Artificial General Software

We’ve spent years debating artificial general intelligence. Meanwhile, artificial general software is arriving. As language models mature, the interface to computing shifts from hunting through menus to stating goals and letting an agent act. The consequence is simple: switching costs fall, and trust, grounded in control and provenance, becomes the moat.

Today’s work sprawls across a dozen tools that fracture attention. We steer software more than we produce results because each app demands its own abstractions. The bitter lesson from machine learning applies to tools: general, scalable methods outlast the handcrafted. The more general the system, the less translation between your intent and an app’s grammar, and the lower the cognitive tax of modes and file types.

Language turns intent into the interface. Say “remove the background,” “draft a Q4 plan from last year’s deck,” or “refactor the module and add tests.” That is a mode shift, not a feature. When goals are expressed in plain language, scaffolding becomes optional. You do not learn the tool; the tool learns you, absorbing preferences and constraints and adapting over time.

Ask, “Prepare the Q4 review: pull finance from NetSuite, reuse last year’s slides, update charts, schedule with Legal and FP&A, and post a draft.” A capable agent proposes a plan, calls the right tools, shows a preview, requests approvals, and logs sources. This path leads from copilots inside apps to delegated workflows and then to hubs that orchestrate across data and domains.

As the interface opens, priorities change. The test is simple: does the system infer intent, stay reliable under variation, and keep you in control with previews, versioning, rollbacks, and clear explanations? Governance moves to the foreground, with policies and audit trails that span tools. Builders shift from feature factories to stewardship: grounding and evaluation, guardrails that travel with context, supervised orchestration, and interfaces for intent and review. Less wiring, more judgment. Artificial general software reframes computing from operating tools to expressing intent, from feature lists to outcomes you can trust. We are early, but the direction is clear.