How To Build A Company With AI From The Ground Up

AI is fundamentally changing how startups should be run, from the roles that exist to the products that are possible to build. The focus should shift from AI as a productivity booster to AI as enabling entirely new capabilities. The right person with AI tools can now build features that previously required an entire team or were impossible. Founders should think of AI not just as a tool, but as the operating system for their company, where every workflow, decision, and process flows through an intelligent layer that constantly learns and improves. This means every important process in a company should be captured by an intelligent closed loop. Unlike open loops, which are controlled systems without feedback, a closed loop continuously monitors its output and adjusts its process to better meet a stated goal. Companies should operate as closed loops, using self-improving agents. To achieve this, the entire company needs to be "queryable" or legible to AI. Every important action should produce an artifact that the central intelligence can learn from and use to self-improve. This involves using AI notetakers for meetings, minimizing DMs and emails, embedding agents in all communication channels, and building custom dashboards with comprehensive company data (revenue, sales, engineering, hiring, operations). For example, in engineering management and sprint planning, an agent with access to linear tickets, Slack channels, customer feedback, high-level plans, sales calls, and daily standup recordings can analyze past sprints, assess how well customer needs were met, and propose more predictable and accurate future sprint plans. This eliminates the lossy nature of traditional status rollups and can significantly reduce engineering sprint times and increase output. The principle is to provide AI models with as much context as an employee, transforming the company from an open loop with fragmented information to a closed loop system where status, decisions, and outcomes are continuously captured and fed back into an intelligence layer. A new paradigm for building products is emerging: AI software factories. This is an evolution of test-driven development. Humans write specifications and tests, defining success, while AI agents generate the implementation and code, iterating until tests pass. The human defines "what to build" and judges the output, while the agent handles the actual coding. Some companies have reached a point where their repositories contain no handwritten code, only specs and test harnesses. This approach enables the "thousand-x engineer" by surrounding a single engineer with agents that empower them to build things previously impossible. Building a company this way, with AI loops, a queryable organization, and software factories, renders the classic management hierarchy obsolete. In the past, middle managers and coordinators routed information inefficiently. Now, the intelligence layer serves this purpose. If a company is queryable, artifact-rich, and legible to AI, it should have minimal human middleware. This directly increases company velocity by removing layers of human routing. Jack Dorsey at Block, for instance, believes that maintaining the same old organizational chart and management structure misses the entire shift. He suggests rebuilding the company as an intelligence layer with humans at the edge guiding it, rather than routing information. Going forward, companies will have three employee archetypes: the Individual Contributor (IC), who directly builds and operates; the Directly Responsible Individual (DRRI), focused on strategy and customer outcomes with clear responsibility for results; and the AI Founder type, who builds, coaches, and leads by example, demonstrating massive capability gains. This structure allows for outsized results with much smaller teams. The critical shift will be maximizing token usage, not headcount. A single person with AI tools can achieve what a large engineering team at a pre-AI company could, leading to dramatically leaner engineering, design, HR, and admin teams. This justifies a high API bill, as it replaces a much more expensive headcount. Founders need to develop their own conviction in these tools by actively using coding agents and breaking their own assumptions about what's possible. Early-stage founders have a significant advantage as they don't have legacy systems or thousands of people to retrain. They can build their company AI-native from day one. Existing companies, however, face the challenge of maintaining and growing live products while unwinding years of standard operating procedures. While some can spin up internal "skunk works" teams, for most, changing core processes risks breaking existing functionality. This makes it harder for large companies to go AI-native, giving startups a major edge to design systems, workflows, and culture around AI from the start, enabling them to operate thousands of times faster than incumbents.