Christian Dior’s $57 Handbags Have a Hidden Cost: Reputational Risk

Christian Dior struck gold when it found a supplier willing to assemble a €2,600 handbag, equivalent to around $2,816, for just €53 a piece—or did it? Cleaning up the reputational damage may not come cheap.

A Milan court named LVMH -owned Dior and Giorgio Armani as two brands whose products were made in sweatshop-like conditions in Italy. Images of an unkempt facility where designer handbags were produced, which was raided as part of an investigation into Italy’s fashion supply chain, are worlds apart from those the luxury industry likes to show its customers.

To keep up with the strong demand for their goods, some high-end brands rely on independent workshops to supplement their in-house factories. Sales at LVMH’s leather goods division have almost doubled since 2019.

While more outsourced manufacturing is understandable in a boom, brands may also have taken cost-saving measures too far in a push to juice profits. Some of Dior’s production was contracted out directly to a Chinese-run factory in Italy, where workers assembled the bags in unsafe conditions, according to a translated court order. In other instances, Dior’s suppliers subcontracted work out to low-cost factories that also used irregular labour.

Nipping the problem in the bud would require hundreds of millions of dollars worth of investment in new facilities to bring more manufacturing in-house. The alternative is for Dior to pay its suppliers more and keep them on a tighter leash. Either way, the result seems likely to be lower profits than shareholders have grown accustomed to.

Top luxury brands such as Christian Dior can have very high margins because consumers are willing to pay steep prices for goods they see as status symbols. They also can spread high fixed costs, such as expensive advertising campaigns over a large volume of sales.

For the LVMH group overall, the cost of making the products it sells—everything from Champagne to watches to cosmetics—amounted to 31% of sales in 2023. But the margins on big-brand handbags are probably at the high end of the spectrum.

Bernstein analyst Luca Solca estimates that a €10 billion luxury fashion label, roughly Dior’s size, may spend just 23% of its sales on the raw materials and labour that go into its products. This implies a €2,600 Dior purse would cost €598 to make, equivalent to $US647 for a roughly $US2,800 product at current exchange rates.

In reality, the cost may be even lower, based on the results of the Italian investigation. The €53-a-piece assembly price it cited, equivalent to around $US57, didn’t include the cost of the leather and hardware, but that would add only another €150 or so, according to one Italian supplier.

Advertising fees are a further €156 per handbag, according to Bernstein’s analysis, and depreciation of the company’s assets is €156. Running the brand’s stores—including paying the rent on some of the most exclusive shopping streets in the world—and head-office costs come to an additional €390. This leaves €1,300 of pure operating profit for Dior, or a 50% margin.

“This is the reality of the business,” says Solca. “The retail price for the goods of major luxury brands is typically between eight and 12 times the cost of making the product.”

LVMH hasn’t commented on the investigation, which first made headlines nearly a month ago. Meanwhile, a public-relations storm is brewing. Luxury influencers on social media are asking what exactly people are paying for when they shell out for a fancy purse. Recent price increases also make the cheap manufacturing costs hard to stomach. A mini Lady Dior bag that cost $3,500 in 2019 will set shoppers back $5,500 today, a 57% increase.

A dozen other luxury labels that remain unnamed are under investigation for similar issues in their Italian supply chains, so this may be a much wider problem.

Profits will take a hit if the industry decides to clean up its act. But the cost of doing nothing might be higher. Luxury brands that charge customers thousands of dollars and rely on a reputation for quality can’t afford to be cheap.

Artificial intelligence is known for its seemingly insatiable appetite for energy. But some tech leaders and analysts are questioning the extent of AI’s footprint going forward, saying innovations in the sector could help offset rising energy demand.

There is certainly no shortage of forecasts detailing the ominous rise in AI’s energy consumption. One commonly-cited report by Climate Action against Disinformation, an association of environmental groups, suggests that AI could drive up global emissions by 80%.

