$VRT (+0,39%) is for me, like Schneider Electric, one of the winners of the AI decade. How do you see it?

Today, after a long period of consideration, I have decided to part with $CDI (-0,77%) ... I have learned that it can also make sense to realize losses.

I now want to invest the money in shares that are better suited to my dividend growth strategy and show more growth. I am thinking of companies like $V (+1,12%)
$ZTS (-0,98%)
$NOVO B (+0,14%)
$6383 (-0,45%)
$VST (-0,89%)
$VRT (+0,39%)

Please share your opinion!

Hello everyone, AI is becoming more and more important in our lives. However, this also means that massive investments continue to be made.

I see AI glasses $QCOM (-0,18%) further ahead.

$NVDA (-0,22%) makes a lot of things possible here.

In terms of infrastructure, I see $VRT (+0,39%) and $IESC (+1%) as one of the beneficiaries.

Cooling of data centers $MOD (-3,37%) and $FIX (-0,35%) .

And as mentioned in the report

$META (+0,06%)
$GOOG (-0,71%)
$MSFT (-0,38%)
$AAPL (+1,41%)

Meta brings the AI functions of its sunglasses to Europe, Gemini's live image recognition becomes accessible to all, and Grok also gets virtual eyes

First we chatted with the AI bots, then we talked to them - and if the manufacturers have their way, we will also show them our surroundings in future so that they can comment on them. What was previously a niche program is now set to find its way into the masses. And in Europe too.

Following the European launch of Meta AI, the Facebook group Meta is now also offering the smart functions of its Rayban sunglasses, which are already available in the USA, in various European countries, including Austria. Initially, it is possible to ask the sunglasses equipped with Meta AI a question by voice input, and the answer is then output via the sunglasses' small speakers. In the near future, the glasses' integrated cameras will also be used to ask questions about visible objects in the environment.

But it's not just Meta, Google is also expanding its potential user base for a similar feature. All Android users will be able to use the image recognition and screen sharing function in Gemini Live free of charge, which was previously reserved for owners of Pixel 9 or Galaxy S25 smartphones. Here, too, the AI answers questions about things it sees via the camera or on the screen. And Elon Musk's Grok now also has digital eyes, albeit only via the iPhone app for the time being.

Of course, where there is light, there is also shadow, and once again this means data protection. Concerns have been expressed, particularly with regard to Meta's smart Rayban glasses, that people could not only be filmed but also identified by the AI. This is not science fiction: in the fall of 2024, Harvard students were already able to determine the personal data of strangers in real time using commercially available Meta smart glasses.

All eyes on Google, efforts at Apple

Another topic that has caused a stir in recent days: the antitrust proceedings against Google. And in this case, an eye is also being cast on the topic of AI, as the company could use the technology to further strengthen its position. Among other things, Google is said to be paying "enormous sums of money" to Samsung to have Gemini installed as the standard AI app on its smartphones. Should the Google business be split up, an AI competitor has already signaled its interest: OpenAI would like to take over the Chrome browser.

In this context, it was made public that Gemini now has 350 million monthly and 35 million daily active users. In October, it still had nine million daily active users and 90 million monthly active users. Despite the growth, however, the figures are still lower than those of the competition: Meta AI has 500 million monthly users, while OpenAI has 400 million.

However, Google has also attracted the attention of competition authorities on this side of the Atlantic. The EU Commission is investigating Google's AI overviews after publishers recorded losses in traffic and advertising revenue following their introduction.

These are problems that Apple does not have: There, they are making more of an effort to catch up. One of the ways in which the new Siri engineering boss Mike Rockwell is hoping to achieve this is by bringing people into the team who previously worked on the Vision Pro headset. Some of the once promised features of Apple Intelligence are still awaited. Previously, Apple had advertised AI functions in commercials that did not even exist, but after various complaints, the company is now much more cautious with its promises.

