According to Moda's founder, the most impactful AI tools are not those that merely accelerate existing workflows. Instead, they are the ones that empower users to achieve outcomes that were previously beyond their skill set, truly unlocking new creative capabilities for non-experts.

Obsessing over the latest AI tools leads to scattered, inefficient usage. Instead, focus on the problem you're solving—like gaining leverage—and design an integrated system to address it. The specific tools become secondary to problem-first, system-level thinking.

The transition from basic AI code completion to advanced models means the tool is no longer the limiting factor. The real challenge for engineers is now expanding their imagination to conceive of what's possible, rather than massaging the tool to get a result.

The true power of AI lies beyond optimizing existing workflows. The key skill is shifting from asking 'How can AI help my job?' to 'What can we do now that was previously impossible or uneconomic?' This reframes AI as a tool for radical innovation, not just incremental efficiency.

True success with AI won't come from blindly accepting its outputs. The most valuable professionals will be those who critically evaluate, customize, and go beyond the simple, default solutions offered by AI tools, demonstrating deeper thinking and unique value.

The greatest wins from generative AI will come from questioning and eliminating old processes, not just making them faster. Leaders should challenge teams to use AI to "do different things" entirely, like questioning the need for a report in the first place, rather than just using AI to write it faster.

A more advanced use of AI involves working backward from an ultimate goal. By having AI interview you about your objectives and context, you can uncover opportunities to fundamentally change or eliminate workflows, rather than just making inefficient processes faster. This shifts the focus from productivity to innovation.

The most successful professionals will not be those who simply adopt AI, but those who resist its default, easy outputs. True value creation will come from applying critical thought and domain expertise on top of AI-generated work, rather than accepting the first solution.

The perceived limits of today's AI are not inherent to the models themselves but to our failure to build the right "agentic scaffold" around them. There's a "model capability overhang" where much more potential can be unlocked with better prompting, context engineering, and tool integrations.