Another estimate by researcher Alex de Vries—which he described as a worst-case scenario—suggests that Google’s AI alone could eventually rack up as much annual electricity demand as the country of Ireland.

Such predictions present a substantial challenge for the relationship between computing and the climate in the coming years.

However, others see reason for optimism. In a Salesforce survey of about 500 corporate sustainability professionals, published Wednesday, nearly half were concerned about AI’s potential negative impacts on sustainability efforts. Meanwhile, almost 60% thought the benefits of AI would offset its risks in addressing the climate crisis.

Microsoft founder Bill Gates recently weighed in on the subject, urging governments not to go “overboard” on concerns about AI’s energy footprint, and suggesting that the technology could actually drive a reduction in global energy demands.

Putting the AI boom in context

The rise of AI should be considered in a broader perspective, according to Charles Boakye, U.S. sustainability analyst at Jefferies. He noted that relative to other industries, the technology still makes up only a fraction of global power demands.

“Data centers—the engines powering everything from AI to traditional computing to cryptocurrency—currently account for about 2% of global electricity consumption. Of this, AI accounts for roughly 0.5%,” Boakye said.

In terms of emissions, statistics last year from the International Energy Agency showed in total, data centers and transmission networks are responsible for 1% of energy-related greenhouse gases. For comparison, the oil-and-gas industry contributed just under 15% of emissions in 2022.

Looking ahead, the intergovernmental organisation said that by 2026, demand for AI is expected to increase ten fold compared with 2023. Even so, IEA data showed that it will still only account for roughly an eighth of total data-center electricity consumption.

“So in terms of AI demand, we’re talking about a small piece of a small piece,” Boakye said, noting that other areas of electrification, such as electric mobility, traditional data center growth and the industrial transition, will ask much greater questions of the power sector.

Charting efficiency trends

Demand is difficult to predict. But recent trends show that in practice, a rise in demand for computing power rarely correlates one for one with a rise in energy consumption, according to climate researcher Jonathan Koomey.

“Between 2010 and 2018, global data centres saw a 550% increase in compute instances and a 2,500% increase in storage capacity. This compares with just a 6% rise in electricity use,” he said.

Koomey, previously a visiting professor at Stanford, Yale and Berkeley, is best known for his work studying long-term trends in the energy-efficiency—now known as Koomey’s Law—highlighting the propensity of computing technologies to become more efficient over time.

AI may be a different animal, with some estimates suggesting large models such as ChatGPT use 10 times more energy than a Google search. But this is unsurprising for a relatively new technology, Koomey noted. In most areas of computing, energy demand tends to spike before levelling off as efficiencies gather pace, he said, noting that forecasts based on this inflection point will often be misguided.

Koomey cited a brief moment in the mid 1990s when internet data flows doubled every hundred days—a statistic that led to overinvestment in networks in the following years and 97% of fibre capacity sitting unused.

Similar efficiency trends can be seen in the development of AI, Jefferies analyst Boakye said. Google’s new TPU processors, for example, are more than 67% more efficient than in 2022, and the energy used to train OpenAI’s ChatGPT models has gone down by 350 times since 2016, he noted.

“It’s in their best interest, and in the interest of their business models, to increase that efficiency,” the analyst said.

Going for gold in the AI Olympics

If any company will have a say in the future of AI, it’s Nvidia . The U.S. technology company designs roughly 80% of the world’s specialized AI chips. Its next-generation GPUs, known as Blackwell, are touted to be 25 times more energy efficient than current iteration Hopper, while offering 30 times more computing power. Blackwell chips are slated for release later this year.

So far, the progress appears consistent, with Hopper 25 to 30 times more efficient than Nvidia’s previous generation of chips. Overall, the company said it has experienced a 45,000 times improvement in GPU energy efficiency over the last eight years.

Nvidia added that software optimisation also plays a big role in increasing the energy efficiency of its products.

“Once a platform has been launched, we will make it more efficient in a single year,” the company said. In the year following its launch in 2022, Hopper became two times more efficient after taking part in MLPerf, otherwise known as the Olympics of AI, in which tech companies compete and collaborate to drive improvements in the speed and efficiency of their models.

