Don't Build Your Own AI (Unless You Have To)

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AI Pricing, Value-Based Models, and the Road to Utility

At 25:58 · chapter starts 25:55

Akshaya identifies a structural tension in the current AI market: the industry is simultaneously watching per-token costs compress toward commodity pricing while also realising that the value delivered far exceeds what those tokens cost to produce. The result is a mispricing problem — vendors don't yet know how to charge for value, and buyers don't yet know how to quantify what they're getting. Zendesk's response has been to move toward resolution-based pricing: customers pay for solved problems, not for compute cycles consumed. That model change, Akshaya argues, offsets some of the cost pressure while aligning incentives more honestly with outcomes. His long-range view is that AI will follow the same arc as internet access — initially priced per megabyte or gigabyte, then compressed into flat-rate utility pricing. A 'value-add tax' will stabilise somewhere above pure commoditisation, but the gross cost trajectory is definitively downward. For enterprises making build-vs-buy decisions now, that means today's integration economics are still favourable, but the calculus will shift over time.