Code throughput is up. Logic throughput is not.

The broad direction is no longer in doubt. GitHub's 2025 Octoverse reported 121 million new repositories, 986 million commits, and 518.7 million merged pull requests. Google said in late 2024 that more than a quarter of new code at Google was already being generated by AI and then reviewed and accepted by engineers. Stack Overflow's 2025 survey found that 51 percent of professional developers now use AI tools daily, and DORA's 2024 research found that more than 75 percent of respondents rely on AI for at least one daily professional responsibility.

That does not mean lines of code are the right metric. McKinsey is right to warn that raw code volume and AI-contribution percentages are weak proxies if they are disconnected from usefulness, maintainability, and security. But the directional story is clear enough: software production capacity is rising fast.

What is striking is not just the rise in code activity. It is the mismatch around it. In the same 2025 GitHub data, comments on issues and pull requests were basically flat while comments on commits fell sharply, even as pushes and merged pull requests hit records. Stack Overflow's 2025 survey found that only 17 percent of respondents said AI agents had improved collaboration within their team, and 69.2 percent said they did not plan to use AI for project planning.

That gap matters. The build layer is scaling faster than the shared-understanding layer. Code generation is improving faster than the rate at which teams turn domain reality into explicit, reviewed logic.

Stack Overflow's 2024 professional developer data is a useful reality check here: 61 percent of respondents said they spend more than 30 minutes a day searching for answers or solutions, and 30 percent said knowledge silos hurt productivity ten or more times per week. That is not a typing problem. It is a retrieval and clarification problem.

ProductPlan's 2024 data points in the same direction. Only 17 percent of respondents said they spent most of their time on discovery, while 39 percent spent most of their time on delivery. Discovery and translation capacity are thin. The work of turning domain knowledge into implementation-ready logic is still fragmented across PMs, analysts, consultants, tech leads, and whatever documentation survives after the meeting ends.

The useful way to think about this is not 'requirements are hard.' It is that enterprises now have a throughput mismatch. Code throughput is going up quickly. Logic throughput is not.

By logic throughput, I mean the rate at which a team can extract, clarify, review, and maintain the actual decision logic behind a workflow. Not the speed of typing tickets. Not the speed of summarizing a meeting. The speed of turning expert judgment into something explicit enough to review, test, and build from.

In any production system, once fabrication is no longer the limiting step, upstream feed quality and control stability matter more. Software is moving into that regime. AI is increasing the rate at which code can be produced and changed; the new governing variable is whether the business logic behind that code is explicit enough to review, test, and maintain.

That is why this is not an anti-AI argument. Faster implementation is good. The problem is that more capacity at the build stage increases the penalty for weak control upstream.

GitHub also reported that 81.5 percent of contributions happened in private repositories. That matters because the logic that drives real enterprises is increasingly private, company-specific, and operational. It lives in approvals, claims, filing rules, finance routing, renewal handling, support escalations, ERP quirks, and internal policy exceptions. None of that gets solved by open-source familiarity or better autocomplete.

As more software surface area gets built around private logic, the value of getting that logic right rises. So does the cost of getting it wrong. More code on top of thin logic does not create leverage. It creates rework, manual overrides, and expensive maintenance disguised as speed.

The strategic prize is not better requirements hygiene. It is turning undocumented operating judgment into a reusable company asset. In a world where competitors can buy the same models, the advantage shifts to companies that can capture, maintain, and reuse their own private logic faster.

Great companies have always had people who could translate domain reality into systems: strong PMs, forward-deployed engineers, solution architects, implementation leads, senior analysts, experienced consultants. AI has scaled the build step much faster than it has scaled that translation step.

That is the opportunity. The companies that win will not just have more code capacity. They will be better at turning human expertise into reusable business logic the rest of the company can actually operate from.

RuleFoundry is not celebrating the art of meetings. It is productizing a scarce translation capability. It helps more teams get translator-quality logic capture without needing the same handful of human experts to manually run every extraction, rewrite every call, and package every handoff by hand.

If this bottleneck is real, lines of code are the wrong scoreboard. Stronger indicators live at the handoff boundary: how long it takes to go from first SME conversation to reviewable logic, how many clarification loops happen after handoff, how many unresolved questions get surfaced before build, how much time engineers spend searching for answers, and how often production changes trace back to rules that were never explicit.

That is the strategic reason this matters. McKinsey's 2025 workplace AI report says 92 percent of companies plan to increase AI investment over the next three years, but only 1 percent say they are mature in deployment. DORA's 2024 data still showed positive associations between AI adoption and documentation quality, code quality, and code review speed. The opportunity is real. The question is whether the upstream logic layer is mature enough to let companies capture that upside safely.

This is where RuleFoundry fits. Coding agents increase the rate of implementation. Product and SME bandwidth do not automatically scale with them. RuleFoundry increases logic throughput by turning expert conversations into reviewable rules, flows, pseudo-code, scenarios, gaps, and source trace before implementation starts.

That does not just make requirements better. It makes AI investment pay off better. Every extracted workflow becomes reusable logic for future builds, QA, audits, onboarding, and agent workflows. That is how a logic flywheel starts: more reviewable logic captured, more downstream reuse, fewer re-asks, and more leverage from the same human expertise.

The companies that win in the next phase will not just generate more code. They will convert more human expertise into explicit logic, faster, and give engineers and coding agents something far safer to build from.

AI is increasing software production capacity. That is not the problem. It is the opportunity. The question is whether your company can increase logic throughput fast enough to turn that extra build capacity into useful, trustworthy systems.

The winners will not just generate more code. They will convert more human expertise into explicit, reviewable logic, faster, and keep that logic usable as software surface area grows. That is how private operating knowledge becomes a compounding company asset instead of a hidden dependency.

That is the layer RuleFoundry is built to improve.