dinosaur story of technology. How do you sorry automate work?
Speaker 12: That's the old school story. The new school story. The
Speaker 12: frontier is how to augment. And so what AI does
Speaker 12: for early career in AI exposed fields, it looks like
Speaker 12: it's automating certain tasks.
Speaker 10: So the trick is how do we move.
Speaker 12: From automation to augmentation and look for those higher value
Speaker 12: tasks higher value work?
Speaker 2: And the result is that in that space employment is contracting.
Speaker 12: Right, So for AI exposed careers, there is a contraction
Speaker 12: for early career in like software developers. So let's take
Speaker 12: software developers. Since the rollout of chat GPT. In November
Speaker 12: twenty twenty two, the dashboard shows there's been a twenty
Speaker 12: percent decline in early career software developers, but when you
Speaker 12: look at older workers, no decline at all. In fact,
Speaker 12: you're seeing growth. That shows you that there is a
Speaker 12: disparate impact here. For skills and tasks that are easily automated,
Speaker 12: you're seeing in an effect that's the early career, But
Speaker 12: for work where it's more complex, AI becomes a helpmate,
Speaker 12: a coworker, an augmentation tool as opposed to an automation tool.
Speaker 3: This is going to be released every Wednesday after Jobs Week,
Speaker 3: so it's monthly data. How are you thinking about when
Speaker 3: you realize an industry is becoming AI exposed At the moment,
Speaker 3: you've been so fascinating with the fact that we've got developers,
Speaker 3: customer services, but which one to understand luhees the next
Speaker 3: How are you seeing low AIX posed operations starting to
Speaker 3: change will become AI exposed.
Speaker 12: That's a great question, and that's really the purpose and
Speaker 12: mission of this work. It is to track value creation
Speaker 12: in real time. And the thing about it is you
Speaker 12: can't track it in a macro way. You can't track
Speaker 12: it in the markets those yeah, market IPO creation. Value
Speaker 12: creation is very different to how it affects the real
Speaker 12: economy and what people are really experiencing at work. And
Speaker 12: so at the task level is where you see value creation,
Speaker 12: and you see that in certain complex jobs. So let's
Speaker 12: move on from software developers. Maybe look at radiologists where
Speaker 12: AI becomes a really important diagnostic tool and you can
Speaker 12: see that value creation and delivery helping them concentrate on
Speaker 12: the work that is necessary for human to human interaction
Speaker 12: as opposed to simply diagnosing different patterns, which AI is
Speaker 12: good at. So the key for employers is how to
Speaker 12: extend human capability, not limit it, not replace it, but
Speaker 12: extend that capability to new task and new value creation.
Speaker 2: We have a good case study for that. Bloomboats remain Bostic.
Speaker 2: Just spoke to the IBM CEO about this exact point.
Speaker 2: Let's listen to what he had to say, and then
Speaker 2: you can say if it shows up in the data.
Speaker 13: Okay, using AI tools, now we can probably add ten
Speaker 13: points of profit energy on day one because the amount
Speaker 13: of time it used to take to move contracts over,
Speaker 13: to do all of the sales automation, to do all
Speaker 13: of the revenue forecasting. All of that now using AI
Speaker 13: can be shrunk down literally a few weeks.
Speaker 2: When I listen to that, I just can't draw a conclusion.
Speaker 2: Are we talking about role elimination? Are we talking about
Speaker 2: a boost of productivity? Which Caroline and I were told
Speaker 2: by SFF president Mary Daily last week, isn't showing up
Speaker 2: in the data yet, Like, how do you read the
Speaker 2: corporate speak in this job market?
Speaker 12: The way I read it is this AI has the
Speaker 12: ability to reach shape work, and yet it's still a
Speaker 12: tool that decision lies with the employer, and so this
Speaker 12: data is about empowering employers to make that decision. Is
Speaker 12: AI going to be your efficiency tool? There's a clear
Speaker 12: case for that. There's a clear use case that AI
Speaker 12: is making certain work more efficient. You can do more
Speaker 12: with less. But I think AI has the potential for
Speaker 12: more than that. It is a productivity tool in the
Speaker 12: sense that it enhances work, it makes work better, it
Speaker 12: makes problems easier to solve, and therefore you can tackle
Speaker 12: more problems. If it's an augmentation tool, that is something
Speaker 12: completely different, and hopefully data will help employers find that
Speaker 12: value creation within their own businesses. So right now, I
Speaker 12: think the narrative is really really wide. The data can
Speaker 12: help anchor it on the truth of the moment.
Speaker 3: And Avin would actually say they are hiring more graduates
Speaker 3: than they happened in the past, more than ever the moment.
Speaker 5: So it is interesting where the new grudge is coming
Speaker 5: out and whether or not.
Speaker 2: Now we have real time data to keep the monest
Speaker 2: on it. Anita Richardson chief promise that ADP is great
Speaker 2: to have you back on the show. Thank you very much.
Speaker 2: Now coming up, Anthropic CEO Dario M. O Day says
Speaker 2: his company's AI doesn't spell the death of software, that
Speaker 2: some firms will be losers at that part of the conversation. Next,
Speaker 2: this is an a big tech.
Speaker 14: Now early on others focused on fund splashy consumer apps.
Speaker 14: You made a bet on coding and enterprise. Why did
Speaker 14: you make that bet? Was it a values decision or
Speaker 14: a business decision?
Speaker 15: Look, if you pick a business model that fundamentally conflicts
Speaker 15: with your values, you're going to have a hard time, right,
Speaker 15: either you betray your own values or you become irrelevant.