New tools and features

In terms of new features and tools, one of the things we can look forward to this week is that Google Workspace will receive components from NotebookLM. People with Gemini Advanced accounts can use Google's video AI Veo 2 in Gemini. Developers can test Google's hybrid reasoning model Gemini 2.5 Flash.

Microsoft, on the other hand, wants to integrate Copilot into the Xbox app. OpenAI has entered into a partnership with the Washington Post and is incorporating their content into ChatGPT. Sam Altman's team also enables developers to integrate OpenAI's image generator into their own applications via API. The o3 and o4-mini versions can now determine exactly where a particular photo was taken.

There is also good news from European providers. For example, the Swedish start-up Lovable, which specializes in vibe coding, has released version 2.0 of its tool, which even non-experts with no coding experience can use to build simple applications. Among other things, the tool now includes more options for cooperation and security checks.

There is also news from Paris. It was only in the fall that Thomas Wolf, Chief Science Officer (CSO) at Hugging Face, told me that, in addition to providing AI models and applications, the platform also wanted to get involved in the field of robotics, and now these plans are becoming more concrete: With the acquisition of Pollen Robotics, the French want to offer open source robots, namely the software as well as construction plans and 3D models.

(Jan 25 (Reuters) - The cloud computing arm of Alphabet Inc (GOOGL. O), opens new tab, announced on Thursday that it had entered into a partnership with startup Hugging Face, to facilitate the development of artificial intelligence (AI) software on the company's Google Cloud.25.01.2024)

https://www.derstandard.de/story/3000000266847/ki-google-chrome-chatgpt

is a global company that focuses on the development, manufacture and maintenance of critical digital infrastructure technology. The company provides solutions for data centers, communications networks, and commercial and industrial environments. Vertiv offers a broad portfolio of products and services, including energy management, thermal management and IT management.

I have been waiting for a long time to get into Vertiv Holdings $VRT (+0,39%) - and now the first position has been opened. With a bit of luck, the rocket will now take off and take me to the ATH 🚀🌜

Market cap USD 33 billion

Forward P/E ratio 20

Fine cash flow and soon no old liabilities

• $VRT (+0,39%) Data center infrastructure provider Vertiv (VRT) significantly exceeded Wall Street's expectations for the fourth quarter on Wednesday.

• However, its earnings guidance was a bit weak, while revenue came in above estimates.

• The Westerville, Ohio-based company posted adjusted earnings of 99 cents per share on revenue of $2.35 billion in the December quarter. Analysts polled by FactSet had expected earnings of 82 cents per share on sales of 2.16 billion dollars.

• Year-over-year, Vertiv's earnings rose 77%, while revenue increased 26%.

• For the current quarter, Vertiv expects adjusted earnings of 60 cents per share on revenue of $1.93 billion. This is based on the midpoint of the forecast. Wall Street had forecast earnings of 63 cents per share on sales of 1.92 billion dollars.

Forecast 2025

• For the full year 2025, Vertiv is targeting adjusted earnings of 3.55 dollars per share on revenue of 9.2 billion dollars, based on the midpoint of the forecast. Analysts had expected earnings of 3.57 dollars per share on revenue of 9.13 billion dollars.

Next week won't be boring either 🫡

There's something for everyone 😁

What numbers are you looking forward to? 🧐

$MCD (-0,09%)
$VRTX (-0,53%)
$KO (-0,17%)
$SPGI (+0,88%)
$VRT (+0,39%)
$PANW (+1,28%)
$CVS (-0,1%)

$MEDP (+0,43%)
$ACLS (-1,38%)
$THC (+0,34%)
$TTD (+3,09%)
$CSCO (+0,63%)
$APP (+3,07%)
$HOOD (+14,66%)
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$ABNB (-1,85%)

Graphic: AI generated

The rapid development of AI has triggered a veritable boom in data centers. Companies such as OpenAI and DeepSeek are driving this revolution and the demand for high-performance servers is growing exponentially.

