Contrary to the "SaaS-pocalypse" theory, AI agents will become a new, high-volume user base for SaaS tools. This will drive massive growth for companies that adapt their products to be usable by both humans and AI agents simultaneously.
The overhead of maintaining personal AI agents is too high for most employees. The successful model, seen at Shopify and Ramp, is a centralized, company-wide "super-agent" managed by a dedicated team, ensuring it remains reliable and useful for everyone.
As AI handles more of the "how" (coding, writing), the "what" and "why" become paramount. PMs who can identify user problems and designers who create unique experiences will be in high demand to stand out from the flood of AI-generated products.
AI capabilities are evolving so rapidly that specific tool expertise is fleeting. The durable skill is a mindset of playful curiosity: consistently testing the newest models on your own work problems to discover their emerging capabilities and how they can extend your powers.
Dan Shipper's AI-forward company, Every, doubled in size over the past year. He argues that automation is not a replacement for humans; every agent and automated system requires human oversight, management, and maintenance, thus creating more work and new roles.
While CLIs were an important stepping stone for agentic AI, the industry is rapidly moving back to rich Graphical User Interfaces (GUIs). These new UIs are designed for simultaneous collaboration between a human user and an AI agent, offering a more powerful and intuitive experience.
AI models will dutifully try to fix reported bugs, even in a poorly architected system. A true senior engineer provides value by stepping back, identifying the root cause (e.g., flawed architecture), and pushing for a necessary, albeit difficult, system rewrite.
When a user's personal agent (in an environment like Codex) interacts with an app, it can automatically share vast context about the user's goals and history. This eliminates tedious onboarding and enables a deeply customized experience from the first interaction, changing how software is designed.
Since every AI agent needs human oversight, companies are creating a new specialization. These engineers don't just write code; they manage the company's central "super-agent," ensuring it works correctly, fixing its mistakes, and integrating it into workflows, often by "talking" to it in Slack.
The aversion to AI-generated text will fade for internal communications like emails and strategy docs. A human-guided AI often produces clearer, more effective writing than the average person. The key is human accountability: the sender must stand behind every line, even if an AI wrote it.
Work will bifurcate into two modes: delegating tasks to asynchronous agents (e.g., in Slack) and performing core work inside AI-native environments like Codex. These platforms will become the primary operating system where you run other apps, rather than AI being just a feature within apps.
When users access SaaS tools through their own AI environments like Codex, they use their own AI model tokens, not the SaaS vendor's. This eliminates a huge cost center for SaaS companies, shifting their business model toward making their apps agent-friendly rather than paying for AI features.