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🎥  Mr. FIRED Up Wealth: Top AI Investing Strategies

Best Stocks to Buy Now EXPERT Reveals Top AI Investing Strategies with Ivana Delevska

🎥 Video 🧧 Investing 💰 Money 🌲 Evergreen

🎥  Mr. FIRED Up Wealth: Best Stocks to Buy Now EXPERT Reveals Top AI Investing Strategies with Ivana Delevska

VIDEO INFORMATION

Best Stocks to Buy Now: EXPERT Reveals Top AI Investing Strategies

Fired Up Wealth

Ivana Delevska
Approximately 60 minutes

HOOK

While most investors chase the next AI sensation without understanding what they own, the most successful approach to tech investing focuses on deep conviction in secular trends and the discipline to avoid speculative manias that ultimately destroy wealth.


ONE-SENTENCE TAKEAWAY

Successful AI investing requires focusing on enterprise technology companies with strong fundamentals, understanding the three-phase evolution of AI adoption (hardware, data infrastructure, applications), and maintaining the discipline to avoid speculative stocks regardless of their short-term performance.


SUMMARY

This engaging conversation between Eric of Fired Up Wealth and returning guest Ivana provides a comprehensive framework for investing in AI stocks across three key categories: hardware, data infrastructure, and applications. Ivana, who runs the SPRX ETF, shares her expertise in identifying high-quality technology companies and avoiding the pitfalls of speculative investing.

The discussion begins with a review of networking stocks that have performed exceptionally well since their last conversation in February 2025, including Astera Labs and Arista Networks. Ivana explains that networking represents a critical area where hardware delivers performance improvements, yet it's often misunderstood by the market. She emphasizes the importance of understanding the products these companies develop, especially newer entrants like Astera Labs, which has expanded from having Nvidia as its only customer to now serving approximately ten customers.

The conversation then shifts to data infrastructure companies, including Snowflake, MongoDB, Cloudflare, and Oracle. Ivana highlights how these companies are experiencing strong growth as enterprises seek to connect their data with AI models. Oracle receives special attention as a company that has transformed significantly over the years and now offers both cloud and on-premise solutions, making it well-positioned for the AI era despite skepticism from some investors.


The third category discussed is AI applications, including companies like ServiceNow, Shopify, and GitLab. Here, Ivana expresses caution, noting that while many companies are developing interesting AI products and applications, few have reached scale to the point where they're significantly moving the needle on fundamentals. This makes the application space particularly tricky for investors.

Throughout the conversation, Ivana and Eric emphasize the importance of investment discipline and conviction. They warn against FOMO (fear of missing out) investing in speculative stocks without fundamentals, noting that while these stocks may show impressive short-term gains, they often lead to significant losses when market sentiment shifts. Instead, they advocate for focusing on secular trends, understanding what you own, and maintaining a long-term perspective.

The discussion also touches on recent market developments, including the partnership between Nvidia and Intel, which they view as strategically important but not immediately transformative. They express continued bullishness on Nvidia and AMD while acknowledging potential competitive threats.

A significant portion of the conversation is dedicated to investment philosophy, with Ivana stressing that investors cannot borrow conviction from others but must develop their own through thorough research. She recommends focusing on enterprise technology over consumer technology due to the stickier nature of enterprise relationships and more predictable revenue streams.

The video concludes with a reminder that successful investing isn't about catching every winner but about having deep conviction in a focused set of investments that align with one's expertise and investment thesis.


INSIGHTS

  1. The Three-Phase Evolution of AI: AI investment opportunities follow a clear progression: first hardware (building data centers and chips), then data infrastructure (connecting enterprise data to AI models), and finally applications (implementing AI solutions for specific business problems).
  2. Networking as the Critical Enabler: While GPUs receive most attention, networking technology represents a crucial area where hardware delivers performance improvements, yet it's often misunderstood by the market, creating opportunities for informed investors.
  3. Enterprise Over Consumer: Enterprise AI investments offer more predictable returns than consumer AI because enterprise relationships are stickier, implementation costs create high switching barriers, and revenue streams are more stable.
  4. The Conviction Imperative: Successful investing requires developing your own conviction through research rather than borrowing it from others. This conviction is essential for maintaining positions during market volatility.
  5. The Speculative Trap: While speculative stocks may show impressive short-term gains, they often lead to significant losses when market sentiment shifts. True wealth is built through consistent returns on quality companies, not by chasing the latest sensation.
  6. The Power of Compounding: Young investors often underestimate the power of compounding consistent returns over time. Double-digit returns can double your money every five years without taking excessive risk on speculative investments.
  7. The Show-Me Story Risk: Companies that constantly promise future growth but fail to deliver (like Sentinel One mentioned in the video) often become value traps. Investors should be wary of companies that are "always a quarter away" from meeting expectations.
  8. The Importance of Portfolio Balance: Successful investing requires balancing momentum stocks that are performing well with value stocks that have pulled back, rather than concentrating in one category.