However, the increase in computing power is also accompanied by massive energy consumption, an issue that is leading to global discussions about infrastructure, efficiency and future investments [1].

At the same time, the question arises as to whether there is currently an overinvestment in computing power. The Chinese AI company DeepSeek, for example, has presented a model that works more efficiently than previous large language models (LLMs).

Does this mean that we will soon need less computing power?

Or will the Jevons paradox occur instead, i.e. the effect that more efficient technologies actually increase overall consumption in the long term? [2, 3]

In this article, I will focus on the key developments in the data center sector, the growing demand for energy, regional characteristics, current challenges and potential investment opportunities.

As always, the article is intended to shed light on the background to current events, provide food for thought and give impetus. The stocks mentioned do not, of course, constitute investment advice.

🤖 Data centers: the foundation of the AI revolution

The growing global demand for AI-supported software and digital applications requires powerful data centers. Goldman Sachs analysts forecast that the global demand for power from data centers will increase by 50% by 2027 and by up to 165 % could increase [1].

This chart below forecasts the energy consumption of data centers (in terawatt hours) by 2030, distinguishing between AI- and non-AI-based applications in the US and the rest of the world. Total consumption is expected to rise to over 1,000 TWh by 2030 [4].

Our analysts expect data center power consumption to increase by more than 160% by 2030

Source: [4], primary: Masanet et al. (2020), Cisco, IEA, Goldman Sachs Research

This data shows how AI applications will massively increase energy consumption. The rapid increase in the area of "US AI" and "Rest of world AI" is particularly striking.

The three main reasons for this increase are

• Larger AI models:

New models such as GPT-5 or DeepSeek AI require more and more computing power. Training and operating these models requires trillions of calculations [1].

• Real-time AI applications:

Companies are integrating AI into numerous applications: from search engines to personalized financial and healthcare services.

• Cloud computing & data storage:

As digitalization progresses, the global demand for data storage and cloud services is increasing [1].

Which companies dominate the market?

On the demand side for data centers, large hyperscale cloud providers and other companies are building large language models (LLMs) that are capable of processing and understanding natural language. These models need to be trained on huge amounts of information using power-intensive processors [4].

On the supply side, hyperscale cloud companies, data center operators and asset managers are deploying large amounts of capital to build new high-capacity data centers.

These include, among others:

• Microsoft $MSFT (-0,38%) : Operator of Azure Cloud and partner of OpenAI
• Alphabet $GOOGL (-1,18%) : With Google Cloud and DeepMind
• Amazon $AMZN (-1,65%) : AWS, the world's leading cloud provider
• Meta $META (+0,06%) : Develops its own AI chips and continues to expand its infrastructure

In addition, specialized data center providers such as Equinix $EQIX (+0,42%) and Digital Realty $DLR (+1,21%) as they supply physical infrastructure to the hyperscalers [6].

According to Goldman Sachs Research, demand for data center infrastructure will increasingly outstrip supply in the coming years.

The utilization rate of existing data centers is expected to rise from around 85% in 2023 to more than 95% by the end of 2026. However, the situation is expected to ease from 2027 onwards as new data centers are commissioned and demand growth driven by AI slows down (see chart below) [1].

Goldman Sachs currently estimates that the global power consumption of the data center market is around 55 gigawatts (GW). This is made up of cloud computing workloads (54%), traditional workloads such as email or data storage (32%) and AI (14%) [1].

For the future, analysts predict that electricity demand will increase to 84 GW by 2027. The share of AI is expected to grow to 27%, while the cloud share will fall to 50% and traditional workloads to 23% [1].

By the end of 2030, around 122 gigawatts (GW) of data center capacity will be online.

At this point, I asked myself as a layman how the units mentioned so far are to be understood, in my first graphic I speak of 1,000 TWh of energy consumption of all data centers by 2030 and now here is talk of 122 GW of data center capacity? In order not to go completely beyond the scope of the article, I have added a section at the very end in case some of you also feel like a layman and want to put the "units" into perspective.

