AI stocks: 3 investment layers of AI world stocks

Artificial Intelligence (AI) is no longer science fiction. It is an economic reality that is transforming industries from healthcare and finance to manufacturing and transport. AI stocks are the hottest investment theme of recent years, offering huge potential but also hiding considerable speculative risk.

For the investor, it is critical to understand the three layers of the AI ecosystem in order to distinguish sustained innovation from short-term hype and to ensure smart portfolio diversification. This article will help you understand the AI background, European regulations and practical investment strategies.

1. What is AI really and why is it a "megatrend"?

In the context of investing in AI, this means not just creating a new robot, but a system that can analyse huge amounts of data, make predictions and perform cognitive tasks.

A. Data is the new fuel

The growth of AI depends on three components: computing power (semiconductors), algorithms (software) and, most importantly. Details. Every digital activity creates data that feeds AI models. This is why the growth of AI is exponential.

B. The three investment phases of AI

The AI investment universe is not uniform:

1. Infrastructure and Hardware (Infrastructure): Companies that provide the computing power needed for AI (GPUs, dedicated chips, servers). (Semi-conductors concerned. Article from: Nvidia, AMD, TSMC).
2. Cloud Platforms and Services: Companies that provide a platform (cloud services) to develop and run AI models (Microsoft Azure, Google Cloud, AWS).
3. Applications and Software (Application Layer): Companies creating specific AI-based tools (e.g. healthcare diagnostics, financial risk assessment, marketing platforms).

2. AI transformation and economic impact

The most profound impact of AI will be seen in the surge in efficiency and productivity of traditional, slow-moving industries. This is not just a technology, but a fundamental transformation of the economy.

A. Productivity and cost optimisation

AI not only replaces low-complexity jobs, but also optimises complex processes on an industrial scale. In logistics, for example, AI-based systems can predict supply chain bottlenecks and optimise inventory, saving large amounts of capital. In manufacturing, AI can monitor equipment wear and predict failure times (predictive maintenance), reducing downtime. For the investor, this means that traditional companies (e.g. in transport or manufacturing) that successfully integrate AI will see their margins and profitability increase even if their turnover remains the same. This is an implicit but stable investment model.

B. Ethics, regulation and trust

The mass adoption of AI brings major challenges in terms of data protection, privacy and algorithmic fairness (e.g. bias in the assessment of loan applications). The European Union is at the forefront of these risks in the world with its EC AI legislation. This regulatory development is a significant investment risk, but also an opportunity. Companies that can demonstrate transparency and compliance with the EU's stringent standards have a strong competitive advantage in a market that values trust and stability. Investors should give preference to software companies that build their systems on ethical and transparent foundations, as future regulations may otherwise hamper their business models.

3. Infrastructure and the Cloud: big and stable bets

The first two layers are the most stable and predictable investments in AI development.

A. The role of cloud platforms

Most of the AI development is done by Amazon (AWS), Microsoft (Azure) and Google (GCP) cloud platforms. Regardless of whether one AI model is more successful than another, each developer pays these giants for the data processing.

• An advantage for investors: The shares of these companies offer an indirect and diversified contribution to the growth of AI, without having to pick one specific AI software winner. This is an AI "shovel and scoop" investment.

B. Specialised infrastructure shares

In addition to GPUs, it is also important to pay attention to companies involved in building data centres and optimising energy consumption. Training AI requires huge amounts of electricity, making energy infrastructure and data centre stocks a significant indirect beneficiary.

4. Application layer: high risk, high potential

The most exciting but also the riskiest investments are at the application layer.

A. Software nichification

These are companies that create specialised solutions using AI:

• Healthcare: AI models that help diagnose diseases early (e.g. cancer) or optimise the drug discovery process.
• Financial: Risk management software that assesses more complex credit risks or automates trading.
• Customer service: Sophisticated chatbots and automated customer relationship management.
• Warning: This layer is full of small startup-businesses, most of which will never grow to profitability. An investor must be prepared to take risks by small contribution to many different companies, in the hope of catching "one horn".

B. Europe's position in the AI market

Europe has AI hardware (ASML) and the application layer market is strong, but lags behind the US giants in Cloud Platforms.

• EU AI law (EU AI Act): Europe is at the forefront of AI regulation, creating a framework for the ethical and transparent use of AI. While this will bring regulatory costs at the outset, in the longer term it could give European software companies a competitive advantage. credibility and ethics field. By investing in European AI companies, you are indirectly supporting this regulatory framework.

5. AI-hype risks and how to manage them

The frenzy around AI hype, which has pushed the values of many shares speculatively high.

A. Valuation risk

Historically, every revolutionary technology (internet, 3D printing, dot-com boom) has gone through a phase where investment expectations exceed real returns. Many AI stocks today are extremely expensive (P/E ratio is high) and based on Future profit expectations.

• Strategy: Avoid only the largest hype participants. Add indirect AI beneficiaries to the portfolio (cloud, data centres, cybersecurity) where the returns are more realistic.

B. Replacement risk

AI software is evolving so fast that today's market leader could tomorrow be replaced by a new, better and open-source model. This is particularly prevalent at the application layer level.

• Strategy: Diversify your investments through an AI ETF. Such funds include hundreds of companies related to the AI sector, hedging the risk that an individual company's share price will fall due to the rapid replacement of technology.

C. Cybersecurity as an indirect beneficiary of AI

As more and more critical decisions are made by AI, the need for cyber protection of AI systems is exploding. Cybersecurity companies that integrate AI into their products (and protect AI) are also important indirect beneficiaries of the AI megatrend.

Summary: How to create an AI-proof portfolio

Artificial intelligence is undoubtedly the biggest investment opportunity of the 21st century, but it is full of volatility and speculative money.

1. Spread between layers: Don't invest only in the most sex kitchens in the shares of the application layer. Add stable stocks to your portfolio Infrastructure and cloud services shares.
2. The advantage of an ETF: Unless you are a specialist who can assess the technological success of individual AI software companies, buy AI sector ETFs.
3. Look to Europe: While the US dominates in cloud services, Europe is strong in AI-related hardware and new ethical software. Add ASML and other European players to your portfolio to strike a balance.

AI development is a marathon, not a sprint. Invest for the long term, and choose a strategy that avoids short term risk hype-falling victim to noise.