Startups and small companies have a massive advantage because they aren't burdened by legacy code. The cost and effort for large enterprises to modernize their tech stack to effectively use new AI tools acts as a significant tax, slowing them down.
Consumer AI tools rely on motivated users to correct errors. For products targeting users who expect perfection, your team must engineer this "reliability layer" to handle AI inconsistencies, which adds significant cost and effort that is often overlooked.
Instead of asking "Can the model do this?", product leaders should ask four critical questions about the user experience, who builds the reliability layer, the business cost of its failure, and the cost to maintain it at scale.
Constraining a powerful, creative AI to perform a single, simple task reliably is counterintuitively harder than letting it be a general-purpose tool. Your team will spend most of its time building guardrails, checks, and other reliability code around the core model.
As AI automates code generation, the ability to write code quickly becomes less of a differentiator for engineers. Instead, a deep understanding of computer science fundamentals and system design is now more critical, as these are architectural skills AI cannot yet replicate.
To set realistic expectations with business stakeholders, describe an AI model's performance by its error rate (e.g., "wrong 15% of the time") rather than its accuracy rate (e.g., "correct 85% of the time"). This reframing highlights the real-world impact of imperfections.
The "should PMs code" debate is settled. With AI tools, PMs don't need to become engineers, but they must become literate in how software is built. Modern tools make it easy to interact with codebases like Git to get answers without waiting for an engineer.
Senior professionals leverage years of experience to "call bullshit" on flawed AI suggestions, using tools as amplifiers. Junior talent, lacking this accumulated judgment, can't easily replicate that skill, creating a widening gap in effective tool usage and product sense.
Intentionally create friction by allowing users to provide part of the "reliability layer." This functions as deep customization, increasing engagement and creating lock-in for sophisticated users. This strategy is best suited for enterprise tiers where the cost can be justified.