The mainstream AIs approaches are the generative and deep learning approaches with large language models (LLMs) and the manually constructed symbolic approach. Both approaches have led to valuable AI systems and impressive feats. However, manually constructed AIs are brittle even in circumscribed domains. Generative AIs make strange mistakes and do not notice them. In both approaches the AIs cannot be instructed easily, fail to use common sense, and lack curiosity. They have abstract knowledge but lack social alignment. Developmental AIs have more potential. They start with innate competences, interact with their environment, and learn from their interactions. They interact and learn from people and establish perceptual, cognitive, and common grounding. Developmental AIs have demonstrated capabilities including multimodal perception, object recognition, and manipulation. Powerful computational models for hierarchical planning, abstraction discovery, curiosity, and language acquisition exist but need to be adapted to a developmental learning based approach. The promise is that developmental AIs will acquire self-developed and socially developed competences. They would address the shortcomings of current mainstream AI approaches, and ultimately lead to sophisticated forms of learning involving critical reading, provenance evaluation, and hypothesis testing. However, developmental AI projects have not yet fully reached the Speaking Gap corresponding to toddler development at about two years of age, before their speech is fluent. The AIs do not bridge the Reading Gap, to skillfully and skeptically learn from written and online information resources. This position paper lays out the prospects, gaps, and challenges for extending the practice of developmental AIs to create resilient, intelligent, and human-compatible AIs that learn what they need to know.

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