Scrutinize the KPIs a company chooses not to highlight. For instance, Lumine and Topicus eschew standard metrics like EBITDA and ARR, instead framing their performance around a custom "Free Cash Flow Available to Shareholders" metric. This reveals their deep focus on cash generation for M&A, not chasing growth narratives.

Data businesses have high fixed costs to create an asset, not variable per-customer costs. This model shows poor initial gross margins but scales exceptionally well as revenue grows against fixed COGS. Investors often misunderstand this, penalizing data companies for a fundamentally powerful economic model.

The compute-heavy nature of AI makes traditional 80%+ SaaS gross margins impossible. Companies should embrace lower margins as proof of user adoption and value delivery. This strategy mirrors the successful on-premise to cloud transition, which ultimately drove massive growth for companies like Microsoft.

Established metrics for evaluating software (high gross margins, capital-light) are obsolete in the AI paradigm. Top AI companies often exhibit opposite traits, like low margins due to inference costs, signaling the "death of spreadsheet investing."

Software's heavy reliance on stock-based compensation (13.8% of revenue vs. 1.1% in other sectors) distorts key valuation metrics. The cash spent on share buybacks to offset dilution isn't factored into free cash flow calculations, making software companies appear more profitable than they are.

In the current mid-cycle phase, investors are looking past headline earnings growth. They are scrutinizing companies' ability to convert profits into actual cash. Stocks that increase earnings but not free cash flow are underperforming, signaling a demand for tangible financial health over pure growth narratives.

Contrary to traditional software evaluation, Andreessen Horowitz now questions AI companies that present high, SaaS-like gross margins. This often indicates a critical flaw: customers are not engaging with the costly, core AI features. Low margins, in this context, can be a positive signal of genuine product usage and value delivery.

Traditional SaaS metrics like 80%+ gross margins are misleading for AI companies. High inference costs lower margins, but if the absolute gross profit per customer is multiples higher than a SaaS equivalent, it's a superior business. The focus should shift from margin percentages to absolute gross profit dollars and multiples.

The market has fundamentally reset how it values mature SaaS companies. No longer priced on revenue growth, they are now treated like industrial firms. The valuation bottom is only found when they trade at free cash flow multiples that fully account for stock-based compensation.