To maintain agility in the fast-paced AI landscape, Arvind Jain actively encourages his R&D team to throw out old code. He believes rewarding code deletion at the same level as building new features is essential to prevent the company from slowly becoming a legacy software stack.

In the AI era, foundation models can render complex, custom-built features obsolete overnight. This requires a culture where teams willingly discard their own hard-built IP without ego, accepting their work has a short shelf life.

In fast-moving industries like AI, achieving product-market fit is not a final destination. It's a temporary state that only applies to the current 'chapter' of the market. Founders must accept that their platform will need to evolve significantly and be rebuilt for the next chapter to maintain relevance and leadership.

The rapid pace of AI development means any new system, process, or architecture is on a path to obsolescence upon launch. Forward-thinking enterprises are building for this ephemerality, designing dynamic systems that assume frequent, fundamental changes will be required.

Contrary to the classic engineering rule to "never rewrite," Block's CTO believes AI will make this the new standard. He is pushing his teams to imagine a world where for every release, they delete the entire app (`rm -rf`) and rebuild it from scratch, with AI respecting all incremental improvements from the previous version.

To innovate at the speed of AI, adopt the mindset that anything you build today could be made obsolete by next week's model release. This forces you to hold ideas loosely, constantly update your beliefs, and prioritize learning and exploration over perfection.

To keep pace with rapid AI advancements, the company intentionally operates on a two-year horizon for its technology stack. This forces them to be dynamic and adapt to new research, rather than getting locked into outdated architectures, having completed four such evolutions so far.

The mantra "don't be married to features" is insufficient. Product leaders must now be willing to abandon entire underlying architectures if a new approach allows for significantly faster value delivery. This may require pausing roadmaps to re-platform, a risk worth taking for long-term velocity.

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.