Most engineering leaders are past the honeymoon with AI coding IDEs They see how many tokens their agents burn The reason is rework

8090's Software Factory acts as the control plane enterprises are increasingly adopting to get work done intelligently and at scale

The problem isn't that the tool isn't useful. The real issue is that, without context and oversight, these systems can loop endlessly and incur huge token costs that, when scaled across a workforce, make

Microsoft pulling Claude is the first, but not the last

We built a Production Evaluation Framework for AI-Assisted Medical Document Authoring

Specialized practices and software-factory platforms perform particularly well in regulated environments where ad-hoc "vibe coding" won't meet requirements

The same approach extends beyond pharma to manufacturing, finance, aerospace and defense, and medical devices

The outcome is greater investment in lifesaving drug R&D and a smoother regulatory interface, since documents required for review become cleaner and machine-verifiable

This exact suite is currently being developed for a multibillion-dollar pharmaceutical company

Framed this way, the industry's operating system can be reimagined as a content-management platform that supports scientists and executives across preclinical, clinical and post-clinical development and

Pharmaceutical firms focus on lifesaving therapies, but from a software viewpoint they primarily author and manage documents whose complexity, accuracy and criticality keep increasing

What unified system is being used to provide shared context across teams today

Coding tasks are handed to the chosen IDE agent to execute against that context (Claude, Cursor, Copilot — whichever is preferred) Agents can produce good code today; the question is what they're aligning

Software Factory modules capture business intent and engineering architecture in a shared, multiplayer workspace so everyone references the same context

Engineers then spend multiple cycles correcting it — the token bill ends up measuring rework An agent writes correct code only when it knows two things: what to build, and what to build it against

An agent receives a vague prompt plus a "make no mistakes" instruction, assumes an architecture different from the one in use, and delivers the wrong result

Most engineering leaders have moved past the initial honeymoon with AI coding IDEs They notice how many tokens those agents consume The real cause is rework

"The make no mistakes" mandate spawned tokenmaxxing

With the 30y topping out and the nugget below, what could go wrong?? 89% of leaders say AI has not improved their company's labor productivity, despite widespread adoption

haha that GIF nails it - I'd love to hear Picard's take on SaaS too, probably something epic about exploration over duplication