A functional organization helps Apple innovate

 HBR: 

SUMMARY:

• THE CHALLENGE: Major companies competing in many industries struggle to stay abreast of rapidly changing technologies.  
• ONE MAJOR CAUSE: They are typically organized into business units, each with its own set of functions.  Thus the key decision makers—the unit leaders—lack a deep understanding of all  the domains that answer to them.
• THE APPLE MODEL: The company is organized around functions, and expertise aligns with decision rights. Leaders are cross-functionally collaborative and deeply knowledgeable about details.

As companies grow, they often switch from a functional organization to a divisional one:

Business history and organizational theory make the case that as entrepreneurial firms grow large and complex, they must shift from a functional to a multidivisional structure to align accountability and control and prevent the congestion that occurs when countless decisions flow up the org chart to the very top.

But, you end up with general managers who lack technical expertise making decisions.  Instead:

...Apple relies on a structure that centers on functional expertise. Its fundamental belief is that those with the most expertise and experience in a domain should have decision rights for that domain. This is based on two views: First, Apple competes in markets where the rates of technological change and disruption are high, so it must rely on the judgment and intuition of people with deep knowledge of the technologies responsible for disruption. Long before it can get market feedback and solid market forecasts, the company must make bets about which technologies and designs are likely to succeed in smartphones, computers, and so on. Relying on technical experts rather than general managers increases the odds that those bets will pay off. 

Example:  Apple puts cameras in iPhones, computers, laptops, and iPads.  

...Apple’s more than 600 experts on camera hardware technology work in a group led by Graham Townsend, a camera expert. Because iPhones, iPads, laptops, and desktop computers all include cameras, these experts would be scattered across product lines if Apple were organized in business units. That would dilute their collective expertise, reducing their power to solve problems and generate and refine innovations.

What happens when functional areas disagree?

The answer is collaborative debate. Because no function is responsible for a product or a service on its own, cross-functional collaboration is crucial. When debates reach an impasse, as some inevitably do, higher-level managers weigh in as tiebreakers, including at times the CEO and the senior VPs. To do this at speed with sufficient attention to detail is challenging for even the best of leaders, making it all the more important that the company fill many senior positions from within the ranks of its VPs, who have experience in Apple’s way of operating. 

BOTTOM LINE:

APPLE’S FUNCTIONAL ORGANIZATION is rare, if not unique, among very large companies. It flies in the face of prevailing management theory that companies should be reorganized into divisions and business units as they become large. But something vital gets lost in a shift to business units: the alignment of decision rights with expertise. 

Why do companies so often cling to having general managers in charge of business units? One reason, we believe, is that making the change is difficult. It entails overcoming inertia, reallocating power among managers, changing an individual-oriented incentive system, and learning new ways of collaborating. That is daunting when a company already faces huge external challenges. An intermediate step may be to cultivate the experts-leading-experts model even within a business unit structure. For example, when filling the next senior management role, pick someone with deep expertise in that area as opposed to someone who might make the best general manager. But a full-fledged transformation requires that leaders also transition to a functional organization. Apple’s track record proves that the rewards may justify the risks. Its approach can produce extraordinary results.  

Supply and Demand for Data Centers

A recent Visual Capitalist Info-graphic displays a fair amount of agglomeration of data centers. Not only does Virginia (!) lead, but many other states (IL, OH, TX, OR) have more data centers than their population or level of economic activity would suggest. The general pattern aligns with Fan and Greenstein's analysis that proximity to customers is important, even if it raises costs due to higher land prices in urban areas. Additionally, data centers' demand for electricity will tend to lead them to states with lower energy prices. The ability to achieve economies of scale also appear to be important, which also would be more expensive in urban areas. Different suppliers locating near to each other suggests that the advantages scale fall on the industry, not just the larger firm. This could be due to a more robust labor market for data center talent.

VPPs Change the Scale of Energy Production

 

Advances in Virtual Power Plants (VPPs) illustrate how changes in Minimum Efficient Scale (MES) can reshape industry structure. Traditionally, electricity generation exhibited a high MES: firms needed large, capital-intensive plants to achieve low average costs, reinforcing concentration. VPPs lower MES by allowing thousands of small, distributed assets, such as rooftop solar, batteries, smart appliances, and EVs, to be aggregated through software and operated as a single, dispatchable resource. Because efficiency now comes from coordination and data rather than plant size, firms can enter electricity markets without owning large-scale generation assets. When MES falls relative to market demand, entry becomes easier and market structure shifts toward greater competition and fragmentation.

Not only are VPPs an example of how lower MES in physical assets reduces market power, they also relocate it. While generation-scale economies decline, new scale economies emerge in aggregation, customer acquisition, data analytics, and platform integration. As a result, competition increasingly resembles a platform market, where firms that control large networks of enrolled devices or superior optimization software can achieve cost and reliability advantages even without owning physical capacity. Declining MES at the asset level intensifies entry and rivalry, while increasing MES at the coordination layer may create strategic bottlenecks that generate a potential winner-take-most outcome.