FRAMEWORKS & MODELS

The Three-Phase AI Investment Framework

  • Components: Phase 1 (Hardware) - Companies building the physical infrastructure for AI including chips, networking, and power systems; Phase 2 (Data Infrastructure) - Companies enabling enterprises to connect their data with AI models; Phase 3 (Applications) - Companies implementing AI solutions for specific business problems
  • How it works: AI adoption follows a predictable pattern where infrastructure must be built before data can be effectively utilized, which must happen before applications can deliver value
  • Evidence: Historical technology adoption patterns and current market developments where hardware companies have seen the most significant gains to date
  • Significance: Provides a structured approach to AI investing that helps investors identify where the greatest opportunities lie at any given time
  • Application: Investors should assess where we are in the adoption cycle and allocate capital accordingly, with current emphasis on Phase 1 and early Phase 2 opportunities

The Conviction Development Model

  • Components: Research, understanding, portfolio sizing, and holding through volatility
  • How it works: Investors must thoroughly research companies to understand their business models, competitive advantages, and growth prospects before investing
  • Evidence: Examples like Astera Labs where deep research led to significant gains despite volatility, including a drop from $80s to $46 before recovering
  • Significance: Prevents panic selling during market downturns and provides the confidence to add to positions during pullbacks
  • Application: Develop a research process that focuses on understanding the business rather than just stock price movements, and size positions according to conviction level

The Enterprise vs. Consumer Investment Framework

  • Components: Customer stickiness, revenue predictability, competitive barriers, and scalability
  • How it works: Enterprise technology companies typically have longer customer relationships, more predictable revenue streams, and higher switching costs than consumer technology companies
  • Evidence: Comparison between enterprise companies like ServiceNow and consumer companies that have fallen out of favor
  • Significance: Enterprise investments generally offer more predictable returns with less volatility than consumer investments
  • Application: When evaluating AI investments, prioritize companies with strong enterprise focus over those primarily targeting consumers

The Momentum-Value Balance Framework

  • Components: Momentum scoring, value assessment, portfolio weighting, and rebalancing triggers
  • How it works: Maintains a balance between stocks that are performing well (momentum) and stocks that have pulled back (value)
  • Evidence: Avana's approach of adding to positions like Coherent that have pulled back despite strong fundamentals
  • Significance: Prevents over-concentration in overvalued momentum stocks while taking advantage of opportunities in undervalued quality companies
  • *Application: Regularly assess portfolio composition and rebalance to maintain appropriate balance between momentum and value positions

