Software Economics
#software #business-models #ai
Governing Question
What happens to software economics when the cost of producing software collapses faster than the value customers are willing to assign to it?
Current Position
When software becomes easier to produce, production alone stops explaining price. Customers still need outcomes they can recognise and trust, while providers need an advantage that survives cheaper models and implementation.
Accumulated operational experience may become such an advantage when it changes future decisions. A system that learns which interventions work in a particular environment can become harder to replace than the tools used to build it.
This shifts the inquiry from margin compression toward ownership of learning. A provider’s durable advantage may also become a customer’s dependency; the terms on which that experience can travel matter alongside the price of the service.
The Argument
Beyond Efficiency: The Transformative Power of AI in Revolutionizing Business Models
AI's real power is not shaving costs off old businesses. It is inventing new ones that were impossible when intelligence was scarce.
How AI Will Disrupt Incumbents and Erase Traditional Margins
AI does not just make markets bigger; it compresses the margins that once protected incumbents.
Enterprise AI Sales Is Changing - Here’s Where the Money Really Is.
The money in AI is not where the models are smartest. It is where the model gets embedded into a workflow someone will actually pay for.
The Platform Era is Dying. Enterprises Are Already Moving On.
Platforms still matter, but the monopoly on workflow is fading. Enterprises are drifting toward smaller, sharper tools that fit the work.
OpenAI Won’t Kill Your Startup
Foundation models can swallow generic work, but they cannot own every niche. Startups win by being specific, trusted, and hard to copy.
In the Indian IT industry, the days of billing by headcount and hours are numbered
AI is making people-heavy operations less necessary. Indian IT firms must shift from billing for headcount and hours to pricing technology margins, outcomes, and capabilities that expand what clients can do.
Distribution is not enough
Distribution is no longer a durable moat. The next wave will win with modular, AI-native technology, outcome-based pricing, and domain-specific units of intelligence that augment higher-value work.
Tokens are not yield
Alex Karp’s critique matters because it comes from inside the AI wave: tokens are not yield, usage is not value, and enterprise AI must be judged by the consequences it produces.
What It Means to Be an AI-First Managed Services Partner
Managed services has spent decades accumulating customer experience without allowing that experience to compound. Being AI-first means changing that; building operational intelligence that learns the customer, remembers what worked, understands what failed, and lets yesterday’s operations improve tomorrow’s decisions.
What Changed
The early argument concerned new markets and the erosion of incumbent margins. The writing on enterprise sales and billing moved toward workflows and outcomes as units of value. The managed-services essay adds a possible source of durable advantage: operational experience that compounds into better decisions. That also introduces a tension between the provider’s switching advantage and the customer’s ability to leave.
Unresolved
- Which forms of software value remain scarce when implementation becomes abundant?
- How will customers distinguish durable outcomes from inexpensive production?
- Where do margins move when software, services, and intelligence increasingly converge?
- Who owns the operational learning when a customer changes providers?
Last revised Sep 11, 2026