With AI, teams can create crude prototypes immediately after a customer call. This "build to learn" phase cheaply validates ideas. Only after confirming market need should teams shift to "build to earn," investing in scalable development. This strategy mitigates the risk of building unwanted products at high speed.

The quickest path to market is a pilot where you sell the desired outcome, not the software. Initially, perform the work manually with AI assistance behind the scenes. This validates customer value and pinpoints the most repeatable patterns to productize.

The most practical first step into local AI isn't training a custom model. Instead, identify a repeated workflow, like analyzing a folder of notes to produce a memo. This proves value quickly and teaches you the limitations before you invest in complex fine-tuning.

Before writing code, manually perform the customer's workflow as a service. This unsexy approach ensures you deeply understand the process, enabling you to build a superior automated solution later. It's about fulfilling the task first, then building the software.

Instead of immediately building an AI agent, founders should first manually perform the target workflow as a service. This process allows them to deeply understand the pain points, map edge cases, and acquire initial clients. Only after mastering the job manually should they incrementally add vertical agents to automate specific steps.

A new training model encourages users to conceptualize AI as the core 'staff' for a new micro-business. This mindset shifts the use of AI from simple task automation to a strategic tool for ideation, business planning, and demand testing, enabling rapid and low-cost entrepreneurial experiments.

Begin by offering AI consulting or services. This provides immediate cash flow and deep customer insights with a 70-80% margin. Use this experience to document workflows and then productize the solution into a scalable software product with ~95% margins.

A massive opportunity exists for service-based startups that help traditional companies become AI-native. The winning strategy is to niche down by industry (e.g., dentistry), function (e.g., marketing), and company size to create replicable workflows.

Traditionally, service businesses lack scalability for VC. But AI startups are adopting a 'manual first, automate later' approach. They deliver high-touch services to gain traction, while simultaneously building AI to automate 90%+ of the work, eventually achieving software-like margins and growth.