High productivity isn't about using AI for everything. It's a disciplined workflow: breaking a task into sub-problems, using an LLM for high-leverage parts like scaffolding and tests, and reserving human focus for the core implementation. This avoids the sunk cost of forcing AI on unsuitable tasks.
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Product managers should leverage AI to get 80% of the way on tasks like competitive analysis, but must apply their own intellect for the final 20%. Fully abdicating responsibility to AI can lead to factual errors and hallucinations that, if used to build a product, result in costly rework and strategic missteps.
The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.
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.
A 'GenAI solves everything' mindset is flawed. High-latency models are unsuitable for real-time operational needs, like optimizing a warehouse worker's scanning path, which requires millisecond responses. The key is to apply the right tool—be it an optimizer, machine learning, or GenAI—to the specific business problem.
Instead of asking an AI to directly build something, the more effective approach is to instruct it on *how* to solve the problem: gather references, identify best-in-class libraries, and create a framework before implementation. This means working one level of abstraction higher than the code itself.
Building a single, all-purpose AI is like hiring one person for every company role. To maximize accuracy and creativity, build multiple custom GPTs, each trained for a specific function like copywriting or operations, and have them collaborate.
Instead of viewing AI collaboration as a manager delegating tasks, adopt the "surgeon" model. The human expert performs the critical, hands-on work while AI assistants handle prep (briefings, drafts) and auxiliary tasks. This keeps the expert in a state of flow and focused on their unique skills.
Borrowing from classic management theory, the most effective way to use AI agents is to fix problems at the earliest 'lowest value stage'. This means rigorously reviewing the agent's proposed plan *before* it writes any code, preventing costly rework later on.
An emerging power-user pattern, especially among new grads, is to trust AI coding assistants like Codex with entire features, not just small snippets. This "full YOLO mode" approach, while sometimes failing, often "one-shots" complex tasks, forcing a recalibration of how developers should leverage AI for maximum effectiveness.
For complex, one-time tasks like a code migration, don't just ask AI to write a script. Instead, have it build a disposable tool—a "jig" or "command center”—that visualizes the process and guides you through each step. This provides more control and understanding than a black-box script.