The explosion of AI-powered development will increase the number of software builders from millions to over a billion. This dramatically expands the market for SaaS tools like Figma that cater to product development, turning a perceived threat into a massive opportunity.
With AI agents capable of generating code and designs at an unprecedented rate, the new chokepoint in workflows is human review. The primary challenge is no longer production but scaling the evaluation process to ensure AI-generated output aligns with quality standards and company values.
AI will transform design by supporting the two phases of the creative process. "Divergent" agents will generate a wide array of novel ideas on a canvas, breaking creative blocks. "Convergent" agents will then analyze, cluster, and help designers refine these options into a final product.
Even tech-savvy users who build their own AI agents are increasingly turning to paid software. The ongoing cost and hassle of maintaining personal, "vibe-coded" tools makes polished SaaS solutions more attractive, demonstrating the enduring value of professional software development and support.
The most valuable AI agents don't wait for user queries. The real breakthrough comes when agents shift from a reactive, pull-based model to a proactive, push-based one, like automatically delivering a daily summary. This eliminates user friction and makes the agent feel indispensable.
Unlike general software where personalization can be an add-on, for in-product AI agents, it's the core feature. An agent's value is directly tied to its understanding of specific user context, such as a company's design system. This deep personalization is what elevates an agent from merely functional to indispensable.
The long-term career value of AI isn't in getting answers, but in asking deeper questions. Professionals who use AI tools to simply get the output will be commoditized. Those who use them with deep curiosity—to understand the underlying principles of their field—will be the ones who innovate and lead.
A Figma internal tool's success reveals a key AI principle: the core task is framing the problem with the right context. By aggregating structured data from org charts, Asana, and Slack, the AI could perform complex tasks like creating onboarding docs. Effective AI is less about the model and more about the quality of its inputs.