The Gig Economy and Its Impact on Labor Market Dynamics

The gig economy offers better income alternatives but faces rising competition and declining earnings for drivers.

### The Challenges of Investment in the Chip Sector and the Gig Economy's Evolution
* 💡 The current exuberance around chip companies reflects a "**positive bubble**," driving necessary innovation and investment, but may lead to disappointment as **expectations outpace actual returns**.
* 📉 Similar to the dot-com bubble, while the Internet transformed daily life, many companies failed to meet **inflated expectations**, indicating a potential pattern in the chip sector.
* 🌍 Investment in data centers is increasing in both the U.S. and Europe, with the U.S. **significantly ahead in capacity**, estimated at **roughly ten times** that of its closest competitor.
* 📊 Efficiency gains in data centers, driven by advancements in **algorithms for AI applications**, may lead to **excess capacity** in both regions.
* 🏗 Europe may present a better environment for construction due to differing privacy regulations, prompting **necessary investments in infrastructure**.
* 🚗 The gig economy, particularly through platforms like Uber, has emerged as a **more viable option for income** than traditional unemployment benefits, reflecting a **shift in labor market dynamics**.
* 📉 However, as more individuals enter the gig economy, competition increases, potentially leading to **declining incomes for drivers**, highlighting the **challenges of this evolving labor market**.
* 🔍 Understanding the rapidly changing data landscape is crucial, especially for central bankers who rely on this information to **guide economic decisions**.

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.

Starting to See Efficiency Gains From AI: Simplify's Green

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Bloomberg Technology

The current enthusiasm for chip companies is seen as a positive bubble, driving innovation and investment in a crucial space, but it may lead to disappointment as these companies struggle to meet high expectations.

The investment activity in chip capacity is occurring both in the US and Europe, with the US maintaining a significant lead in data center capacity.

The efficiency gains in data centers are expected to increase their capacity even further, challenging the need to prioritize investment in one region over the other.

The Impact of AI on Infrastructure and Business

The build-out of dark fiber in the late 1990s parallels the current efficiency gains from AI and data center applications, leading to a potential period of excess capacity.

Europe offers a better construction environment for infrastructure investment, while AI's impact is expected to disrupt various industries, including financial institutions and customer support.

The rapid changes and uncertainty in the business landscape necessitate pricing in the optionality of companies affected by AI's disruption.

The Impact of the Gig Economy on the Labor Market

The majority of people are struggling to capitalize on available opportunities, while interesting prospects are emerging in areas like fixed income.

The gig economy, driven by app-based services like Uber, has become a key player in the labor market, offering an alternative to traditional unemployment claims.

This shift suggests that the labor market may be weaker than official data indicates, with gig work providing a better financial option for individuals.

Driving for Uber can help individuals offset car depreciation and replace a larger portion of their income.

The simultaneous increase in drivers for Uber leads to a decline in demand for ridesharing services, resulting in increased competition and falling incomes for drivers.

This dynamic presents a challenge for understanding the data and its rapid changes, which is crucial for central bankers' decision-making.