Building on my post from yesterday....

(https://getqu.in/xnw3fe/)

in this post I would like to write more about the transition from AI infrastructure expansion to the inference market, taking into account my personal assessment and using some sources. The stocks mentioned do not constitute investment advice. As always, the lines are intended to provide impulses to understand the potential winners of tomorrow.

In recent years and currently, a large proportion of investment has gone into building the infrastructure for artificial intelligence.

Tech giants and cloud providers have built huge data centers to enable the training phase of AI models such as GPT-4 or Gemini [1].

However, a paradigm shift is now emerging: the focus is shifting from expensive training to the inference market, i.e. the practical use and monetization of AI models, as general AI models will eventually become a "commodity".

When we say that AI is becoming a commodity, this means that artificial intelligence is increasingly becoming a standard technology that is readily available and accessible to many companies and industries, similar to electricity, the internet or cloud computing today.

Standardization and availability

• AI solutions are being standardized to such an extent that companies can use them without in-depth specialist knowledge.
• Instead of creating individually developed AI systems, companies are turning to ready-made solutions or platforms (e.g. AI models, APIs or services from companies such as OpenAI, Google or Microsoft).

Lower costs

• Economies of scale and technological advances are making AI systems cheaper and therefore affordable for more companies.
• Cloud providers such as AWS, Azure or Google Cloud offer AI tools as pay-as-you-go services, which lowers the barriers to entry for smaller companies.

Wide range of applications

• AI is used in all kinds of industries, from logistics and finance to healthcare and agriculture.
• Technologies such as image and speech processing, automated decision-making and data analysis are becoming the basis for innovation.

Loss of competitive advantages

• When AI is equally available to all companies, those that used to be leaders will lose their unique competitive advantage.
• The difference will no longer lie in the use of AI itself, but in the efficient and creative application of the technology.

Long-term significance

• AI as a commodity will lead to a democratization of technology where every company has access to the same basic tools.
• Competition will shift to who uses AI better, rather than who owns or develops it.
• At the same time, new differentiators could emerge, such as industry-specific customization or unique data.

In practice, this means that AI is no longer the exclusive domain of tech giants, but a tool that anyone can use to drive innovation.

📈 Why the shift to the inference market is inevitable

From training to inference: What's the difference?

Training:

AI models are trained with enormous amounts of data, a computationally intensive process that requires specialized hardware such as GPUs and TPUs. Companies such as Nvidia, AMD and Broadcom are benefiting greatly from the boom in data centers at this stage.

Inference:

Once training is complete, the models must be used in the real world, whether through chatbots, voice assistants or image processing. Efficiency is key here, as AI has to respond to millions of requests in real time.

Capital shift: the money follows the monetization

Infrastructure development completed:

Building training infrastructure is costly, but after a certain phase, the focus shifts to optimization and more efficient use over frugal training or specialized chips.

Energy efficiency and cost optimization:

Companies are looking for solutions that are not only powerful but also energy-efficient and cost-effective, a crucial factor in maximizing margins.

🔮 The growth market of the future

The inference market is expanding rapidly as AI applications penetrate more and more areas of life and work:

Automated customer service:

Chatbots and virtual assistants are replacing traditional call centers and offer round-the-clock support (e.g. IBM watsonx Assistant) [2].

Medical diagnostics:

AI-supported image analyses improve diagnostics and enable personalized therapies [3].

Language models & generative AI:

Applications such as ChatGPT or Google Bard are revolutionizing the field of generative AI.

Industry 4.0 & automation:

Smart factories and predictive maintenance increase efficiency in production [4].

🏆 The profiteers of the inference market - how are the Big Techs positioned?

📌 AMD
$AMD (+0,36 %) Nvidia's challenger in the inference sector

Technology:

• AMD's MI300X is considered a direct competitor to Nvidia's H100 and offers advantages in terms of energy efficiency and costs [5].

Market outlook:

• With a strong positioning in the cloud and data center sector, AMD could gain market share in the growing inferencing market

Nvidia $NVDA (-0,47 %) From training king to inference king?

Dominance in the training market:

• Nvidia has dominated the training sector to date with its A100 and H100 GPUs [6].