Part of this optimization involves taking large, energy-intensive AI models—such as ChatGPT—and refining them to perform more specific tasks. On July 18, for example, OpenAI launched a smaller, smarter and more energy efficient version of its previous GPT model, known as GPT-4o mini. Koomey sees these more “lightweight” models driving demand in the future .

“I’m not convinced large-scale AI has a good business model at this point, despite them driving all the investment. The slam dunk machine learning usually involves smaller, more efficient models, focused on a specific task. For me, this is where most of the business value lies,” he said.

Training and education will be essential for getting a more targeted and efficient experience with AI, helping to narrow the gap between businesses and their sustainability goals, said Suzanne DiBianca, Salesforce’s chief impact officer.

According to Nvidia, another way of alleviating AI’s impact is directing workloads—particularly the more energy-intensive jobs of training AI models—to regions with more abundant resources, ideally renewables which are set to keep a lid on electricity emissions in the coming years.

One company making strides in this space is NexGen Cloud, which builds renewable-powered data centers in areas with untapped energy resources.

According to co-founder and CSO Youlian Tzanev, a large portion of AI workloads don’t need to be performed close to traditional logistical hubs like London or New York, and can be powered from more remote areas in countries like Canada and Norway with excess hydropower.

“We have significantly more power than people believe. The power just isn’t reaching the grid in many cases and is going to waste, and so that is where we focus our efforts,” Tzanev said.

In the U.S., Crusoe Energy Systems offers another example of startups finding innovative ways to power the AI boom. Crusoe’s modular data centers are designed to run on excess natural gas produced at oil wells, achieving a 99.9% methane reduction in the process.

Common forecasting pitfalls

Projecting an outlook for AI’s energy demands is far from straightforward. In its midyear electricity update, the IEA noted that estimates exhibit a wide range of uncertainty, with some analyses following overly “simplistic” extrapolations.

For instance, the organization said certain studies make the mistake of assuming data center operators build all the facilities for which they apply to utilities. Given that several applications can be made for each new data center, this can lead to a multiplication of estimates.

Within data centers themselves, it is tempting for forecasters to imagine computers working flat out around the clock, Koomey said. In practice, GPUs usually operate on much less than their full power capacity, he added.

Based on modeling carried out by Nvidia, GPUs on average tend to run on less than 70% of their potential power. One particular function, known as Multi-Instance GPU, enables workloads—and therefore energy consumption—to be split into seven distinct components, with each able to function independently.

In addition, the company noted the role of substitution effects, in which traditional computing workloads are transferred onto AI platforms and subsequently performed more efficiently—an aspect that can be easily overlooked.

Forecasts can also conflate local data-centre developments with broader energy demands , Koomey added, noting that the most common estimates for electricity demand come from local utility companies.

In the U.S. as a whole, electricity use actually fell in 2023 compared with the previous year, according to the U.S. Energy Information Administration. In March 2024—the most recent month of available data—total demand reached 306 billion kilowatt-hours, down from 317 billion kWh in the year-prior period.

“I worry that people are jumping on the explosive demand-growth train before really understanding what’s going on,” Koomey said. “If you cluster data centers in certain places you’re going to see some local power constraints, but that doesn’t necessarily mean that AI will be a key driver of electricity use more broadly.”

Corrections & Amplifications undefined Nvidia said it has experienced a 45,000 times improvement in GPU energy efficiency over the last eight years. An earlier version of this article incorrectly said the company developed its first GPU eight years ago. (Corrected on July 24)

Christian Dior’s $57 Handbags Have a Hidden Cost: Reputational Risk

An Italian investigation is shining a harsh light on the supply chain of luxury brands

Nvidia says its machine-learning chips have become 45,000 times more energy efficient, with further improvements in the pipeline

Putting the AI boom in context

Charting efficiency trends

Going for gold in the AI Olympics

Common forecasting pitfalls