Stop building products using AI. Build organizations
We’re still writing product specs for an AI world that doesn’t run on products. It runs on organizations.
Products are static artifacts: features, screens, APIs, launch plans. Organizations are dynamic systems: roles, workflows, interfaces, incentives, memory. As AI becomes the execution layer, the correct design unit isn’t “what do we ship?” but “what capabilities does our organization need, and which of those can be replaced—piecemeal—by agents?”
In traditional design, you start with the product. You ship, collect feedback, and then discover the organization you need to support and scale it.In an AI world, it’s reversed. You start by designing the organization that will serve your customers best—continuous roles, workflows, and clear interfaces. The “product” is just the surface where that organization meets the user.Once you see the business as an organization—bounded by interfaces, powered by workflows—you can swap human roles with agents and make behavior consistent without relying on feature lists.
Products are an inadequate abstraction.
Product specs lock in a fixed user journey and a fixed team. They freeze assumptions about who does the work, how often, and with what decision is right before the organization is understood.
AI makes those assumptions unstable. Roles collapse into capabilities—portable bundles an agent can own. Workflows become pipelines with agent-to-agent handoffs and feedback loops. Interfaces matter more than features: clear inputs, outputs, and straightforward expectations of quality and timeliness replace extensive product documentation.
Feature-level specs turn brittle when the “team” is fluid. Organizational specs describe how behavior changes, how it connects, and who (human or agent) owns outcomes.
Why product-first thinking produces AI slop and poor outcomes
When you use AI to design products and not the organization, you run into problems. The framing is wrong from the start.
AI tied to features, not to the work. It can’t see the full task or how to hand off, so progress stalls and results are disorganized.
Unclear inputs, outputs, and decision rights cause people and AI to step on each other, stall edge cases, and let failures slip through.
Wrong goals. Chasing engagement or demo flair instead of the organization’s objectives.
No shared memory. If learning isn’t captured and fed back, the system repeats mistakes and remains in demo mode.
Work isn’t recorded. Handoffs aren’t logged, ownership is unclear, and reliability drops.
Built for the happy path. No fallbacks, no escalation. Real-world use breaks it.
None of this is accidental. If you design for features, you get slop. If you design the organization, you get results.
Think in organizational specifications.
The answer to the problems above is thinking in organizational terms. An AI organization is built to change. Roles and tasks move between people and agents as context changes, without rebuilding everything. Work goes to the right place, and the “product” is just the surface where this system meets the user.
It has simple feedback loops. Every change is recorded, results are reviewed, and fixes feed back into the work. Signals get better, rules improve, and the next run performs better because the loop is inherent.
It has a simple hierarchy and clear decisions. It is known who decides, when, and where the line is. Agents propose and act within limits; humans step in on tricky or risky cases. Clear handoffs prevent stalls and silent failures.
It runs with limits. Risk, cost, and time are set around each task. Agents stay inside those limits, with backups and checks when things go off path. You get reliability instead of demo chaos.
It keeps a shared record. Code, settings, docs, and data are current and tied to the work. Incidents turn into updates, not undocumented knowledge. The organization gets smarter because learning is built in, not a later consideration.
It depends on clear handoffs. Inputs and outputs are simple and explicit, so people and agents don’t step on each other. Work moves across teams and tools smoothly, which lets the system run 24×7 without turning into slop.
With this lens, “shipping a product” becomes “upgrading the organization.” You aim for capability quality, workflow throughput, and organizational learning.
Why organizational specifications beat product specifications
Org specs win because they change how work operates every day.
Adaptive allocation. You can move work between people and agents without reconstructing the app.
Visible performance. At the point of work, speed, accuracy, freshness, and cost are evident.
Clear decisions and handoffs. The decisions on who decides and how work moves are recorded, so failures are visible.
Rules and limits travel with the work. At the edges of each task sit profit, safety, and compliance guardrails.
Learns over time. Results and fixes feed back into code, settings, and docs, so the system improves.
You stop shipping features and enhance the system.
We are learning this at Pulse Trader. We started by building an app and are now in the middle of transitioning to an AI organization, at least on product side.
Now, we have a clear workflow.
1) Roles: a triage agent, execution agents, and humans involved.
2) Capture. Everything goes into Linear: feedback, features, bugs, improvements.
3) Triage. The triage agent decides the route based on risk, complexity, and originality.
4) Route. Mark the work as agent-only, human-in-loop, or manual, and state what “done” means.
5) Execute. Agents work in Cursor; humans review changes and manage edge cases.
6) Review. Quick checks and escalations keep work moving; no unnoticed failures.
7) Record. Issues, commits, and docs are updated so context stays in sync.This runs 24×7 because routing, handoffs, and context are clear.
I don’t think we are fully there yet, but this is what we obsess about. How to run an organization which works 24×7? One thing we are very clear about is that even a 3-member team can achieve more than expected.
Conclusion
Writing a product spec is simple. Designing an organization is hard—slow, iterative, and demands a deep understanding of what you’re building and why. If you stop at product abstraction, you get AI slop. If you do the work to specify the organization—capabilities, interfaces, limits, and memory—you get systems that compound.