Switch to inference mode:

• With new software optimizations such as TensorRT and specialized hardware solutions such as the Grace Hopper superchips, Nvidia is preparing to be a leader in the inference sector as well.

Google
$GOOGL (-0,87 %) TPUs as the inference solution of the future

Own AI chips:

• Google relies on its own Tensor Processing Units (TPUs) to reduce inference costs in the cloud [7].

Range of applications:

• Optimized inference chips play an essential role in Google Cloud as well as YouTube AI recommendations.

Amazon
$AMZN (-0,74 %) The cloud giant with its own AI hardware

Inferentia chips:

• Amazon's specially developed Inferentia chips are designed for the cost-effective operation of AI inference solutions [8].

Market penetration:

• With a steadily growing number of AWS customers using customized AI inference solutions, Amazon remains a key player in the AI sector.

Microsoft
$MSFT (-0,64 %) Profiteer through OpenAI & Azure AI

Partnership with OpenAI:

• The close cooperation with OpenAI positions Microsoft as one of the largest providers of inference solutions.

Azure AI:

• With Azure AI, Microsoft is building a powerful cloud infrastructure for generative AI.

Future developments:

• In-house AI chips could reduce dependence on external providers such as Nvidia in the long term.

Broadcom
$AVGO (+0,23 %) The "hidden champion" for inference networks

Network technology:

• Broadcom's high-speed networking technology enables the fast data processing that is critical for AI inference.

Market Leadership:

• As a leading provider of networking chips in hyperscale data centers, Broadcom benefits from the growing demand for AI-optimized solutions.

Qualcomm
$QCOM (+0,2 %) Inference on the edge market

Edge Computing:

• In addition to the cloud, inference is also needed on mobile devices and IoT systems. Qualcomm offers powerful, energy-efficient AI chips for this.

Smartphone applications:

• In particular, the use of Snapdragon AI processors in smartphones makes Qualcomm an important player in the edge sector.

🎯 Conclusion: the inference market could be the new AI gold rush

The AI boom is far from over, it's just shifting. While the expansion of infrastructure laid the foundation, the focus is now on continuous revenue from inference solutions.

• Companies that specialize in energy-efficient and high-performance inference hardware have long-term growth potential.

• Providers with their own AI solutions can benefit massively by reducing costs and improving margins.

• Companies like Broadcom provide the technological foundation for fast and reliable AI inference.

The inference market has long been a reality and is not a short-term phenomenon.

❓Which companies do you see benefiting alongside the big players?

In yesterday's post, I briefly outlined my personal portfolio breakdown on the topic of the AI revolution.

Thanks for reading! 🤝

__________

Sources:

[1]

https://datacentremagazine.com/it/gartner-why-global-it-spending-will-hit-us-5-61tn-in-2025

[2]

https://www.ibm.com/de-de/products/watsonx-assistant?utm_content=SRCWW&p1=Search&p4=43700081249081072&p5=e&p9=58700008827051052&gclsrc=aw.ds&gad_source=1&gbraid=0AAAAA-oK3j5oGHzEFRXtK5errGcJ_JurZ&gclid=CjwKCAiAnpy9BhAkEiwA-P8N4u6i5OsTL36ctZDn93JgG58kV90ckf7U6o2QKeKPSUqqwHSw3MxoaRoC2IwQAvD_BwE

[3]

https://www.mckinsey.com/featured-insights/themes/whats-next-in-ai-and-healthcare

[4] https://www.deloitte.com/de/de/issues/innovation-ai/industrie-40.html

[5] https://www.amd.com/de/products/accelerators/instinct/mi300/mi300x.html

[6]

https://www.nvidia.com/en-us/data-center/

[7]

https://cloud.google.com/tpu

[8]

https://aws.amazon.com/de/ai/machine-learning/inferentia/

Hello everyone! I am in my early 20s and have now decided to open a portfolio, naturally with a slightly smaller investment than some of the others here. I would like to build up a relatively stable portfolio that generates steady returns but doesn't involve too much risk. I have decided on the dip of $SOL (-2,52 %) and $ETH (-2,3 %) but I regret it a little because I have the feeling that the 25% weighting in crypto is too high. So my question is:

What do you think can be adjusted? Do you have any tips? If I reduce the crypto position, where should I reinvest? (Maybe I was thinking of $AMZN (-0,74 %)
$GOOGL (-0,87 %) or $MSFT (-0,64 %) what do you guys think?)

