Large enterprises navigate a critical paradox with new technology like AI. Moving too slowly cedes the market and leads to irrelevance. However, moving too quickly without clear direction or a focus on feasibility results in wasting millions of dollars on failed initiatives.

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.

The default assumption for any 'moonshot' idea is that it is likely wrong. The team's immediate goal is to find the fatal flaw as fast as possible. This counterintuitive approach avoids emotional attachment and speeds up the overall innovation cycle by prioritizing learning over being right.

Instead of defaulting to skepticism and looking for reasons why something won't work, the most productive starting point is to imagine how big and impactful a new idea could become. After exploring the optimistic case, you can then systematically address and mitigate the risks.

True innovation requires leaders to adopt a venture capital mindset, accepting that roughly nine out of ten initiatives will fail. This high tolerance for failure, mirroring professional investment odds, is a prerequisite for the psychological safety needed for breakthrough results.

During technological bubbles or periods of intense change, it's possible to accomplish seven to ten years of work in one. This 'dog years' effect offers a unique opportunity for compressed learning and value creation, even if the specific venture fails. The key is embracing the frenetic pace.

In the current AI landscape, knowledge and assumptions become obsolete within months, not years. This rapid pace of evolution creates significant stress, as investors and founders must constantly re-educate themselves to make informed decisions. Relying on past knowledge is a quick path to failure.

The pace of change in AI means even senior leaders must adopt a learner's mindset. Humility is teachability, and teachability is survivability. Successful leaders are willing to learn from junior colleagues, take basic courses, and admit they don't know everything, which is crucial when there is no established blueprint.

Dell argues that to take on giants like IBM, you need extreme self-belief and, crucially, naivete—not knowing enough to believe it's impossible. This combination allows founders to ignore conventional wisdom that paralyzes incumbents and invent entirely new approaches.