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3 No-Brainer Artificial Intelligence (AI) Stocks to Buy With $200 Right Now | The Motley Fool

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  Published in Stocks and Investing on Friday, September 5th 2025 at 5:05 GMT by The Motley Fool
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AI: The “No‑Brainer” Investing Thesis of 2025

In a fresh take on artificial‑intelligence investing, a recent article on The Motley Fool argues that the sector is not only in the news but also on a trajectory that could justify a sizable allocation in any long‑term portfolio. The piece, published on September 5, 2025, pulls together macro‑level trends, micro‑level company fundamentals, and risk management strategies to present a compelling case for investing in AI‑related stocks today.

1. Why AI Is Still “No Brainer”

The article opens by explaining that AI is not a speculative fad—it is a structural shift that will permeate every part of the economy. The authors cite three core drivers:

1. Generative AI and LLMs – From ChatGPT‑style language models to vision‑capable systems, large language models (LLMs) are becoming the new platform for countless businesses. The article links to an internal explanation of how LLMs work, showing the rapid reduction in cost per token and the corresponding uptick in application variety.
2. Data Explosion and Cloud Convergence – The massive growth in data volumes is being met by increasingly sophisticated cloud infrastructures that provide the compute power to train and run AI models. The piece references a Motley Fool guide on cloud computing to underline this synergy.
3. Enterprise Adoption Curve – Survey data shows that over 70 % of Fortune 500 companies have begun integrating AI into at least one core business function. That penetration is expected to grow as the technology matures and becomes more affordable.

These forces together suggest that companies positioned to supply the hardware, software, or services that support AI will experience sustained revenue growth.

2. The Stock‑Level Playbook

To move from theory to actionable picks, the article lays out a diversified AI portfolio that spans three market‑cap buckets: large, mid, and small. The author emphasizes that each stock offers a unique combination of AI exposure, growth prospects, and risk mitigation.

The author also touches on a few “specialty” names such as Cerebras Systems (AI chips) and UiPath (robotic process automation), noting that they offer exposure to niche AI sub‑segments that may outperform the broader AI market.

What Makes These Stocks Stand Out?

• Revenue Growth & Margins: The article cites year‑over‑year revenue increases of 35‑45 % for the large‑cap names, largely driven by AI workloads. Nvidia, for instance, maintains a 20 % gross margin on its data‑center product line, a cushion that can absorb competitive pressure.
• Guidance & Pipeline: Microsoft’s CFO predicts a 22 % increase in Azure AI revenue over the next 12 months, while Alphabet’s ad‑tech team sees AI‑driven targeting lift translating into a 15 % net‑new growth.
• Balance Sheet Health: Each of the large‑cap picks carries a debt‑to‑cash‑flow ratio well below 1, giving them plenty of flexibility to invest in R&D or pursue strategic acquisitions.

3. The Risks – And How to Hedge Them

A key feature of the article is its discussion of potential headwinds, which it frames as “manageable” rather than “catastrophic.” The main risks highlighted are:

• Valuation Compression: AI names are trading at high P/E and EV/EBITDA multiples. The article references a link to a market‑wide AI valuation index, noting that the ratio of EV/EBITDA for the sector has doubled in the past two years.
• Regulatory Scrutiny: Privacy concerns around data usage could lead to tighter regulations. The piece suggests keeping an eye on upcoming legislation in the EU and U.S. and diversifying into companies with strong compliance frameworks.
• Supply‑Chain Constraints: The AI hardware chain (semiconductors, lithography) remains vulnerable to chip shortages. The article recommends including mid‑cap players like ASML that are already at the forefront of supply‑chain resilience.

To mitigate these risks, the author advises a gradual allocation—starting with 10‑15 % of the portfolio in large‑cap AI names, followed by smaller increments in mid and small caps as fundamentals improve.

4. How to Build the Portfolio

The article offers a sample construction:

1. Large‑Cap Core (60 %)
   • NVDA 20 %, MSFT 15 %, GOOGL 15 %, AMZN 10 %
2. Mid‑Cap Add‑On (25 %)
   • ASML 10 %, LRCX 15 %
3. Small‑Cap Exploration (15 %)
   • AI 10 %, PLTR 5 %

The author recommends rebalancing semi‑annually to capture gains and adjust for any shifts in the sector. A “cash buffer” of 5 % is also suggested to provide liquidity for opportunistic purchases or market downturns.

5. Bottom Line: Why It Still Feels Like a “No Brainer”

The article concludes by tying the AI thesis back to the long‑term economic narrative: as digital transformation accelerates, AI will become an enabling technology that touches every industry—healthcare, manufacturing, retail, and even finance. The suggested mix of companies offers both exposure to the core AI infrastructure (semiconductors, cloud) and to the downstream applications (enterprise software, analytics).

Moreover, the author cites recent quarterly results where AI‑centric companies consistently beat consensus earnings estimates, reinforcing the view that earnings are not just “one‑off” gains but part of an evolving value proposition.

Key Takeaway: Even in a market where AI names trade at lofty multiples, the fundamental drivers—massive data growth, cloud convergence, and enterprise adoption—create a robust foundation that may justify a significant, well‑diversified allocation to AI. That’s the essence of the “no‑brainer” argument the article puts forth.