If you have anything to say, let me know!

Best regards and thanks in advance!

$MSFT (-0,64 %)

More than 10.00% of my portfolio is invested in Microsoft between direct stocks and ETFs. The company dominates the cloud with Azure, drives innovation in AI through OpenAI, and continues to generate record profits. So far it has been a winning bet, but is having so much capital concentrated on one stock a sustainable strategy or a risk? Is it better to take profit and diversify or to hold on to a big tech stock that seems unstoppable? How much do you count on the growth of big companies in the long run? Do you prefer the security of diversification or bet big on market leaders? Tell me your strategy and how you balance risk and opportunity in your investments!

#Investimenti
#Microsoft
#StockMarket
#Azioni
#ETF
#Diversificazione
#BigTech
#AI
#Azure
#Portafoglio
#FinanzaPersonale
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#Investire

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 (-0,64 %) : Operator of Azure Cloud and partner of OpenAI
• Alphabet $GOOGL (-0,87 %) : With Google Cloud and DeepMind
• Amazon $AMZN (-0,74 %) : AWS, the world's leading cloud provider
• Meta $META (+0,37 %) : Develops its own AI chips and continues to expand its infrastructure

In addition, specialized data center providers such as Equinix $EQIX (-1,15 %) and Digital Realty $DLR (-1,94 %) 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 (-1,98 %) : Precise cooling technology and air conditioning for data centers
• Vertiv Holdings $VRT (-2,25 %) : 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 (+2,02 %) : Leader in chips for energy efficiency and thermal management. Solutions reduce power consumption in data centers and support AI integration
• Texas Instrumentes $TXN (-0,74 %) : Energy-saving semiconductor products used in data center servers
• Equinix $EQIX (-1,15 %) : Specialized in data center infrastructure
• Digital Realty $DLR (-1,94 %) : Provider of physical infrastructure for data centers
• IBM $IBM (+1,56 %) : Quantum computing technologies that potentially consume less energy and development of energy-efficient AI solutions
• Arista Networks $ANET (-3,03 %) : Specialist in high-speed networking products for data centers
• Nvidia $NVDA (-0,47 %) : Leader in AI GPUs, Leader in AI training market. Best choice for large AI models and data center training
• AMD $AMD (+0,36 %) 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 (+0,23 %) Profits from network solutions for data centers
• Microsoft $MSFT (-0,64 %) Google $GOOGL (-0,87 %) Amazon $AMZN (-0,74 %) : The big hyperscalers investing heavily in AI and cloud

European stocks e.g.:

• Siemens Energy $ENR (-0,09 %) Important role in modernizing power grids, integrating renewable energies and improving storage solutions for data center reliability
• Schneider Electric $SU (+0,3 %) : Leader in the development of energy management and cooling technology for data centers - specialty in the automation of both systems.
• ASML $ASML (+1,03 %) : Indispensable for modern chip production
• Infineon $IFX (+0,58 %) and STMicroelectronics $STM (+0,45 %) : Leading semiconductor companies with a focus on AI applications
• RWE $RWE (-0,22 %) and Enel $ENEL (-0,58 %) Utilities that are increasingly focusing on renewable energies for data centers

Japanese stocks e.g:

• Daikin Industries $6367 (-1,2 %) 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 (-0,15 %) : Important supplier for semiconductor manufacturing
• Mitsubishi Heavy Industries $7011 (-1,27 %) : 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 (-0,56 %) (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 (-1,2 %) share in order to increase the exposure to Japan and the share price offers an entry point at first glance.

In addition, AMD $AMD (+0,36 %) 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 (+1,5 %) 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

__________

Podcast episode 75 "Buy High. Sell Low."

Subscribe to the podcast to help Palantir break $200.

