When you sell a solution based on replacing human hours, your price becomes capped by the cost of that human. If a person costs $100k, you can't realistically charge more than a fraction of that for the software, creating a natural ceiling on your average sales price.
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When selling to enterprises, founders can feel intimidated asking for large contract values. A powerful yardstick is to frame the price relative to a fully-loaded engineer's salary (e.g., 'is this worth half an engineer to you?'). This contextualizes the cost against a familiar, significant budget item.
While preventing a single multi-million dollar mistake is a product's biggest value, it's easier to sell based on quantifiable time savings. The justification "this costs one-fourth of a new hire" is a straightforward business case for a budget holder, making the sale simpler.
Value-based flat fees should not just reflect the initial time estimate. As a business becomes more efficient and reduces the time required for a task, the flat fee should remain the same. This allows the business, not the client, to reap the financial reward of its accumulated experience.
When selling to small businesses, especially in emerging markets, they are often time-abundant but customer-scarce. They are hesitant to pay for SaaS tools that save time or improve efficiency but will readily share economics for solutions that directly bring them more demand and revenue.
Traditional SaaS companies are trapped by their per-seat pricing model. Their own AI agents, if successful, would reduce the number of human seats needed, cannibalizing their core revenue. AI-native startups exploit this by using value-based pricing (e.g., tasks completed), aligning their success with customer automation goals.
Entrepreneurs second-guess pricing because they undervalue intangible benefits like time savings, convenience, and client relationships. They also wrongly assume customers are solely price-driven, when loyalty is affected by many other factors.
Instead of viewing your limited one-on-one time as an unscalable weakness, frame it as an extremely scarce resource. This fixed, low supply naturally drives up price. The goal isn't asking if a task is 'worth your time,' but setting a price that makes it worth your time.
The "last mile" difficulty of implementing AI agents makes them economically viable for huge enterprise deals (justifying custom engineering) or mass-market apps. The traditional SaaS sweet spot—the $30k-$50k mid-market contract—is currently a "missing middle" because the cost to deliver the service is too high for the price point.
In the age of AI, software is shifting from a tool that assists humans to an agent that completes tasks. The pricing model should reflect this. Instead of a subscription for access (a license), charge for the value created when the AI successfully achieves a business outcome.
The push for AI-driven efficiency means many companies are past 'peak employee.' This creates a scenario analogous to a country with a declining population, where the total number of available seats is in permanent decline, making per-seat pricing a fundamentally flawed long-term business model.