QUOTES

  1. "You can't borrow conviction from me. You can't borrow conviction from you. You have to have that conviction yourself."
    • Context: Discussion about the importance of developing personal conviction in investments
    • Significance: Emphasizes that successful investing requires personal understanding and confidence rather than following others' recommendations
  2. "The returns you're not going to generate by chasing stuff that your neighbor owns. It's really going to come more about like investing in what you own for the long term."
    • Context: Warning against FOMO investing and speculative stocks
    • Significance: Highlights that true investment success comes from owning and understanding quality companies over time, not from chasing popular stocks
  3. "If you look at the most popular names that were kind of meme type names and meme is is one of those terms I think people look at it condescending. I mean I' I've said this many times you know I own Palanteer and it's a good company but at an 83x revenue and a cult following it starts to cross the line a little bit."
    • Context: Discussion about the fine line between quality growth stocks and speculative meme stocks
    • Significance: Illustrates how even quality companies can become overvalued and develop cult followings that disconnect them from fundamentals
  4. "The way we deliver returns is by sticking to our knittings. So, I couldn't agree more with you that really people should narrow down what they want to be researching and doing work on and really sticking to that because that's how you're going to generate the best returns."
    • Context: Ivana explaining her investment philosophy
    • Significance: Emphasizes the importance of focus and specialization in investment research and decision-making
  5. "Young people especially don't value is the power of compounding, right? Time passes so much. People have so many years ahead. If you can generate double-digit returns like high teens, you're going to be doubling your money every five years. You don't need 300% returners to to do that, right?"
    • Context: Discussion about the importance of consistent returns over speculative gains
    • Significance: Highlights how consistent, moderate returns can build substantial wealth over time through compounding
  6. "I think the consumer space is very large. So from that perspective it's interesting right? But on the flip side people change a lot quicker than enterprises. So that's one thing that we don't love about the consumer right where all of a sudden everybody wants GPT and then next thing you know like people have moved on to something else."
    • Context: Comparison between enterprise and consumer AI investments
    • Significance: Explains why enterprise technology investments typically offer more predictable returns than consumer technology
  7. "This area has been pretty tricky, especially from um from um the AI side. I think there hasn't been a ton of like to your point, it's either low conviction and and large pops and not a lot of like fundamental support."
    • Context: Discussion about AI application stocks
    • Significance: Highlights the challenges of investing in the AI application space where fundamentals haven't yet caught up with valuations


HABITS

  1. Develop Deep Conviction: Before investing in any stock, conduct thorough research to understand the business model, competitive advantages, and growth prospects. Don't rely on others' analysis or recommendations.
  2. Focus on Secular Trends: Identify long-term technology trends (like AI adoption) and invest in companies positioned to benefit from these trends over multiple years, not just short-term fads.
  3. Balance Momentum and Value: Maintain a portfolio that includes both stocks performing well (momentum) and quality stocks that have pulled back (value). Rebalance regularly to maintain appropriate allocation.
  4. Prioritize Enterprise Technology: Focus primarily on companies serving enterprise customers rather than consumers, as enterprise relationships tend to be stickier with more predictable revenue streams.
  5. Avoid Speculative Manias: Resist the temptation to invest in stocks with no revenue or weak fundamentals, regardless of their recent performance or popularity on social media.
  6. Buy During Pullbacks: When high-conviction stocks decline due to market volatility rather than fundamental issues, consider adding to positions rather than selling.
  7. Limit Speculative Investments: If investing in higher-risk speculative stocks, limit them to 5-10% of your portfolio and diversify across multiple positions rather than concentrating in one or two names.
  8. Regular Portfolio Review: Continuously assess your holdings to ensure they still meet your investment criteria. Be willing to sell when the investment thesis changes, even if it means admitting a mistake.

REFERENCES

  1. SPRX ETF: The ETF managed by Ivana that focuses on innovative technology companies, mentioned multiple times throughout the conversation as a way to gain exposure to the investment themes discussed.
  2. Astera Labs (ALAB): Networking company highlighted as a top performer that expanded from having Nvidia as its only customer to serving approximately ten customers, demonstrating the growth potential in networking technology.
  3. Oracle (ORCL): Legacy technology company that has transformed significantly over the years and now offers both cloud and on-premise solutions, positioning it well for the AI era despite skepticism from some investors.
  4. Snowflake (SNOW): Data infrastructure company mentioned as experiencing strong growth as enterprises seek to connect their data with AI models.
  5. MongoDB (MDB): Database company that has pulled back recently but is seen as well-positioned for the AI era.
  6. CrowdStrike (CRWD): Cybersecurity company that has maintained its leadership position in endpoint security despite competition, demonstrating the importance of sticking with high-conviction investments.
  7. Nvidia (NVDA): Chip company that continues to dominate the AI hardware space and recently announced a strategic partnership with Intel.
  8. Intel (INTC): Legacy chip company that announced a strategic partnership with Nvidia, representing a potential turnaround story but facing significant challenges in competing with TSMC in foundry services.
  9. Sentinel One (S): Cybersecurity company mentioned as an example of a "show-me story" that failed to deliver on its promise, illustrating the risks of holding companies that consistently underperform expectations.
  10. GitLab (GTLB): Software development platform mentioned as potentially undervalued but representing a value investment that requires patience and conviction.



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