00:00:00 Infineon $IFX (+0,58 %)

00:46:00 Microsoft $MSFT (-0,64 %)

00:58:20 Alphabet $GOOG (-0,89 %)
$GOOGL (-0,87 %)

01:17:40 Palantir $PLTR (-4,22 %)

01:53:00 Dirt of the week: Meta $META (+0,37 %)

Spotify

https://open.spotify.com/episode/3oBWsRFxjlXSoXdTsrk0Pa?si=VhrZm1X3Qx-NQ9g47OffXg

YouTube

https://youtu.be/r2YVY5mAE30

Apple Podcast

https://podcasts.apple.com/us/podcast/folge-75-palantir-alphabet-meta-microsoft-infineon/id1695869891?i=1000689579333

Hello, I am thinking about restructuring my portfolio, which in my opinion is very unbalanced, with a view to the future. I'm thinking of an investment horizon of around 15-20 years.

I would like to structure the core of my portfolio with the help of 60% #etfs with the help of

• $VWRL (-0,34 %) 30%
• $CSPX (-0,41 %) 30%

In addition, around 5% of my postage portfolio is to be #crypto above all $BTC (-2,72 %) consist of

I invest the remaining 35% in individual stocks.

• Technology sector 8%
• Thought of $MSFT (-0,64 %) , $GOOGL (-0,87 %) , $AMZN (-0,74 %) , $SAP (+2,6 %) ,$NVDA (-0,47 %) in approximately equal parts
• Healthcare sector 4%
• Something like $JNJ (+0,82 %) , $NOVN (+0,45 %) , $NOVO B (-1,51 %) but I'm not quite sure here yet, I want to read up on it in more detail
• Consumer sector 6%
• Thought of $COKE (-0,73 %) , $1810 (-3,06 %)
  $PG (+0,26 %) but not sure yet either
• Industrial sector 5%
• Something like $6501 (+0,29 %)
  $SIE (+1,5 %)
• 
  Energy sector 7%
• $SHEL (+1,76 %) , $ENR (-0,09 %)
• 
  Financial sector
• $ALV (+0,03 %) and $NU (-1,03 %)

I currently have a monthly savings rate of 400-500 euros in addition to my studies and would divide this up accordingly and put it into a #sparplan and put it into a savings account.

What do you think of the weighting and do you have any tips, especially in the health sector?

Brief feedback is very welcome

#stockanalysis
#portfolio

The USA has 45.6% of all data centers worldwide. By 2030, US energy demand will account for 8% of US electricity. China continues to struggle with chip manufacturing due to trade bans on high technology.

$AMD (+0,36 %) , $NVDA (-0,47 %)
$IREN (-4,7 %) , $ASML (+1,03 %)
$TSM (+0,25 %) , $AMZN (-0,74 %) , $MSFT (-0,64 %) , $GOOG (-0,89 %)

The first month of the new year is already over. While the first half of January was characterized by Trump euphoria and rising markets, the second half was much more volatile, particularly due to DeepSeek and its impact on the AI sector.

For me personally, the savings rate was unfortunately somewhat lower due to the issue of house building, as already mentioned at the end of 2024. Currently (and probably also for the coming months) it will therefore only be around €500-700.

I am aware that this is of course still a very high savings rate, but it is significantly lower than the ~€2,000 of recent years.

Also very exciting: In November, I withdrew over €20,000 from my custody account to raise the equity for the bank. Just 2 months later, my custody account is back to the same value of almost €300,000.

But now it's finally time to look at the hard figures:

In total, January was +3,9%. This corresponds to price gains of ~11.000€.

The MSCI World (benchmark) was +3.5% and the S&P500 +2.7%

Winners & losers:

A look at the winners and losers shows an unusual picture in January:

The biggest loser by far is NVIDIA! With price losses of almost €3,000, the emergence of DeepSeek caused substantial losses in the portfolio.

The other losers, on the other hand, are only worth a side note. After NVIDIA, Apple has already lost significantly less with ~€400 losses. The remaining loser stocks then only lost around €200.

