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.

Bland AI intentionally avoided using third-party APIs like OpenAI or 11 Labs, building its entire voice AI stack in-house. This difficult decision was less about features and more about winning enterprise trust through superior security, reliability, and having a single, accountable provider for critical infrastructure.

In a complex legacy environment, internal motivations like improving developer experience or modernizing technology often fail to gain traction. The initiatives that successfully navigate the process are those that can clearly articulate and deliver tangible value to the end customer.

Applied AI startups must solve immediate customer problems by building proprietary technology, even if they know it will be commoditized by foundation models in a few years. The strategy is to win customers now with superior tech, building a product and market position that will endure after the technology becomes table stakes.

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 This forces them to be dynamic and adapt to new research, rather than getting locked into outdated architectures, having completed four such evolutions so far.

When generative AI emerged, the team feared their existing product would become obsolete. Instead of retrofitting AI features, they made the strategic decision to rebuild the entire platform from the ground up with AI at its core. This allowed them to realize their long-term product vision.

To transition to AI, leaders must ruthlessly dismantle parts of their existing, money-making codebase that are not competitively differentiating or slow down AI development. This requires overcoming the team's justifiable pride and emotional attachment to legacy systems they built.

To succeed in the AI era, SaaS companies cannot just add AI features. They must undergo a 'brutal' transformation, changing everything from their org chart and GTM strategy to their core metrics and pricing model. This is a non-negotiable, foundational shift.

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.