... and now on with the article...

One central problem remains:
Where does all the energy come from?

⚡️Energieversorgung: Can the grid keep up?

According to estimates by Goldman Sachs, more than 720 billion US dollars will have to be invested in expanding the power grid worldwide by 2030 in order to supply the new data centers with sufficient energy [1].

Europe in particular, where electricity consumption was expected to decline for many years, is experiencing a veritable "demand shock" [1].

Which energy sources supply data centers?

• Natural gas & battery storage:

Natural gas is seen as a realistic short-term solution to meet continuous demand. It serves as a bridging technology until renewable energy and storage solutions are further developed, as renewable energy is not available around the clock [4].

• Renewable energies:

Wind and solar energy could cover around 80 % of demand in the long term, provided that sufficient storage solutions are integrated [4].

In practice, solar plants run on average only about 6 hours per day, while wind power plants run on average 9 hours per day. There is also a daily volatility in the capacity of these sources, depending on the radiation of the sun and the strength of the wind [4].

The graph shows the fluctuations in capacity factors for wind and solar energy in the USA in 2023. The capacity factor indicates how efficiently an energy source utilizes its maximum output throughout the year.

• Wind energy (light blue line): The highest capacity factors occur in the winter months (Jan-March) and drop significantly in the summer months (Jun-Aug).

• Solar energy (dark blue line): Efficiency rises in the spring (Mar-May) and reaches its maximum in the summer months (Jun-Aug) before falling in the winter (Nov-Dec).

The graph illustrates that wind and solar energy can complement each other seasonally: While wind is more efficient in winter, solar energy provides the highest yields in summer. This shows how important a balanced energy mix is to ensure security of supply.

In addition to finding environmentally friendly energy sources to power data centers, technology providers can reduce emissions intensity through efficiency gains.

The following chart shows the development of the workload and energy consumption of data centers between 2015 and 2023. Although the workload almost tripled, energy consumption remained almost constant until 2019 thanks to efficiency gains. The efficiency gains then slowed down from 2020 onwards.

Source: [4], primary: Masanet et al. (2020), IEA, Cisco, Goldman Sachs Research

This chart supports the discussion on the Jevons paradox (see below). Efficiency gains could be offset or even exceeded in the long term by higher workloads and AI demand. This highlights the need to make data center energy sources more sustainable.

• Nuclear energy:

Meanwhile, governments are also becoming more supportive of nuclear energy on the whole. Switzerland is reconsidering the use of nuclear generators for its electricity supply, while nuclear energy enjoys bipartisan support in the US and the Australian opposition party has put forward plans to introduce nuclear reactors [4].

Participants at the COP28 conference at the end of 2023, an annual summit convened by the United Nations to combat climate change, agreed to triple global nuclear capacity by 2050 [4].

Nuclear energy is considered the ideal option for basic power supply as it provides a reliable and constant supply of energy.

As a result, more and more large tech companies such as Alphabet, Amazon and Microsoft are turning to small modular nuclear power plants (SMRs).

📊 Increasing efficiency & the Jevons paradox

With new technologies such as DeepSeek, AI could work more efficiently in the future. But does greater efficiency automatically mean that less computing power is required?

The Jevons paradox: More efficiency = more consumption?

The Jevons paradox describes the fact that increases in efficiency often do not lead to lower consumption, but to higher consumption overall.

-Example:

In the 19th century, more efficient steam engines did not lead to lower coal consumption; on the contrary, as the machines became cheaper and more versatile, coal consumption actually increased.

With cars: more fuel-efficient engines did not lead to less gasoline consumption, but to people driving more cars.

-Applied to AI:

As AI models become more efficient, the cost per computation decreases. This makes AI applications attractive in even more areas, which in turn leads to a higher overall demand for computing power.

🌎 Regional distribution and global expansion of data center infrastructure

Current distribution: Where are the data centers located today?