On the winners' side at the top with a total of almost €2,000 in price gains is the Meta share. Followed by Bitcoin with €1,500 price gains and 2 other tech stocks: Alphabet with €1,200 and Crowdstrike with €1,000. 5th place goes to Starbucks, also with gains of almost €1,000.

The performance-neutral movements were clearly negative in January, as some of the cash was used to buy the property. The property is of course also an asset, but I would rather focus on liquid assets here.

The performance-neutral movements in relation to my portfolio were ~€600

Dividend:

• The dividends in January were 30% above the previous year at ~€166
• TSMC is in the lead with over 30€ (gross) dividend every 3 months.

Buys & Sells:

• I bought in November for significantly reduced ~600€
• As always, my savings plans were executed:
• Blue ChipsMicrosoft $MSFT (-0,64 %) Stryker $SYK (-1,04 %) Apple $AAPL (+1,94 %) Home Depot $HD (-0,33 %) Starbucks $SBUX (-0,76 %) Texas Instruments $TXN (-0,74 %) Nike $NKE (-0,22 %) Novo Nordisk $NOVO B (-1,51 %) Lockheed Martin $LMT (-0,05 %) LVMH $MC (+0,45 %)
• 
  Growth: -
• ETFsMSCI World $XDWD and the WisdomTree Global Quality Dividend Growth $GGRP
• Crypto: Bitcoin $BTC and Ethereum $ETH
• Sales -

YouTube:

In mid-January, I uploaded my first video to YouTube. I am particularly proud of a video explaining my dividend strategy. The video has already reached almost 200 views and a playback time of ~10 hours!

Even though I'm still not really sure what exactly I want to do on YouTube, I really enjoy editing videos and talking about shares.

That's why I created my January portfolio update as a video for the first time. Of course I would love to hear your feedback!

https://youtu.be/SD8ZKVWbUGA

Target 2025

Building a house makes it particularly difficult to formulate a goal this year.

A certain savings rate? Difficult if additional costs are suddenly added

A certain deposit value? Also difficult, as I can't really back this up with my savings rate this year.

Nevertheless, I would formulate the following goal for this year:

Current portfolio balance is €300,000. I would estimate additional investments at ~€15,000. With an average market return of ~7%, that would be price gains of ~€21,000.

This would put my portfolio at €330,000 to €340,000 at the end of 2025.

-> I would therefore aim for a portfolio value of well over €300,000, so that a 2% fluctuation does not lead directly back below €300k.

#dividends

#dividende

#rückblick

#depotupdate

#aktie

#stocks

#etfs

#crypto

#personalstrategy

The financial markets have provided us with some exciting developments this week. Today's newsletter brings you the most important events in a compact and understandable way:

From DeepSeekthe Chinese AI start-up that has surprised the technology sector with an innovative approach, to the latest interest rate decisions of the central banks, to the mixed quarterly quarterly figures from Tesla - it was a week full of headlines.

Enjoy reading!

DeepSeek: China's new AI player and its consequences

The Chinese AI start-up DeepSeek has caused quite a stir with its new model DeepSeek R1. The AI chatbot, launched on January 20, is now one of the most downloaded apps worldwide. In the USA, it currently occupies 1st place in the iPhone app storeeven ahead of ChatGPT or Google's Gemini. The app is also one of the top downloads in Germany. Users can chat with the AI, upload files or carry out web searches - and the basic version is free.

What makes it special: DeepSeek's chatbot is said to require significantly less computing power than comparable models from large US companies such as OpenAI or Google $GOOGL (-0,87 %) . According to reports, around 2,000 Nvidia H800 graphics processors were used to train DeepSeek-R1, resulting in costs of around 6 million US dollars. By comparison, OpenAI invested around USD 100 million in the development of GPT-4 in 2023. This efficiency calls into question the previous assumption that high-performance AI models necessarily require investments in the billions and specialized high-performance chips. If these figures prove to be true, this could increase the pressure on US companies to rethink their cost structures and investment strategies.

