The phrase “Netflix wants to become HBO faster than HBO becomes Netflix” captures a classic dynamic in technology-driven industry change, namely that boundaries between industries or value chain roles dissolve.
When a new entrant attacks an incumbent from outside the incumbent’s core model, the winner is often the firm that successfully invades the other’s strengths first, not the one that defends its legacy longest.
The race wasn’t “who streams better" but rather which firm could absorb the other’s advantage first without losing their own?
Netflix’s goal was to climb up the value chain (from distributor to premium content owner) before HBO could climb down (from content owner to global tech platform).
And that's the challenge for enterprise software firms and AI language models. Just as HBO adding streaming apps didn’t make it Netflix, enterprise apps adding AI features doesn’t make them AI-native.
Among the real questions is whether enterprise apps can re-architect around AI as the primary interface faster than AI systems absorb enterprise logic and relegate apps to backend record-keeping.
It might be easier for AI systems to learn enterprise workflows than for enterprise apps to reinvent themselves as AI-first systems.
Enterprise apps are constrained by:
* Backward compatibility
* Regulatory commitments
* Pricing models tied to seats and modules.
AI-native systems are constrained mainly by:
* Access to data
* Trust and permissions (solvable over time)
Some might argue the attacker has advantages. As with Netflix in its battle with HBO, content quality was hard, but learnable.
Software culture and data advantage were not similarly "learnable" by HBO.
Enterprise apps must become AI-native faster than AI systems become the enterprise app, because once AI owns the interface, orchestration, and decision layer, everything else becomes a back-end service.
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