Today, most data centers are located in the Asia-Pacific region and North America. Well-known locations are:

North America:

- Northern Virginia

- San Francisco Bay Area

Asia: Beijing

- Beijing

- Shanghai

These regions are characterized by high computing power, intensive data traffic and strong demand from corporate campuses [1].

The chart also shows the historical development of data center capacity by region (North America, APAC, etc.) from 2017 to 2024. The figures illustrate how fast the infrastructure for the AI revolution is growing and underlines why the energy requirements of data centers are increasing so rapidly.

The increase in capacity from around 20 GW in 2017 to almost 60 GW in 2024 shows an enormous growth trend. This correlates directly with the increasing demand for AI applications and cloud computing.

How is supply growing?

Goldman Sachs Research estimates that global data center capacity will increase to around 122 GW by the end of 2030, as mentioned above. The share of hyperscalers and specialized operators will increase from the current 60% to around 70% [1].

• Asia-Pacific:

The largest expansion of data centers has been recorded here in the past ten years.

• North America:

The largest expansion of new data centers is planned in North America over the next five years.

📈 Investment opportunities: Some winners of the AI and data center revolution

US shares e.g.:

• Carrier Global $CARR (-1,24%) : Precise cooling technology and air conditioning for data centers
• Vertiv Holdings $VRT (+0,39%) : Specialist in cooling and power solutions specifically for data centers
• Brookfield Renewable Partners $BEP.UN : Leading provider of renewable energy (hydropower, solar, wind) - supply contracts (PPAs) with data centers
• ON Semiconductor $ON (-2,05%) : Leader in chips for energy efficiency and thermal management. Solutions reduce power consumption in data centers and support AI integration
• Texas Instrumentes $TXN (-0,26%) : Energy-saving semiconductor products used in data center servers
• Equinix $EQIX (+0,42%) : Specialized in data center infrastructure
• Digital Realty $DLR (+1,21%) : Provider of physical infrastructure for data centers
• IBM $IBM (+1,04%) : Quantum computing technologies that potentially consume less energy and development of energy-efficient AI solutions
• Arista Networks $ANET (+1,78%) : Specialist in high-speed networking products for data centers
• Nvidia $NVDA (-0,22%) : Leader in AI GPUs, Leader in AI training market. Best choice for large AI models and data center training
• AMD $AMD (-1,88%) Competing with Nvidia with its own AI chips, but better positioned in the AI interference market where energy efficiency and cost-effectiveness are key. The interference market will be the next most important market, perhaps even the more important one.
• Broadcom $AVGO (+0,58%) Profits from network solutions for data centers
• Microsoft $MSFT (-0,38%) Google $GOOGL (-1,18%) Amazon $AMZN (-1,65%) : The big hyperscalers investing heavily in AI and cloud

European stocks e.g.:

• Siemens Energy $ENR (+3,12%) Important role in modernizing power grids, integrating renewable energies and improving storage solutions for data center reliability
• Schneider Electric $SU (-1,46%) : Leader in the development of energy management and cooling technology for data centers - specialty in the automation of both systems.
• ASML $ASML (+0,2%) : Indispensable for modern chip production
• Infineon $IFX (+1,57%) and STMicroelectronics $STM (+0,78%) : Leading semiconductor companies with a focus on AI applications
• RWE $RWE (-0,48%) and Enel $ENEL (-0,04%) Utilities that are increasingly focusing on renewable energies for data centers

Japanese stocks e.g:

• Daikin Industries $6367 (+3,48%) World market leader in air conditioning and cooling, offers specialized cooling systems for data centers and AI-supported plant management systems to further increase efficiency
• Tokyo Electron $8035 (-0,7%) : Important supplier for semiconductor manufacturing
• Mitsubishi Heavy Industries $7011 (-1,48%) : Works on the development of new nuclear power plants to secure the energy supply

🧠 Conclusion: AI, data centers & energy as the trend of the century?

Although some analysts warn of possible overinvestment, the figures indicate that the demand for computing power and energy for AI data centers will continue to rise sharply.

