In the fast-evolving AI landscape, building for current capabilities means a product will be obsolete upon launch. Ambience actively predicts AI advancements 18 months out and designs its products for that future state, treating the present as a constantly shifting foundation.

Unlike traditional software development, AI-native founders avoid long-term, deterministic roadmaps. They recognize that AI capabilities change so rapidly that the most effective strategy is to maximize what's possible *now* with fast iteration cycles, rather than planning for a speculative future.

Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.

AI is evolving so rapidly that building for today's limitations is a mistake. Leaders should anticipate the state of the technology six months in the future and design products for that world. This prevents being quickly outdated by the pace of innovation.

The rapid pace of change in AI renders long-term strategic planning ineffective. With foundational technology shifts occurring quarterly, companies must adopt a fluid approach. Strategy should focus on core principles and institutional memory, while remaining flexible enough to integrate new tech and iterate on tactics constantly.

In the fast-paced AI landscape, success is fleeting. The underlying models and capabilities are advancing so rapidly that market leaders must fundamentally reinvent their company and product every six to nine months. Stagnation for even a year means falling hopelessly behind, as demonstrated by Cursor's evolution from auto-complete to managing agentic swarms.

Building on AI requires creating custom infrastructure to fill performance gaps. As underlying models improve, founders must be prepared to delete this now-redundant code and upgrade their product vision to tackle the next set of challenges at the new frontier. This cycle of building and deleting is key to staying innovative.

The underlying infrastructure for AI agents ('harnesses') becomes obsolete roughly every six months due to rapid advances in AI models. At Notion, this means completely rewriting the harness multiple times a year, demanding a culture comfortable with constantly rebuilding core systems and discarding previous assumptions.

To fully leverage rapidly improving AI models, companies cannot just plug in new APIs. Notion's co-founder reveals they completely rebuild their AI system architecture every six months, designing it around the specific capabilities of the latest models to avoid being stuck with suboptimal implementations.