Speaker 15: And so when we thought about it, we said, look,
Speaker 15: you know, we've seen the world of social media, the
Speaker 15: consumer world. It really seems to you know, encourage engagement
Speaker 15: even addiction. You know, the slop we've seen with AI
Speaker 15: video models, It's like, what's going on? Is it want
Speaker 15: to maximize the number of minutes that you're you're paying
Speaker 15: attention to because that's the advertising revenue driven incentive. Whereas
Speaker 15: if we look at enterprise, look, I mean, you know,
Speaker 15: we want to make these models useful to people. We
Speaker 15: want to use AI to you know, cure diseases that
Speaker 15: we couldn't cure before. Right, Well, that's working with biotech,
Speaker 15: it's working with pharma, it's working with academic research groups.
Speaker 15: All of those are enterprises.
Speaker 6: Right.
Speaker 15: We want to use AI to like, you know, to
Speaker 15: make energy cheaper and more efficient.
Speaker 2: That's that's all enterprise.
Speaker 15: And so I think it served us well to have
Speaker 15: this business model that largely aligns with our values.
Speaker 14: Soon after Claude Cowork was released, two hundred and eighty
Speaker 14: five billion dollars in market value vanished overnight. Traders called
Speaker 14: it the SaaS apocalypse.
Speaker 15: This kind of white collar wipeout story in the software
Speaker 15: set to terrifying.
Speaker 3: Some of those are down for nine days in a row,
Speaker 3: So clearly the is building if.
Speaker 14: AI continues improving at this pace, how much of traditional
Speaker 14: software gets replaced and how fast I think.
Speaker 15: With AI, like the pie is getting bigger. Right, so
Speaker 15: the existing incumbents may be smaller and relative terms, some
Speaker 15: of them may may go down in value. Some of
Speaker 15: them may even may even go out of business if
Speaker 15: they don't, if they don't adapt in the right way.
Speaker 15: But like I would guess that the software industry gets larger,
Speaker 15: not smaller, although there will be some big losers, those
Speaker 15: who don't kind of see what's coming, who don't identify
Speaker 15: the motes they have, they're going to have a really
Speaker 15: hard time.
Speaker 3: That was Bloombg Examine Chang speaking with Anthropic CEO Daria Amiday,
Speaker 3: and you can catch part one of this two but
Speaker 3: episode of the circuit it comes out later today. It
Speaker 3: airs on Bloombg TV at six pm Eastern and sticking
Speaker 3: with Anthropic, but the company has released a new model
Speaker 3: called Claude Fable five for the capabilities of it SMITH
Speaker 3: or SAI, but includes godrails so prevent it from responding
Speaker 3: to queries on topics including cybersecurity and biology.
Speaker 5: Now you'll remember Anthropic.
Speaker 3: Initially released with us only to select organizations after warning
Speaker 3: that it could exploit cyber vulnerabilities.
Speaker 2: AI startup Poetic has emerged from sealth with fifty million
Speaker 2: dollars in funding and a half a billion dollar valuation.
Speaker 2: Right out of the gate. Poetic system helps businesses streamline complex,
Speaker 2: long running tasks. Its founder and CEO, Markey Wagner, previously
Speaker 2: launched AI consultancy Delphi Labs and worked on machine learning
Speaker 2: at Google and Waimo and Markey joins us. Now this
Speaker 2: was one of the I guess we call it a
Speaker 2: coconut round or a mango seed round. But right out
Speaker 2: the gate open AI Kleine Perkins founder's fund are backing you.
Speaker 2: What is it they know that we don't yet about Poetic?
Speaker 2: What is Poetic up to?
Speaker 16: Yeah, so a bit about Poetic.
Speaker 17: So Poetic is in an AI system that can learn
Speaker 17: and execute extremely complex, multi hour processes at some of
Speaker 17: the biggest companies in the place with over ninety nine
Speaker 17: percent accuracy and ten times less tokens. And so what
Speaker 17: they've seen is they know our customers and they've seen
Speaker 17: the results, and they've seen a lot of AI pilots
Speaker 17: that have gone well and poorly. And you know, we've
Speaker 17: scaled up every single customer you've had into production in
Speaker 17: a time when a.
Speaker 16: Lot of these things are getting stuck in demo land.
Speaker 16: And so that's what they've seen.
Speaker 3: I mean, marketing people describe you to me as the
Speaker 3: AI whisperer to some of the most important companies out there.
Speaker 3: And Clin Perkins has put out a blog about them
Speaker 3: backing you, and they reference Anthony Noto.
Speaker 5: It's so far.
Speaker 3: The CEO just saying how in weeks your company is
Speaker 3: sort of turned around food processes and to end, how
Speaker 3: and how are you doing it with not really that
Speaker 3: much compute, that much token being used at that time.
Speaker 17: Yeah, so we have a bit of a different approach
Speaker 17: than what most folks are doing right now. And so
Speaker 17: we have this system that is kind of the synthesis
Speaker 17: of both AI and code. So, you know code today
Speaker 17: is you know, it's very static, and so the innovations
Speaker 17: of the past written in code. If something small changes
Speaker 17: like a column name, it would break an AI. On
Speaker 17: the other hand, agents are incredible, but they figure out
Speaker 17: what to do step by step and they can easily
Speaker 17: go off the rails.
Speaker 2: We have this.
Speaker 16: System that takes the best of both.
Speaker 17: So a task is written in English similarly to an
Speaker 17: operating procedure right, and our.
Speaker 16: System will turn that into code under the hood.
Speaker 3: I'm terribly sorry, Maki Wagne, but this President of the
Speaker 3: United States is talking.
Speaker 5: At this moment. We must go on.
Speaker 3: Iran CEO founder a poetic We tend our attention to
Speaker 3: President Trump
Speaker 16: At speeds that you wouldn't want to go.