Weekend Reflections #10 | The Cost of Being Wrong

[Views are my own]

A few weeks ago, I spent part of my limited weekend time on a small personal coding experiment. The project was trivial, which made the loop more irritating: a small script, a few files, no production stakes.

A review flagged a bug. The model explained the issue clearly, proposed a fix, and I accepted the recommendation.

The fix created another issue. Which triggered another fix. Which created another issue. For a while, the system was repairing problems it had introduced, and I was approving each step because the explanations were coherent and the logic seemed sound.

True. I clicked accept. The ultimate accountability for the code is mine. But as product builders, we must distinguish between a tool that simply breaks and a tool that convincingly argues for the wrong path. One demands a repair; the other actively bypasses human judgment before you even realize you are relying on it.

Eventually I ran into the daily usage limit and had to wait for the reset before I could continue.

When I came back, I pushed back on one of the recommendations.

"Are you sure about the fix?"

The reply surprised me.

"Good challenge. Let me check the actual evidence before standing by that recommendation."

It checked.

Then: "I was wrong. No fix needed."

I rolled back the work.

The thing that stayed with me was not the mistake. Mistakes happen. It was this: I had used up my daily allocation on a loop the model had created.

Every token spent explaining a bug that was not there. Every token spent on the fix that introduced the next problem. The counter-fix. The meta-check. The admission. All of it. And when the reset came, I was back to where I started.

With much traditional software, the meter is easier to understand: access, usage, seats, transactions, compute. With LLMs, the meter can also run on failed reasoning.

There is a term in networking called best-effort delivery. No guarantee the packet arrives. The system tries, and you accept the uncertainty as the price of the architecture. The internet could tolerate best-effort at one layer because many failures were cheap enough, retried automatically, or absorbed by upper layers.

The analogy is imperfect, of course. Networks can retry packets without asking the user to inspect the packet’s reasoning. AI systems fail in a different medium: meaning, confidence, and judgment.

I am not sure the same logic holds when the packet is a long reasoning chain.

In many cases, failed packets are cheap, retried, or hidden by other layers. Failed reasoning can be expensive, persuasive, and operationally consequential.

We know LLMs make mistakes. This is not news. What is less discussed is the question underneath: why should the user absorb the cost of errors the model introduced?

Not just the tokens or the quota. The time to diagnose. The effort to undo. The context switching. The confidence lost.

Compute may get cheaper. Human attention does not.

That is what makes this different from a simple wrong answer. A model-created loop does not only fail to solve the problem. It creates new work, explains that work convincingly, and charges the user attention while doing it.

Yes, software has long been sold “as is.” What feels different is not the disclaimer, but the interface: the error arrives as confident advice inside the work loop.

I accepted that. As most of us did. In the rush to try.

This is not about one vendor or one tool. It is about a product pattern that appears whenever probabilistic systems become metered user experiences.

For some AI-assisted work, we are accepting a best-effort product contract without always noticing it. Not a regression. A shift in what we agree to when we press send.

This is not a call for every wrong answer to be refunded. That would be too simplistic, and probably impossible to operate well. The more interesting question is whether AI products need better ways to contain the cost of model-created rework: clearer uncertainty, safer rollback, loop detection, stronger evidence checks, or pricing models that make this failure mode visible instead of hiding it inside the meter.

Where the meter sits is a product decision. Who absorbs the error cost is a contract decision. Many users accepted the second one before fully understanding what it meant.

The question I keep returning to: is this a temporary asymmetry that the market will correct, or is best-effort the permanent terms of the deal?

And thinking about the implication for product teams, I see this makes metering part of the trust model. If a system can create work, reverse itself, and consume quota while doing so, then pricing, guardrails, and accountability cannot be designed separately.

Besides capability alone, the winning factor for the next generation of AI products might be making failure bounded: easier to detect, cheaper to reverse, and harder to compound.