Many late-stage investors focus heavily on data and metrics, forgetting that the quality of the leadership team remains as critical as in the seed stage. A new CEO, for example, can completely pivot a large company and reignite growth, a factor that quantitative analysis often misses.

The historic rotation out of momentum and into value may signal a major regime change. If cheap AI models boost margins for traditional "value" businesses, it could reverse a two-decade trend of growth stock outperformance for the first time since the dot-com bust.

Many investments labeled "value traps" aren't bad picks but are simply taking longer than expected to mature. During this latency, the business's fundamentals and earnings potential can actually improve, making it a better investment.

Identifying a stock trading below its intrinsic value is only the first step. To avoid "value traps" (stocks that stay cheap forever), investors must also identify a specific catalyst that will unlock its value over a reasonable timeframe, typically 2-4 years.

Wagner's strategy shifted from buying statistically cheap companies to requiring a clear catalyst for value realization. He found that without a catalyst, even correctly underwritten cheap stocks would continue to decline due to factors like technological disruption, making the old "cigar butt" approach obsolete.

Mark Ein's investment model focuses on finding fantastic existing companies that have plateaued. He then applies a venture-style growth mindset to accelerate their trajectory, combining the stability of an established business with the rapid-scaling tactics of a startup.

Significant disruption often comes from applying mature technologies in novel contexts, not just from new inventions. Gaonkar points to 1970s lithium-ion batteries revolutionizing EVs and old gaming GPUs now powering the AI boom as prime examples of this powerful investment thesis.

The Alger investment philosophy reframes growth investing. Instead of screening for fast-growing companies, their team looks for fundamental "change"—in management, regulation, or technology. They believe this change is the root cause that ultimately begets identifiable growth.

Instead of betting on unknowable AI winners, a better strategy is to find quality companies the market has written off as "losers" due to AI fears. Similar to the unloved "old economy" stocks during the dot-com bubble, these perceived victims could offer significant upside if the disruption threat is overblown.