Growing up in our family's grocery store, I learned fundamental truths about sustainability. The harsh realities of thin margins and the devastating impact of waste served as daily lessons in survival, where a spoiled shipment of produce or a miscalculated inventory order could erase a day's profits. Now, as I build in the AI space, I ponder about this quite a bit: despite the shift away from zero interest rates, the technology sector remains partially tethered to an era of abundant capital, operating under the hypothesis that compute costs will eventually become negligible—if companies can simply survive long enough.

This mindset manifests in intriguing ways across the ecosystem. Companies are aggressively offering comprehensive AI feature sets at $10-20 per seat while the underlying unit economics reveal a more complex and concerning reality. Consider meeting transcription - a seemingly straightforward service that perfectly exemplifies this challenge. A typical sales executive or manager accumulates over 80 hours of meetings monthly (4,800 minutes of transcription), costing approximately $23 through premium providers like Deepgram with Nova-2 streaming at growth-tier pricing. Layer on the compute costs for LLM inferencing, platform fees, and operational overhead, and you're looking at a business model that becomes increasingly precarious at scale.

In the B2B landscape, companies often attempt to offset these costs by adding platform fees or surcharges to reach $50-60 per seat, but this approach introduces additional security and support overhead. While this strategy can work, it requires building genuine defensibility through deep workflow integration or unique intellectual property. Without these, we inevitably spiral into a race to the bottom, where companies compete primarily on price rather than value. Continuing in meeting space - even established players like Gong are facing pressure from startups entering the market with significantly lower pricing. The paradigm shift our industry needs isn't just about better technology or more efficient models; it's about fundamentally rethinking how we build sustainable AI businesses with genuine competitive advantages that justify their economics.