AI startup pitches can sound similar: proprietary intelligence, massive markets and rapidly improving models. Investors need a framework that separates product momentum from temporary model advantage.

Start with the user problem

Ask what behavior changes if the product disappears. A strong startup solves a recurring problem or creates a new habit rather than wrapping a model around a one-time demo.

Measure distribution

Model quality can converge quickly. Distribution through creators, communities, enterprise workflows or proprietary data can be harder to copy.

Understand model dependency

Does the company own core models, orchestrate third-party models, or combine both? Each approach has different capital needs and margin risks.

Examine unit economics

Inference, image and video generation create variable costs. Revenue growth should be viewed alongside gross margin and engagement intensity.

Look at retention

Consumer AI products can generate viral signups but weak repeat use. Cohort retention, repeat workflows and willingness to pay reveal whether novelty becomes utility or habit.

Assess defensibility

Potential moats include proprietary workflows, creator or IP supply, network effects, data flywheels, brand and integrated multimodal systems. AI social companies such as Tuikor AI are interesting examples because their defensibility thesis combines technology with creator distribution and persistent digital identities.