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.

For a true AI-native product, extremely high margins might indicate it isn't using enough AI, as inference has real costs. Founders should price for adoption, believing model costs will fall, and plan to build strong margins later through sophisticated, usage-based pricing tiers rather than optimizing prematurely.

By fixing the upfront cash collection, the business generates enough surplus to potentially double sales commissions from $50 to $100 per deal. This elevated pay structure attracts a completely different caliber of salesperson—"an order of magnitude better"—who can close more deals per day, dramatically accelerating growth without adding financial risk.

Investors and acquirers pay premiums for predictable revenue, which comes from retaining and upselling existing customers. This "expansion revenue" is a far greater value multiplier than simply acquiring new customers, a metric most founders wrongly prioritize.

Contrary to the trend of staying private, Navan's IPO was partly a go-to-market strategy. Large corporate customers demand the financial transparency and long-term stability that being a public company provides. This credibility was crucial for unlocking the enterprise segment and winning major accounts.

New AI companies reframe their P&L by viewing inference costs not as a COGS liability but as a sales and marketing investment. By building the best possible agent, the product itself becomes the primary driver of growth, allowing them to operate with lean go-to-market teams.

This model focuses on rapid cash conversion by making gross profit from a new customer in the first 30 days exceed twice the cost of acquiring and serving them. This self-funding loop eliminates cash flow as a growth constraint, allowing for aggressive scaling.

Navan's IPO stumbled despite decent growth and improving margins, not because of its own fundamentals, but due to its relative unattractiveness. In the current market, public investors prefer putting capital into proven, profitable tech giants with strong AI stories over an unprofitable company at a high sales multiple.

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.