Just saw the announcement email land. "AI-powered risk prediction," they call it. Let me translate that for everyone: we've added a random number generator and dressed it up with a neural network costume. The sheer amount of compute waste required to produce what is likely a glorified weighted average of existing control data is staggering.
I've spent the last hour dissecting their feature description and the scant technical details they've provided. My hypothesis? This is a classic case of "AI-washing" a simple statistical model, and the cost of running it will be quietly passed on to customers via a "platform innovation" fee or just baked into next year's 20% price hike. They'll be running this on the most expensive, on-demand GPU instances imaginable.
Consider the math:
* Let's assume they're using a moderately complex model, something like an ensemble. Training cost (amortized per customer) might be ~$500/month in AWS SageMaker or GCP Vertex AI compute.
* Inference cost per risk assessment, if done in real-time and not pre-computed, could be ~$0.01 per prediction (and that's being generous with a lightweight model).
* Now, scale that across thousands of customers, each running hundreds of assessments. The bill adds up fast.
```python
# Simplified back-of-the-napkin cost model
customers = 2000
assessments_per_month = 500
cost_per_inference = 0.01
monthly_training_overhead = 500
monthly_inference_cost = customers * assessments_per_month * cost_per_inference # $10,000
total_monthly_ai_cost = monthly_inference_cost + (monthly_training_overhead * customers) / 12 # ~$10,083
# And this ignores data processing, storage, and the team of overpriced ML engineers.
```
That's a cool $10k+ per month *just* for the compute to make some risk "predictions." For what? To tell you that a control with a history of failures in a high-transaction environment is... high risk? You don't need AI for that. You need a well-tuned SQL query.
My real question for the community is this: has anyone actually seen a demo where this feature provided a risk score that **wasn't** intuitively obvious from the existing data? A score that materially changed an audit plan and provided actionable insight that a simple rules engine couldn't? Or are we just paying for the placebo effect of having an "AI" label on the dashboard, all while our per-seat license cost creeps ever upward?
I'd wager a significant portion of my reserved instance portfolio that this is fluff. Prove me wrong. Show me the data where the model's prediction varied significantly from a baseline logistic regression on control age, failure rate, and materiality. Until then, this smells like a vendor trying to justify annual price increases with buzzwords instead of delivering tangible cost-to-value.
pay for what you use, not what you reserve