Why is the Venezuelan stock market up over 100% following US hostilities?

Students for Liberty:

Under Maduro's "socialism of the 21st century," ... Venezuelans resorted to eating dogs and scavenging trash to survive.

  Marginal Revolution:  In expected value terms, the people of Venezuela are now much better off.

A tale of two cities and rent control

WSJ: What the Twin Cities Tell Us About Fixing the Housing Crisis

The Natural Experiment:

• In 2022, St. Paul enacted one of the strictest rent-control regimes in the country. The ordinance capped annual rent increases at 3% for most apartments, even empty ones. It didn’t adjust for inflation. ... 
• Across the Mississippi River, Minneapolis steered clear of rent control. Instead, city officials strictly focused on creating new housing.

Results:

• [In St Paul]:  Real-estate investment activity nearly froze. Developers halted new projects as lenders pulled back....Property values declined as investment cooled. 
• In Minneapolis:  ...developers kept building. Housing permits surged nearly fourfold in early 2022 from the year before. Downtown hubs blossomed as new apartments hit the market and attracted young professionals.

BOTTOM LINE:  In the short run, a rent ceiling create excess demand for, or shortage of available apartments.  In the long-run, it reduces supply, thereby exacerbating the shortage.   

President Trump vs. Economists on Tariff Predictions

WSJ: Why Everyone Got Trump’s Tariffs Wrong 

Economists were right that Tariffs would push up inflation (it went up slightly) and have little effect on the trade deficit (it didn't move much).  President Trump was right when he predicted they would raise a lot of money.

European Pensions are in bad shape

Economist:  

Europe’s fastest-ageing countries also already offer some of the most generous pensions and lowest retirement ages. The average French retiree now spends 23 years drawing a pension, longer than in any other OECD country (see chart below). In Denmark, by contrast, pensioners draw one for 19 years on average. Its government plans to raise the retirement age from 67 to 70 by 2040, which would be the highest in Europe.

Arbitrage Fueled by Data Analytics

Reuters reports that Williams-Sonoma recently sued "dupe" producer, Quince, for an unfair comparison. Dupe producers include online retailers who make copies of popular high-end items that often include high margins. 

"By brand-washing its ads, Quince creates the false impression that consumers will receive comparable quality and design, when in reality they may be purchasing unrelated items of often inferior quality," Williams-Sonoma said.

Quince seems to have struck a nerve. It mostly sells apparel targeted to women aged 25 to 55 and women’s business accounts for about 80% of its sales. Quince uses data analytics to determine what is hot at other retailers and then scours the customer reviews for areas of improvements. “Things like, where are the pockets, what zipper, quality of the hardware,” Of course, it is implementing AI to speed up production of its dupes. Fashion is especially vulnerable to this sort of arbitrage because the IP protections generally do not apply.

Streaming Merger Market Definition

 

One aspect of the antitrust review of the Warner Brothers Discovery (WBD) merger with Netflix (or Paramount) will be what constitutes the relevant market. Eric Fruits provides a nice explanation of the issues over on "Truth on the Market." It essentially boils down to whether a narrow "streaming services" definition is used versus a broader "screen time" definition that includes recreational Internet scrolling and maybe video gaming. The market would be quite concentrated under the former definition and quite a bit less so under the latter. This is an empirical question over the extent to which consumers substitute their time between various screen content. 

I happen to have some experience with time use data from the ATUS from some of my past research. These data are amazing with a quarter million "diary days" covering every day since 2003. Other nice things about these data are that they are publicly available and have consistent definitions over almost a quarter century. Among other activities, these data include time spent watching TV, playing games (mostly video games), and "recreational computer" usage. A major problem with ATUS for screen time measurement is that, because it was setup before smartphones were a thing, there no good way of measuring time spent looking at your smartphone while you are doing something else. The amount of computer time in ATUS is a fraction of time on smartphones reported elsewhere. So I spent the morning seeing how time spent on these three activities related to each other. My quick and dirty analysis indicates that each minute playing games decreases TV time by 4 seconds [P<0.01] while each minute "recreating" with a computer decreases TV time by 8 seconds [P<0.01].* If you confine the sample to just the past 10 years, you get slightly more time diversion. This is evidence suggesting that consumers do substitute between television and other screen time.

Surely the parties, whoever they end up being, will have more granular proprietary data yielding better analyses. 

 

*This analysis includes fixed effects for age category by sex and year by sex and uses ATUS's weights. Interpreting these correlations as causal is problematic. Most of the variation is likely to come from ever better video games and ever more Internet activities (e.g., YouTube, Facebook, TikTok, etc) which would suggest a causal interpretation. But this analysis is merely conditional correlations.

Econ makes you right -eaning, clear-thinking, and wealthier

 From Marginal Revolution:

• studying social sciences and humanities makes students more left-leaning, whereas studying economics and business makes them more right-leaning. 
•  the rightward effects of economics and business are driven by positions on economic issues, whereas the leftward effects of humanities and social sciences are driven by cultural ones. 
• humanities and social sciences increase activism, while economics and business increase the emphasis on financial success.