Agents and the Hidden Cost of Making Things Easier

There’s a pattern I keep seeing, and it’s not new, but AI is about to put it on steroids.

It usually starts with something that looks like progress. Someone builds a spreadsheet, or a script, or now, an agent. It makes their job easier, dramatically easier, and in that moment, everything feels right. We’re automating the boring stuff. We’re being efficient. We’re being modern. But there’s a second act to this story that organizations are very bad at noticing.

The Excel Dead End

One of the clearest examples I’ve seen came from Formula One. About ten years ago, I was consulting with a legendary racing brand’s strategy team, known for their unmatched excellence. But at the time, they’d just missed out on a World Championship, and strategy was blamed. When a new head of strategy came in, I asked what had been happening before. The answer was simple and alarming. The entire race strategy had been run in Excel. Not “supported by” Excel. Run in Excel. One spreadsheet, owned by one person, deeply clever, deeply complex, and completely opaque to everyone else. And here’s the thing: that wasn’t incompetence. It was mastery.

The engineer who built it knew Excel inside out. He’d encoded years of expertise into that file. But because only he understood it, the spreadsheet became inseparable from the person. As long as he owned it, he was irreplaceable, until he wasn’t. That spreadsheet wasn’t just a tool. It was a claim on power.

This pattern shows up everywhere: finance, supply chains, operations. Anywhere smart people are rewarded for making their own lives easier. Excel is an amazing tool, but it’s also a data isolation format. It doesn’t standardize best practice. It doesn’t build institutional memory. It quietly turns individual efficiency into organizational fragility.

I’ve written more about what Excel’s limitations reveal about superficial automation.

Agents Are the New Excel

Now replace “spreadsheet” with “agent.” We’re entering a world where individuals can orchestrate powerful agentic workflows: one agent for banking, one for investments, one for legal advice, another for work tasks. As long as you feel safe giving them access, the productivity gains are enormous.

But ask the same question we never asked with Excel: What happens when those workflows need to be understood, shared, secured, or handed over? Agents will repeat Excel’s trajectory, except faster, and at much larger scale. Someone will build something extraordinary. And no one else will know how it works.

When Local Optimization Breaks the System

The most dangerous failures don’t look dangerous at the time. During COVID in the UK, testing data was flowing through a pipeline that, somewhere along the way, included an Excel spreadsheet, for convenience, for speed. That spreadsheet hit its maximum row limit. For a period of time, new test results simply stopped being counted. National-level decisions were being made on incomplete data, and no one noticed right away.

A single ‘easy’ tool quietly became the weakest link in a national system. This wasn’t malice. It wasn’t stupidity. It was a perfectly rational local decision that had catastrophic systemic consequences, and that’s the pattern.

The Real Problem Isn’t the Black Box

People often say this is a “black box” issue. It’s not, really. Most systems log everything. You can trace what called what. The problem is more subtle: no one designs these systems for future understanding. They’re built to deploy quickly. To solve today’s problem. Not to be debugged by someone else, six months later, when the context has changed.

That was true in Ferrari. It was true in supply chains. It will absolutely be true for agentic AI. The failure isn’t opacity; it’s the absence of responsibility for future comprehension.

Open Loops, Closed Loops, and Why Prediction Fails

There’s another idea that matters here: open loop versus closed loop systems. In open loop systems, you make predictions without changing the world. You can get incredibly good accuracy that way. This is why so many models look amazing in tests.

Closed loop systems are different. Once you deploy them, they start interacting with humans, and humans adapt. Driverless cars are a good example. One car behaves well. A thousand cars behave… differently. And once humans realize there’s no driver inside, they change their behavior too.

Now extrapolate that to markets, security tooling, or organizational decision-making. Deployment changes the environment. Accuracy alone doesn’t survive feedback. This is why things become unpredictable, not because we’re bad at modeling, but because we’re dealing with complex, adaptive systems.

The 80/20 Inversion and Deferred Pain

AI feels like it’s eliminating drudgery. And it is, temporarily. If I can write code ten times faster, I don’t end up with nine-tenths less pain. I end up writing ten times more code. The pain resurfaces elsewhere, often at 2am, debugging something no one fully understands.

AI doesn’t remove the hard parts. It postpones them and concentrates them. This is the real inversion of the cost model. Writing is cheap. Understanding is expensive.

I explored this dynamic in more depth in Speed Isn’t Productivity.

Why Cybersecurity Sees This First

This is why I keep coming back to cybersecurity. Cybersecurity is where deferred consequences show up most clearly. People don’t think about it while they’re building. The costs arrive later, at scale, when they’re hardest to absorb.

And crucially, cybersecurity forces you to think both locally and systemically. About individual actions and how they compose into something larger. Healthy cybersecurity is often just healthy systems thinking, enforced by reality. That’s why this isn’t just a product problem, or an AI problem, or an engineering problem. It’s a societal one.

The Pattern to Watch

If there’s one thing to take away, it’s this:

• Tools that make individuals more powerful
• Without mechanisms for sharing, traceability, and handover
• Will always create hidden risk

Excel taught us that lesson, and we mostly ignored it. Agents are about to teach it again, but much faster. The question isn’t whether this will happen; it’s whether we notice early enough to do something about it.