E247: Why Wall Street Is Wrong About AI w/ Dan Ives
Timestamps are as accurate as they can be but may be slightly off. We encourage you to listen to the full context.
Podcast Summary
In this compelling episode, tech analyst Dan Ives, Managing Director at Wedbush Securities, reveals his unconventional investment philosophy that has led to legendary calls on Tesla, Nvidia, Microsoft, and Palantir. Ives explains why he views Tesla as the world's leading "physical AI" company rather than just an automaker, and how he spotted the AI revolution months before it became mainstream consensus. (01:57)
• Core themes include the power of pattern recognition over spreadsheet analysis, the importance of looking 3-7 years ahead instead of quarterly metrics, and how being contrarian while maintaining conviction can lead to transformational returns in disruptive technology stocks.Speakers
Dan Ives
Dan Ives is the Managing Director and Global Head of Technology Research at Wedbush Securities, where he has become one of Wall Street's most followed tech analysts over his 25-year career. He's renowned for making bold, high-conviction calls on major technology companies like Tesla, Nvidia, Microsoft, and Palantir years before they became consensus picks. Ives has traveled over 3 million air miles conducting field research globally, giving him unique insights into technology trends and market dynamics.
Key Takeaways
Look Beyond Quarterly Metrics to Long-Term Vision
Ives emphasizes that focusing on one-year valuations causes investors to miss every transformational tech stock. (03:39) He advocates looking 3-7 years ahead to understand where markets are heading. For Tesla, he projects that 20% of automotive will be autonomous by 2030, making current delivery numbers irrelevant compared to the robotaxi opportunity. Practical Example: Instead of judging Tesla by quarterly car deliveries, evaluate its potential $12-20 EPS power from autonomous driving and robotics over the next 6-7 years.
Field Research Trumps Spreadsheet Analysis
The most valuable insights come from talking directly to customers, engineers, and partners rather than relying solely on financial models. (08:08) Ives discovered the AI revolution in late 2022 by meeting with engineers who were excited about the technology, even when markets were skeptical. This ground-level intelligence often contradicts market sentiment but provides early signals of inflection points. Practical Example: When MongoDB missed a quarter and the stock crashed, customer feedback at user conferences revealed their unique competitive advantages, creating a buying opportunity.
Embrace Contrarian Positioning When Conviction Is High
The biggest opportunities come when stocks are declining but underlying fundamentals are strengthening. (10:10) Ives learned to use market downturns as conviction-building moments rather than capitulation points. When Palantir was selling off from $30 to $23, boot camp customer demand was unprecedented, signaling a temporary disconnect between price and value. Practical Example: Scale up position sizes when conviction moves from 8.5 to 9+ on a 1-10 scale, regardless of short-term stock performance.
Management Quality Is Often Mispriced
Betting on exceptional leadership teams provides asymmetric returns that spreadsheets can't capture. (39:02) Ives credits much of his success to identifying visionary CEOs like Jensen Huang at Nvidia, Alex Karp at Palantir, and Gary Steele at Proofpoint. Founder-led companies are particularly undervalued because they can make long-term bets without quarterly pressure. Practical Example: Evaluate whether you'd trust a CEO to "fly the plane" - if the answer is yes, that's often more predictive than financial metrics alone.
Pattern Recognition Beats Consensus Analysis
Developing a framework for spotting inflection points before they're obvious is crucial for generating alpha. (27:27) Ives positions himself as a "conduit of information" by constantly gathering data from global customers, partners, and investors. This creates a feedback loop where he can identify emerging trends and sentiment shifts ahead of traditional Wall Street research. Practical Example: Track when enterprise customers shift from viewing AI as "hype" to making it a top-2 budget priority - this behavioral change often precedes stock performance by months.
Statistics & Facts
- Dan Ives has traveled over 3 million air miles in his 25-year career conducting field research. (07:07) This extensive travel gives him unique global perspectives on technology trends and customer sentiment.
- Ives projects that 20% of the automotive market will be autonomous by 2030. (04:00) This statistic underpins his Tesla valuation framework and explains why he views it as a $2-3 trillion market opportunity.
- Tesla could potentially achieve $12-20 in earnings per share power over the next 6-7 years according to Ives' projections. (04:32) This represents a significant multiple of current earnings and justifies his long-term bullish stance.
