The idea that a few top VCs can anoint a winner by concentrating capital into one company ('kingmaking') is a fallacy. While access to significant capital is an advantage, particularly with product-market fit, it does not guarantee victory or prevent a competitor from slingshotting from behind.
When a company is growing 10x or 50x year-over-year, obsessing over the entry multiple is a mistake. An initially 'insane' valuation can look cheap in retrospect. The primary focus should be on determining if the company is on an exponential curve; price is the least important factor in that equation.
Counterintuitively, data shows that companies in higher market cap bands (e.g., $10B to $100B) have a statistically better chance of achieving a 10x return than those in lower bands. This supports the strategy of doubling down on winners, as the 'next double' is often easier for established platform companies.
Unlike a decade ago, today's most transformative, high-growth companies like OpenAI and Anthropic are choosing to remain private for longer. This trend concentrates the highest potential returns in private markets, making it difficult for public investors to 'own the future' of technology.
The AI wave is creating uncertainty about the long-term durability of SaaS revenue streams, which were once considered as reliable as insurance annuities. This doubt is driving a market-wide downturn for public SaaS stocks, as investors struggle to predict which companies will thrive or become obsolete.
In an era of rapid technological shifts, durable value comes not from steady revenue growth but from a founder's capacity to reinvent the company repeatedly. Databricks' CEO Ali Ghodsi exemplifies this by successfully navigating multiple S-curves, which is the true driver of long-term success.
Instead of focusing on the current price, a more effective framework is to ask if you would be excited to invest more at a significantly higher valuation if the company executes well over the next six months. This tests your conviction in the company's long-term, generational potential.
The private market ecosystem exhibits extreme value concentration. Just 20 'platform companies' account for 80% of all private enterprise value, and a mere 4 companies are responsible for 65%. This power law reality dictates that being in these few key companies is all that matters for generating top-tier returns.
To generate fund-returning outcomes (5-6x), a simple 3x potential isn't enough. A company must be compelling enough that after you've made your 3x, another investor can clearly see a path to make *their* 3x. Without this 'next 3x' potential, the company will lack exit opportunities and liquidity.
While strong data is a necessary condition for investment, it shouldn't be the sole determinant. Focusing too intently on a single metric, like quarterly net new ARR, can cause you to miss the larger secular trend. Data provides guideposts, but you can't lose sight of the bigger picture, the 'forest through the trees.'
During major technology shifts like the move to cloud or AI, the best companies (e.g., hyperscalers, Snowflake) often have terrible early margins. In AI, inference costs are falling so rapidly that a company's margin profile can improve dramatically. Judging an early AI company on SaaS-era margin expectations is a mistake.
While AI companies are structurally lower gross margin due to cloud and LLM costs, this may be offset by significantly lower operating expenses. AI tools can make engineering, sales, and legal teams more efficient, potentially leading to a higher terminal operating margin than traditional SaaS businesses, which is what ultimately matters.
Mamoon Hamid, a top Series A SaaS investor, has a unique talent for identifying the precise moment a company's trajectory 'kinks' upward, even with very little data. He did this with Figma at only $500k ARR by seeing the usage curves inside key early customers like Google, recognizing the inflection point before anyone else.