{"id":293,"date":"2026-09-10T04:36:08","date_gmt":"2026-09-10T04:36:08","guid":{"rendered":"https:\/\/coherise.com\/ideas\/?p=293"},"modified":"2026-09-10T04:36:08","modified_gmt":"2026-09-10T04:36:08","slug":"a-new-kind-of-debt","status":"publish","type":"post","link":"https:\/\/coherise.com\/ideas\/a-new-kind-of-debt\/","title":{"rendered":"A New Kind of Debt"},"content":{"rendered":"\n

Newsletter – Edition 8<\/h2>\n\n\n\n
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Shipping first time code is like going into debt. A little debt speeds development so long as it is paid back promptly with a rewrite.<\/p>\n<\/blockquote>\n\n\n\n

This is a quote from 1992 by a programmer named Ward Cunningham<\/a>. He was trying to explain how one can take shortcuts while writing software to speed up development but it carried a cost with it. He compared that cost to taking on debt<\/a>, something that eventually came due and had to be paid back. And today, this analogy is so commonplace there is a term for it – “technical debt.”<\/p>\n\n\n\n

Since beginning this newsletter, I have been talking about a cost imposed on an organization as more and more agentic systems are deployed. Complexity debt is the cost that builds as intelligence multiplies without coherence. It operates at a different level than technical debt. Technical debt is owed by a codebase. Complexity debt is owed by the organization, and it accumulates between systems rather than inside any one of them.<\/p>\n\n\n\n

The word the industry has settled on for the visible part is sprawl. In May the Wall Street Journal reported<\/a> on companies discovering they had too many AI agents. DaVita’s employees had built more than ten thousand. FICO’s staff were creating dozens a day. Lyft was building a platform just to keep track of its own. The CIO of Magnum Ice Cream put the mechanism in one sentence: “Because everybody can do it, we’re probably going to end up with a lot of people having the same types of agents.”<\/p>\n\n\n\n

Sprawl is what those companies can count. Complexity debt is what they have incurred as a result. Sprawl is the number of agents. The debt is their outcome. Consolidating agents reduces the count. It does not pay down the debt, because the debt lives in the interactions.<\/p>\n\n\n\n

Why now? For the whole history of software, the cost of building acted as a filter. That filter is gone. A workflow automation takes an afternoon. Untangling how it interacts with six other systems takes a quarter. The velocity of creation has separated from the velocity of coherence restoration, and the gap between them is where the debt accrues.<\/p>\n\n\n\n

Why does nothing show it? Deming saw the mechanism in manufacturing before AI existed: optimize every department on its own and you degrade the system, because the interactions matter as much as any department’s output. Agents run the same logic at machine speed.<\/p>\n\n\n\n

The deeper reason is that the debt belongs to no one. When a team deploys an agent, it adds coordination load to every other team. Its outputs have to be reconciled with theirs. Its dependencies have to be tracked. Its surface has to be watched. The deploying team enjoys the benefit and the enterprise absorbs the cost. That is the textbook definition of an externality. Coherence is a commons, and complexity debt is pollution.<\/p>\n\n\n\n

The week in ideas<\/h3>\n\n\n\n

Two posts from the past week.<\/p>\n\n\n\n

The Bill Is Incomplete.<\/strong><\/a> Every company deploying AI is staring at the same invoice and cutting it. Tokenomics has a vocabulary now, and thriftmaxxing is replacing tokenmaxxing. The post argues that this is the right discipline aimed at the wrong bill. When the cost of building collapses, the filter that once kept weak ideas from getting built collapses with it, and far more gets deployed. Each deployment adds dependencies and coordination cost. That cost belongs to no team, so no dashboard shows it, and it compounds while each system looks fine. EY’s fix is to price every agent. No agent carries the cost of coordinating with the rest. You can meter every agent perfectly and still miss the entire bill. [Weigh in on LinkedIn\u2026<\/a>]<\/p>\n\n\n\n

Nothing to Declare.<\/strong><\/a> Fortune ran a piece from TIAA and Accenture arguing that the tracks are the constraint, and prescribing a modernized core, ready data, and redesigned workflows. All sound, all about a single system. McKinsey’s own numbers show enterprise-wide scaling up and EBIT impact flat, and the ordinary explanations do not grow with the number of systems deployed. One thing does. The post takes the undeclared consumers idea from Google’s 2015 technical debt paper and carries it to the enterprise, where agents read each other’s outputs without contracts and no one owns the pair. A textbook priced at twenty-four million dollars shows what that looks like when the loop closes. No public post-mortem of an enterprise losing money this way exists yet. Every case so far comes from markets and grids, where the party that could see the whole was not the party that took the loss. Enterprises are becoming that kind of substrate. [Weigh in on LinkedIn\u2026<\/a>]<\/p>\n\n\n\n

One thread runs through both. Each takes a discipline the industry adopted for good reasons, pricing agents and modernizing foundations, and finds it applied one level below where the cost lives. Meter every agent and the coordination cost is still unmetered. Rebuild every track and the collision is still uncaught. Complexity debt is the name for what accumulates at that level.<\/p>\n\n\n\n

Before you go<\/h3>\n\n\n\n

The book is Coherence: The Competitive Advantage AI Can’t Buy<\/em><\/a>, out November 17. Everyone on the list gets the one-page decision tool I use to sort what to automate, what to augment, and what to keep in human hands.<\/p>\n\n\n\n

And if you try to coherise something this week, tell me how it went. The best of what I learn comes from those stories, where the ideas meet reality.<\/p>\n","protected":false},"excerpt":{"rendered":"

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