긱 경제와 노동 시장 역학 관계에 미치는 영향

### 칩 부문에 대한 투자의 과제와 긱 경제의 진화
* 💡 현재 칩 기업을 둘러싼 활기는 필요한 혁신과 투자를 촉진하는 **'긍정적인 거품'**을 반영하지만, 기대치가 실제 수익률을 앞지르면서 실망으로 이어질 수 있습니다.
* 📉 닷컴 버블과 마찬가지로 인터넷이 일상생활을 변화시켰지만 많은 기업이 **부풀려진 기대치를 충족시키지 못했고**, 이는 칩 부문에서도 잠재적인 패턴이 나타날 수 있음을 시사합니다.
* 🌍 데이터 센터에 대한 투자는 미국과 유럽 모두에서 증가하고 있으며, **미국은 가장 가까운 경쟁국의 약 10배로 추정되는 용량**에서 크게 앞서고 있습니다.
* 📊 AI 애플리케이션을 위한 알고리즘의 발전으로 인한 데이터센터의 효율성 향상은 **두 지역 모두에서 용량 초과**로 이어질 수 있습니다.
* 🏗 유럽은 다양한 개인정보 보호 규정으로 인해 **건설에 더 나은 환경**을 제공할 수 있으며, **인프라에 필요한 투자**를 촉진할 수 있습니다.
* 🚗 특히 **Uber와 같은 플랫폼을 통한 긱 경제**는 노동 시장의 역학 관계 변화를 반영하여 기존의 실업급여보다 **더 실용적인 소득 수단**으로 부상하고 있습니다.
* 📉 그러나 긱 이코노미에 진입하는 개인이 늘어나면서 경쟁이 치열해져 **운전자의 소득 감소**로 이어질 수 있으며, 이는 진화하는 노동 시장의 과제로 부각되고 있습니다.
* 🔍 급변하는 데이터 환경을 이해하는 것은 특히 경제 의사 결정을 내리는 데 이러한 정보에 의존하는 **중앙 은행가에게 매우 중요**합니다.

긱 이코노미는 더 나은 수입 대안을 제공하지만, 경쟁이 치열해지고 드라이버의 수입이 감소하는 문제에 직면해 있습니다.

You've got a rich history in terms of thinking through the macro. Michael, what then of the exuberance still around chip names? Is it right to have that reality? Well, I think this is a natural challenge. This is what's called a positive bubble. It effectively is driving innovation and investment into a space that absolutely requires it. But there's almost no real opportunity for the companies currently involved to earn their returns that are actually built into these expectations. We may see that not happen in this month or in this quarter, but as we look forward, there's going to inevitably be a period of disappointment. This is in a lot of ways very similar to the build-out around the dot.com cycle in which the Internet ultimately exceeded all of our expectations, radically changing how we shop and live our lives. But at the same time, we saw extraordinary disappointment as many of the companies involved were unable to realize their potential. Michael, that piece of news about TSMC

in Europe shows there is datacentre and chip capacity activity here in the States. And there's the same in Europe. Is there pros and cons to kind of focusing your attentions as an investor on just one? I'm not really sure that I would emphasize one versus the other, because, again, this is all data that can be transmitted. A lot of the investment in Europe is going to be a byproduct of separate and different privacy rules, and we'll probably continue to see investment around that. The United States still stands head and shoulders above the rest of the world in terms of data center capacity. I forget the exact number, but I want to say it's roughly ten X, It's next largest competitor. And at the same time, we're seeing extraordinary gains in the efficiency of these data centers, which is actually going to, of course, mean that the capacity ends up being much greater than we thought.

Same underlying phenomenon we saw, again, with fiber optic build-out in the late 1990s, where we expected to have to use tons of dark fiber. We ultimately laid that in the ground, and we've used only a small fraction of it as other forms of technology improved alongside it. In this case, we're starting to see those efficiency gains from rewriting algorithms to optimize them for A.I. or data center applications. So ultimately, I think in both places, we're going to find that we have at least a period of excess capacity that emerges. And I think investors in both locations should be considerate of that. Europe probably offers a better construction environment, as I said, because they have different rules. They're going to need to make their own investments. So everyone going all in on basically the infrastructure build-out. But where else in the stack, where else in the value add of AI should you be broadening out to?

