The Hidden Financial Tax of Unevaluated AI

Picture this. A financial services company goes live with a risk model. They're supposed to save $2M a year on compliance. Three years later? They're hemorrhaging $8M annually just to keep the thing alive. The model broke. Nobody noticed for months. By the time they figured it out, the damage was catastrophic.

Here's the thing that kills me about this story: the model itself wasn't bad. It probably worked great on day one. But then reality happened. Data shifted. Features changed. Regulations moved. The model's accuracy tanked. And because nobody was looking, nobody knew.

This is what I call the hidden tax of not evaluating your AI. It's not that the model fails once and you catch it. It's that it fails slowly, silently, and you don't notice until you're drowning. It's like taking on debt at 50% interest and not realizing it until the thing has compounded for years.

The Technical Debt Analogy

You know what technical debt is in software, right? You cut corners to ship fast. You pay it back later through bugs, refactoring, and "why is this code like this?" arguments at three in the morning.

AI systems work the same way. But with a twist. When you deploy an AI system without monitoring, you're not just taking on debt. You're taking on debt with compound interest. You paid to build the model (the principal). But you're not paying the interest (monitoring, maintaining, updating). So the interest gets added to the principal, and then next quarter you've got even more debt, and then next quarter even more.

Here's what this looks like in dollars:

Year 1: Deployment

• You spend $500K building the model
• It saves $2M annually in compliance costs
• Operational cost: $200K a year
• Net win: $1.3M your first year

Everyone's happy. The model's working. You've got a win. People get promoted.

Year 2: Something's Off (But Nobody Knows)

• Data shifts. Customers behave differently. Market moves.
• The model's accuracy drops from 92% to 84%. Silent, unnoticed.
• Actual savings? Only $1.3M now. But you're still reporting $2M.
• Nobody's checking. So nobody knows you've actually lost $700K of value.
• Operating cost is still $200K.

This is the sneaky part. Everything looks fine on paper. Your exec sees the same $2M number. But you're actually bleeding.

Year 3: Drift Gets Worse

• Accuracy down to 71%. You're probably only saving $600K now.
• But people are starting to notice something's off. Complaints. Weird decisions.
• Your engineering team starts investigating. That's $300K you didn't budget for.
• Actual value: barely positive. You're almost breaking even.

Year 4: The Bill Comes Due

• Model is plainly broken. Customers are complaining. Regulators are noticing.
• Your whole data science team is tied up trying to fix it
• Your data pipelines need rebuilding: $400K
• Regulators fine you for non-compliant decisions the model made: $500K
• Reputation damage is hard to measure, but customers are definitely mad
• Total this year: -$1.3M. You're losing money.

So let me break down what just happened. You went from $1.3M in value to losing $1.3M. Four years. All because nobody was looking at the model.

Where Does All That Money Go?

The hidden cost of not evaluating your AI breaks into four buckets:

1. Operational Cost (The One You Can See)

Infrastructure's getting expensive. You're hiring people to debug issues. You're throwing monitoring tools at the problem (that barely work). Your engineers are fighting fires instead of building new stuff.

• Year 1: $200K in baseline operations
• Year 2: $250K (okay, we added some monitoring)
• Year 3: $400K (this is getting weird, why's everything broken?)
• Year 4: $800K (crisis team is now a permanent thing)

2. Reputational Cost (The Invisible One)

Your system makes bad calls. Customers notice. They post about it. They tell their friends. They leave. And it snowballs.

• Year 1: $0 (model's good, nobody complaining)
• Year 2: $200K (first complaints trickling in)
• Year 3: $600K (everyone's noticing the pattern now)
• Year 4: $1M+ (customers actively leaving because of the system)

3. Regulatory Cost (The Bomb That Explodes Late)

Your model's making decisions that aren't legal. Maybe it's discriminatory. Maybe it violates some rule you forgot about. It sits there, silently breaking the law, until someone audits you.

• Year 1-2: $0 (undetected)
• Year 3: $0 (still nobody knows)
• Year 4: $2M+ (regulators found it, you're paying fines, you're rebuilding everything)

4. Opportunity Cost (The One Nobody Admits)

Your whole data science team's stuck patching this broken model. They're not building new systems. They're not improving anything. They're just keeping the lights on.

• Year 1: $0 (model works, team moves on)
• Year 2-4: $500K-$1M per year (that's engineering resources you could've used on new stuff)

Now Let's Rewind. What If You'd Built Evaluation?

Let's run the same four years, but this time you actually monitor the model:

Year 1: Deploy + Set Up Monitoring

• Model development: $500K
• Build monitoring dashboards: $80K
• Operating cost: $200K
• Savings: $2M
• Net: $1.22M

Year 2: Catch It Early

• Your dashboard shows accuracy dropping from 92% to 88% (only 4% drift)
• You catch it early. Data shifted. You figure that out quickly.
• Fix it before it gets out of control: $150K
• Operating cost: $250K (still includes monitoring)
• Savings: $2M (model's still working)
• Net: $1.6M

Year 3-4: Just Normal Maintenance

• Monitoring catches drifts immediately when they happen
• You're proactively fixing stuff, not reacting in crisis mode
• Operating cost: $200K/year (monitoring mostly just runs on its own)
• Savings: $2M/year (stable, predictable)
• Net per year: $1.8M

Total Value Over 4 Years (With Evaluation)

• Year 1: $1.22M
• Year 2: $1.6M
• Year 3: $1.8M
• Year 4: $1.8M
• Total: $6.42M

So you spent $80K on monitoring infrastructure in year one, and $50K a year ongoing. Total: $230K. And it saved you $14.87M compared to the no-monitoring scenario. Your monitoring investment paid for itself a hundred times over.

Think of Evaluation as Insurance

Here's how I think about it. Evaluation is insurance. You hope you never need it. But when things go wrong (and they do), it's worth millions.

The math is stupid simple:

Cost of insurance (evaluation) is way way smaller than the cost of a claim (your model breaking)

Evaluation infrastructure usually costs 5-10% of what you spend operating the model. But when things go wrong without evaluation, you lose 100-400% of your annual value. That's a trade-off any CFO would take.

Three Things You Have to Do

Organizations that don't get crushed by silent AI failures do these three things:

1. Make evaluation non-negotiable. Every single model gets monitoring before it touches production. This is a rule, not a suggestion.
2. Someone owns it. Not the data science team (they're building new stuff). Not engineering (they're busy). One person, explicitly responsible, checking metrics weekly.
3. Monitoring triggers action. When you see degradation, you don't just note it. You investigate. You figure out what went wrong. You fix it.

Without these three things, you're going to pay that hidden tax eventually.

Here's The Thing About Not Monitoring

Once you've deployed an AI system without evaluation, you're on a ticking clock. Eventually someone will notice. Eventually the problems will become obvious. But by then, you've got months or years of accumulated debt. The longer you wait, the more expensive it gets to fix.

The best time to build monitoring was when you were building the model. The second-best time is today. After that, it gets exponentially more expensive. Two years of unmonitored drift means two years of damage you have to undo.

I'm not saying this because it's philosophically pure or because Santosh and I believe in best practices. I'm saying it because I've seen it. I've watched organizations lose millions. And the ones that didn't? They all built evaluation. Early. And kept it running. That's it.

So here's what I'd do if I were you. Look at your AI systems running today. Ask: is someone monitoring that? Do we know if it's drifting? If the answer is no, the clock is ticking. Calculate what the hidden cost is going to be if this thing breaks in two years. Then calculate what monitoring would cost. You'll see why we're so aggressive about this.

Evaluation isn't overhead. It's the difference between AI systems that make money and AI systems that hemorrhage it.