This surprising development and the associated uncertainty triggered a significant reaction on the stock market on Monday. The US tech index Nasdaq 100 lost around 3%, and Nvidia in particular $NVDA (-0,47 %) - the leading provider of chips for AI applications, which has benefited enormously from the AI trend in recent years - fell by almost 17%. Companies such as Broadcom $AVGO (+0,23 %) , AMD $AMD (+0,36 %) , ASML $ASML (+1,03 %) and Microsoft $MSFT (-0,64 %) also came under pressure in the meantime. Most tech stocks stabilized somewhat over the course of the week. This is because some experts are questioning the transparency of DeepSeek's data from China and are urging caution when assessing the actual performance and efficiency of the model. It is already becoming apparent that DeepSeek's actual cost advantages could be lower than initially assumed - further developments remain to be seen.

What you as an investor can learn from the DeepSeek turmoil:

• #1: Don't panic - markets often tend to exaggerate in both directions: Tech stocks are sensitive to sensationalized headlines. Share prices can move rapidly up or down in the short term, regardless of the substance of the news. Sharp price losses happen again and again - and are often followed by a quick recovery. It is important to remain calm and not be tempted to sell rashly.

• # 2: Corrections bring opportunities: Sharp price falls like those on Monday often offer good buying opportunities - especially for quality companies with long-term potential and reasonable valuations, e.g. ASML $ASML (+1,03 %) . As long as companies are fundamentally sound, short-term setbacks are usually no cause for concern.

• #3: AI competition is getting tougher - this is positive for innovation and adoption: DeepSeek shows that AI development does not necessarily have to be extremely expensive. More competition and the supposedly new, innovative approach could lead to AI models becoming even more efficient and widely available.

• #4: The technology sector is constantly changing: The technology sector is one of the most rapidly changing markets. Companies that are considered market leaders today can be challenged or displaced by new innovations within a few years. As an investor, you should therefore regularly check whether your technology investments will remain competitive in the long term. Do you only have quality companies in your portfolio with sustainable competitive advantages and attractive future prospects? Or is their market position crumbling, e.g. due to the introduction of new technologies?

Interest rate decisions: What the ECB and Fed mean for the market

This week there were two landmark interest rate decisions - one in Europe, one in the USA.

A brief reminder: Developments and expectations regarding key interest rates are a decisive factor for the stock market. Falling interest rates make loans cheaper and boost investment and consumption. This often causes share prices to rise. Rising interest rates, on the other hand, slow down growth and make alternative investments such as bonds more attractive - this can put pressure on shares.

• ECB lowers key interest rate to 2.75 percent - potential stimulus for the economy: The European Central Bank (ECB) has lowered its key interest rate for the fifth time since the monetary policy turnaround at the end of 2023. The monetary authorities decided to lower the deposit rate by 0.25 percentage points to 2.75%. ECB chief Christine Lagarde justified the move with the weakening economy. The EU statistics office reported zero growth in the last quarter of 2024, with growth of just 0.7% for the year as a whole. The aim of the interest rate cut: cheaper loans for companies and private individuals to boost investment and consumption.

• US Federal Reserve remains firm - no interest rate cut (yet): While the ECB is cutting interest rates, the US Federal Reserve (Fed) is standing firm. Despite political pressure from Donald Trump, the Fed has decided to leave the key interest rate in the range of 4.25% to 4.50%. The reason: the US economy remains strong - growth is continuing and inflation has not yet been conquered. Fed Chairman Jerome Powell made it clear that a premature interest rate cut could jeopardize the fight against rising prices.

Hot phase of the reporting season for many companies in the S&P 500

We are currently in the middle of the phase in which many US companies publish their figures for the last quarter. The following chart from Fundstrat provides an overview of the results so far and how they have been received by the market:

As of Wednesday this week, 103 of the 500 companies in the S&P 500 have reported their quarterly results for Q4 2024. 79% of the companies have beaten exceeded earnings estimateswhich indicates an overall strong operating performance. The positive surprise factor of 6.1% shows that companies are performing better than analysts had expected. The S&P 500 is up 3.2% since 12/31/2024, also indicating positive market sentiment. The solid earnings season so far therefore appears to be boosting investor confidence in corporate earnings.