Well, I think we're starting to get to the point that you want to start thinking about the implications of this. Right. So financial institutions could benefit tremendously from an improvement in the ability to run analysis or process components. We're starting to see shopping algorithms. Certainly, we're seeing things like support bots in terms of the traditional call center type work. The efficiency gains there are immense. Again, I think one of the real challenges that we're going to face is that disruption and we're really uncertain of it, right? People remember the comparisons of Amazon to Barnes and Noble and now they no longer remember Barnes and Noble except as a college textbook destination. My hunch is that we're going to see a lot of the same things. The world is changing very rapidly. It's a period of uncertainty. You have to price that into effectively the optionality of these names. Once you start to realize that it may

turn out that they are the vast majority are unable to deliver on the opportunity that was in front of them. Against that, I think that we're seeing lots of interesting opportunities that are being created in more mundane areas like fixed income, where we're beginning to recognize that the level of interest rates is unlikely to be sustainable. Microphone. We can say that sort of. I'm sorry to interrupt you there, Mike. I just want to say, when we consider the US economy on Bloomberg Technology in particular, it's often through the lens of infrastructure and that kind of end of the economy. But interestingly, specifically, when it comes to the labor market, you've been thinking about the gig economy and we're going to go very deep on that later in the program. So I just wondered if you'd outline your thesis for us. Yeah. So one of the things that we're really dealing with is, again, this sort of radical change 29 with Uber was the

advent of the app-based gig economy that initially targeted professional drivers in 2012. That shifted with the introduction of Lyft and Uber X to effectively allow anyone to use their car to make additional money, either as a side job or as a substitute for unemployment. The interesting thing that's happened is we haven't really had a normal cycle or period of unemployment. And one of the things that we're finding is that the gig economy is effectively a better destination for people than filing for unemployment claims. That would imply that the economy is actually the labor market is quite a bit weaker than we're getting in the headline data. And we're seeing this in sentiment surveys of consumers and households, but it's not showing up in the official data. One of the examples they give is California, for example, where if you file for unemployment, your maximum benefit is $450 a week for a maximum of 26 weeks. That puts you at half of the poverty level for a two-person household.

In contrast, if you already have a car and you're going to face the depreciation associated with it, driving for Uber allows you to replace a much larger fraction of your income by utilizing an asset that you are no longer using to commute to work, etc. That I think is really a key story that we're going to have to figure out the reason why it becomes an issue. And I want to emphasize, I think it's better that people are finding alternatives in the private sector, but those private sector alternatives have their own feedback loop, whereas more people find themselves driving for Uber simultaneously. The demand for these ridesharing services and gig economy applications begins to decline, and the competition for the revenues associated with them increases again. We're starting to see the signs of that that people who are driving for Uber are suddenly discovering that their incomes are beginning to fall significantly. So this is going to be a real challenge for our time.

Do we actually understand the data that's coming in, and particularly with central bankers choosing to try to be as data-dependent as they possibly can? That data can change very rapidly if we have a misunderstanding of the system.

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현재 칩 기업에 대한 열기는 긍정적인 거품으로 여겨져 중요한 분야에 대한 혁신과 투자를 촉진하고 있지만, 이러한 기업들이 높은 기대치를 충족시키지 못하면서 실망으로 이어질 수도 있습니다.

칩 용량에 대한 투자 활동은 미국과 유럽 모두에서 일어나고 있으며, 미국이 데이터센터 용량에서 상당한 선두를 유지하고 있습니다.

데이터센터의 효율성 향상으로 용량이 더욱 증가하여 한 지역에 대한 투자 우선순위를 정해야 할 필요성이 제기될 것으로 예상됩니다.

AI가 인프라 및 비즈니스에 미치는 영향

1990년대 후반의 다크 파이버 구축은 현재 AI 및 데이터센터 애플리케이션의 효율성 향상과 유사하며, 잠재적인 용량 초과 시기로 이어지고 있습니다.

유럽은 인프라 투자를 위한 더 나은 구축 환경을 제공하는 반면, AI의 영향은 금융 기관과 고객 지원을 포함한 다양한 산업에 혼란을 가져올 것으로 예상됩니다.

비즈니스 환경의 급격한 변화와 불확실성으로 인해 AI로 인한 혼란에 영향을 받는 기업의 선택권을 고려한 가격 책정이 필요합니다.

긱 경제가 노동 시장에 미치는 영향

대다수의 사람들이 이용 가능한 기회를 활용하기 위해 고군분투하고 있는 가운데, 고정 수입과 같은 분야에서 흥미로운 전망이 떠오르고 있습니다.

Uber와 같은 앱 기반 서비스가 주도하는 긱 경제는 노동 시장의 핵심 플레이어가 되어 기존의 실업수당 청구에 대한 대안을 제시하고 있습니다.

이러한 변화는 노동 시장이 공식 데이터에 나타난 것보다 더 취약할 수 있으며, 긱워크가 개인에게 더 나은 재정적 옵션을 제공할 수 있음을 시사합니다.

Uber 드라이버로 일하면 차량 감가상각을 상쇄하고 수입의 상당 부분을 대체할 수 있습니다.

Uber의 운전자가 동시에 증가하면 차량 공유 서비스에 대한 수요가 감소하여 경쟁이 심화되고 운전자의 소득이 감소합니다.

이러한 역학 관계는 중앙 은행의 의사 결정에 중요한 데이터와 그 빠른 변화를 이해하는 데 어려움을 줍니다.