• Efficiency gains from models such as DeepSeek or new chip technologies could reduce energy consumption per computer, but the Jevons paradox means that overall demand will increase because more efficient systems will be used more often.

The biggest winners are therefore:

• Semiconductor companies: They supply the AI chips needed.
• Data center operators: They build the necessary infrastructure.
• Energy suppliers: They ensure the energy supply for the AI revolution.

In the long term, these companies could be among the biggest beneficiaries of the coming decades.

👨🏽‍💻 How do I position myself?

Personally, I think I am well positioned with the NASDAQ 100 $CSNDX (-0,13%) (portfolio share of 23%), as the focus is on US technology and growth stocks. The ETF complements my All-World with a stronger weighting in innovative sectors such as AI and cloud computing.

In the near future, I will also take a closer look at Daikin Industrie $6367 (+3,48%) share in order to increase the exposure to Japan and the share price offers an entry point at first glance.

In addition, AMD $AMD (-1,88%) has also caught my attention, the reason being its positioning in the aforementioned interference market. Most of the capital is currently flowing into the expansion of new AI models. However, as soon as these become a "commodity" and everyone uses them, most of the capital will probably flow into the interference market (market for the application of AI models).

Furthermore, I have Siemens AG $SIE (-2%) with approx. 2.3% portfolio share (still growing to approx. 4%), which I also see as well positioned for the future for the following reasons (in the context of the article):

Network stability

• Develops technologies for intelligent power grids ("smart grids"), essential for integrating renewable energies into the supply of data centers.

Data center control

• Provides automation and monitoring systems that optimize the energy consumption and efficiency of data centers

Efficient building structure

• The "Smart Infrastructure" division supports data centers with energy-efficient solutions for lighting, air conditioning and building monitoring

Not directly cooling systems, but:

• offers technologies that increase the energy efficiency of cooling systems by optimizing energy flows and data analysis

What is your opinion❓

• Which companies do you have on your radar?
• Is there a threat of overinvestment or are we just at the beginning of a century revolution?

Thanks for reading! 🤝

...Said digression follows after the sources...

__________

Sources:

[1] https://www.goldmansachs.com/insights/articles/ai-to-drive-165-increase-in-data-center-power-demand-by-2030

[2] "The Coal Question"
http://digamo.free.fr/peart96.pdf

[3] https://de.m.wikipedia.org/wiki/Jevons-Paradoxon

[4] https://www.goldmansachs.com/insights/articles/is-nuclear-energy-the-answer-to-ai-data-centers-power-consumption

[5] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

[6] https://www.cbre.com/insights/reports/global-data-center-trends-2024

__________

🧭 Digression: on gigawatts and terawatt hours

In order to understand the relationship between the two figures, 122 GW (gigawatts) and 1,000 TWh (terawatt hours), it is important to clarify the units and their meaning:

• 122 GW (gigawatts):

Refers to the current average power capacity that data centers worldwide require to function. Power (measured in GW) describes the amount of energy consumed per second. This is therefore a snapshot of energy requirements.

• 1,000 TWh (terawatt hours):

This is an indication of energy consumption over a certain period of time, in this case one year. It describes how much energy is required in total in 12 months.

The forecast of 1,000 TWh is slightly below the value resulting from the calculation. The graph shows values slightly above 1,000 TWh; according to the calculation based on 122 GW of power capacity, energy consumption should be around 1,069 TWh.

Nevertheless, general reasons for deviations may be as follows:

• Efficiency improvements: Data centers could operate more efficiently through improved cooling, optimized hardware and software and thus consume less energy.
• Peak vs. average consumption: The figure of 122 GW could reflect peak demand, while the actual average annual demand is somewhat lower.
• Adjustments to the model: It is possible that the forecast of 1,000 TWh is conservative and does not take into account all additional loads or regional differences.

This shows how much the demand for data centers and energy will increase due to AI and digitalization by 2030

__________