The reports and forecasts of some heavyweights such as Meta $META (+0,37 %) , ASML $ASML (+1,03 %) , Netflix $NFLX (-1,56 %) , Mastercard $MA (+0,07 %) and Intuitive Surgical $ISRG (-1,15 %) were well received by investors mostly well well received by investors. Microsoft $MSFT (-0,64 %) on the other hand, showed a rather subdued share price reaction to the results presented and the short-term outlook. Tesla Tesla $TSLA (-6,72 %) a company in my portfolio, also presented its figures this week for Q4 2024 with mixed results. The most important findings are summarized here in a compact and understandable way:

• Tesla has in 2024 around 1.8 million vehicles delivered in 2024 - an increase of 36% compared to 2022, but no further increase compared to 2023. With sales of 25.7 billion US dollars in Q4, Tesla achieved a slight increase of 2% compared to the previous year, but still fell short of analysts' high expectations. Margins and profits also fell due to price cuts and financing options. The positive ray of hope was once again the solar systems and energy storage division, where sales doubled to more than USD 3 billion and are expected to grow by >50% growth in 2025. Another positive aspect is the rising cash position, which now stands at USD 36 billion - as far as I know, only the big tech companies such as Alphabet (Google), Amazon, Microsoft, Meta (formerly Facebook), Apple and Nvidia have a higher value.

• Despite the mixed results, Tesla shares remained surprisingly stable after CEO Elon Musk reiterated his vision for the coming years. Tesla announced that from June 2025 a fully autonomous driving service without a human driver in Austin, Texas, from June 2025 - and is expected to roll it out in other US cities by the end of the year. Cheaper e-models are planned for the first half of 2025 with the aim of making electric mobility accessible to even more people. Tesla also plans to produce several thousand of the humanoid robot "Optimus" and put them to good use in its own factories. Ideally, the first robots should be sold to external customers by the end of 2026 - an idea that is arousing the curiosity of many, as it could be used not only in companies but also in private households in the future.

To put things in context: The company is 2024/25 in a transition phase between two growth cycles. The first phase began at the end of the 2010s with the scaling of electric cars, while a new boost is expected from 2026/27/28 due to the introduction of autonomous driving and humanoid robots.

Finally, some noteworthy quotes from the earnings call from Elon Musk - as always, to be interpreted with caution. Tesla's current high valuation is not based on measurable sales and earnings growth, but above all on ambitious plans for the future, which are fraught with uncertainty:

• At the Launch of autonomous driving in the USA: "We live at this unbelievable inflection point in human history. We're going to be launching unsupervised full self-driving as a paid service in Austin in June."

• On the scenario of other car manufacturers licensing Tesla's software license "At this point, we're seeing significant interest from a number of major car companies about licensing Tesla full self-driving technology."

• The prospect of Tesla becoming the most valuable company in the world in the long term: "I've said this before and I'll stand by it. I see a path of Tesla being the most valuable company in the world by far. There is a path where Tesla is worth more than the next top five companies combined. It's a difficult path but it is an achievable path. And that is overwhelmingly due to autonomous vehicles and autonomous humanoid robots."

• Musk also gave a few details on the development and production of the humanoid robot"The normal internal plan calls for roughly 10,000 Optimus robots to be built this year. So the current line that we're designing is for roughly 1,000 units a month. The next line would be for 10,000 units a month. The line after that would be for 100,000 units a month. My very rough guess is that we start delivering Optimus robots to companies that are outside of Tesla in maybe the second half of next year. I'm confident at 1 million units a year, that the production cost of Optimus will be less than $20,000."

• On the Significance of the year 2025 in Tesla's company history: "2025 really is a pivotal year for Tesla. And when you go look back on 2025 and the launch of unsupervised full self-driving, I think we may regard it as the most important year in Tesla's history. There is no company in the world that is as good at real-world AI as Tesla. I don't even know who's in second place."

I bought my last Tesla shares in August 2024 at €196 - I only plan to buy more when the valuation becomes more realistic and attractive again. I consider a fair valuation to be a price/sales ratio in the range of 8-12x (currently 13x), below that an entry would be much more interesting.

Thank you for reading.

Stay informed and invest wisely.