AI Engineer Hariprasad Sudharshan on the 'Median' and Novelty Budget
In a video talk, Hariprasad Sudharshan shares insights from two years of solo building an AI calculator-like app. He notes that identical models and tools often lead to converging solutions, termed the 'median.' He proposes a 'novelty budget' to define unique value and suggests leaders evaluate demos by asking who has already solved the problem, what distinct assets the team holds, and where users will be reached.
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In his video talk titled "I Spent Two Years Building the Median," Hariprasad Sudharshan explores the homogenization of software construction in the AI era. Drawing from his experience of independently developing a calculator-like AI application over two years, he observed that other builders, driven by the same models, tools, and familiar problems, often produce strikingly similar solutions without copying each other. He refers to this shared starting point as the "median," noting that while faster development makes it easier to ship useful software, a functioning demo does not establish a durable product advantage.
Sudharshan distinguishes between the model's capability, its harness, and the final product received by the customer. He describes a "build first" reflex where developers immediately open a coding agent instead of searching for existing solutions. As an alternative, he introduces the concept of a "novelty budget": naming the one distinctive thing before starting, including it in the roadmap, and reviewing it as deliberately as other project spending.
For leaders evaluating demos, Sudharshan proposes three critical questions: Who has already solved this? What does this team have that a competitor does not? Where will the application reach users? In later discussions about iterating on the calculator, he addresses the difference between running open model weights inside an enterprise versus purchasing inference, touching on utilization and infrastructure costs. His core lesson is to use readily available starting points where they help, then spend judgment on a specific problem, distribution, and a defensible difference.