Despite knowing customers would pay far more, Shopify intentionally underpriced its product. This lowered the barrier to entry for entrepreneurs, focusing on massive user acquisition and solving merchant problems first.

Unlike Apple's high-margin hardware strategy, Meta prices its AR glasses affordably. Mark Zuckerberg states the goal is not to profit from the device itself but from the long-term use of integrated AI and commerce services, treating the hardware as a gateway to a new service-based ecosystem.

After pivoting from hardware to software, SkillVari found value in reintroducing proprietary hardware (like a $2,500 welding gun) as optional accessories. This hybrid model leverages commodity headsets while capturing additional revenue and creating a more immersive, defensible user experience.

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.

For consumer robotics, the biggest bottleneck is real-world data. By aggressively cutting costs to make robots affordable, companies can deploy more units faster. This generates a massive data advantage, creating a feedback loop that improves the product and widens the competitive moat.

SkillVari uses a land-and-expand model where schools start with a low-cost software plan using standard VR controllers. As students advance, schools can purchase higher-margin hardware extensions like welding guns, increasing account value over time without a large upfront commitment.

The dominant per-user-per-month SaaS business model is becoming obsolete for AI-native companies. The new standard is consumption or outcome-based pricing. Customers will pay for the specific task an AI completes or the value it generates, not for a seat license, fundamentally changing how software is sold.

In a crowded market where startups offer free or heavily subsidized AI tokens to gain users, Vercel intentionally prices its tokens at cost. They reject undercutting the market, betting instead that a superior, higher-quality product will win customers willing to pay for value.

For tools requiring a new workflow, like Factory's AI agents, seat-based pricing creates friction. A usage-based model lowers the initial adoption barrier, allowing developers to try it once. This 'first try' is critical, as data shows an 85% retention rate after just one use.