The AI startup ecosystem is undergoing a 'wrapper reckoning' as investors reject shallow LLM-wrapper products in favor of purpose-built AI. The core argument is that different problem types require different AI paradigms: LLMs for language and reasoning, CNNs for perception, and temporal difference learning for sequential decision-making with delayed rewards. Applied to sales, this means LLMs can draft emails but TD learning is better suited for pipeline optimization and deal sequencing decisions. The future architecture is a distributed network of specialized agents, each using the right AI technique, orchestrated by a coordination layer and guided by LLM-based reasoning. Human-agent collaboration is positioned as the true source of competitive advantage in this 'agentic enterprise' model.
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The wrapper reckoningA decade of specialized breakthroughsThe right tool for each taskFrom monolithic models to diverse agent networksThe agentic enterpriseWhat this means for practitionersSort: