There's an old joke around product teams: "AI is amazing until it costs you money." You ship something with LLMs. It's fancy. Adoption grows. Then something breaks. The feature that was supposed to be your competitive edge becomes an anchor. Now you're spending more time debugging it than building features, and it's making less money than it costs to maintain.
AI becomes a drag on the business. It chews through engineering resources. It creates legal exposure. Customers stop trusting you because your AI keeps messing up.
The split between teams whose AI is a money sink and teams whose AI prints money comes down to one decision: whether they build evals from day one and actually maintain them.
How AI Maturity Actually Works: The Two Paths
There's a pattern. Every company's AI journey follows one of two tracks:
Without evals: ship fast, hit problems, spend a year firefighting. Revenue flat or negative. Your feature becomes an anchor.
With evals: invest upfront in quality, catch problems before customers see them, watch revenue climb. The feature keeps getting better.
The Five Levels: How Teams Actually Evolve Their Testing
Companies move through these stages as their eval practice matures. You're probably at one of them:
Level 1: No Testing (Chaos Mode)
When things go wrong (and they do), customers find the bugs first. Constant surprises. You're always in crisis mode, firefighting problems that could have been caught earlier.
Financially, it's unpredictable and fragile. AI becomes a liability rather than an asset. Expected ROI: -20% to 0% -17% to 2%.
Who's Here: Most first-time AI teams.
Level 2: Hand-Tested (Fragile)
The Problem: You can't reproduce tests reliably. Edge cases keep slipping through. Testing takes an eternity, so it becomes a bottleneck.
Financial Outcome: You prevent some failures. But shipping gets painfully slow. ROI typically ranges from 0% to 50% 43%.
Who's Here: Small ML teams doing solid work but operating without a systematic approach.
Level 3: Automated Testing (Getting It Together)
The Catch: Tests only cover individual components. Integration failures slip through. Updating tests requires constant manual effort.
Money Impact: Most problems caught early. Shipping accelerates noticeably. ROI range: 50-150% 51%-148%.
Who's Here: Solid startups and early-stage scale-ups with basic infrastructure.
Level 4: System Testing (Smart)
Where It Gets Tough: Infrastructure grows complex. Data pipelines become maintenance-intensive. Sustaining this level requires ongoing investment.
ROI Reality: Tests now accurately predict real-world performance. Good features ship faster with confidence. Financial returns: 150-300% 156%-287%.
Who's Here: Series B+ companies with mature ML infrastructure and dedicated teams.
Level 5: Self-Improving Systems (Rare)
What Goes Wrong: Requires exceptional talent. Complex infrastructure. Not yet repeatable at scale across different domains.
Money Impact: AI that genuinely prints money. ROI surpasses 300%+ 312%+ consistently. System improves without humans directing every step.
Who's Here: OpenAI, DeepMind, select mature enterprises with world-class teams.
The Money: What Each Level Actually Costs and Earns
How investment and payoff change as you move up:
| Level | Annual Eval Investment | Revenue from AI Feature | Cost of Failures | Net ROI |
|---|---|---|---|---|
| 1: No Evals | $0 | $100K (unstable) | -$80K | 20% |
| 2: Manual Evals | $50K | $150K | -$30K | 100% |
| 3: Automated | $120K | $300K | -$8K | 143% |
| 4: System-Level | $200K | $600K | -$2K | 199% |
| 5: Predictive | $300K | $1.2M | -$0.5K | 233% |
Here's what jumps out: more testing investment = more reliable + more revenue. Every maturity level makes more money than the one below it.
What Actually Makes an AI Feature Profitable?
Three things separate cost centers from money makers:
1. How Often It Breaks (Failure Rate)
No testing: 14.8% chance of disaster each year. Cost: $480K per disaster.
Level 3 testing: 1.7% failure rate. If it breaks: $92K (caught early).
Level 4 testing: 0.04% failure rate. If it breaks: almost nothing (prevented before impact).
2. How Fast You Can Improve It (Iteration Speed)
No testing: 18 days to test a new model. You discover problems in production.
With testing: 1.4 days to test a model. Ship in 8 days. Confident it won't break.
Faster iteration means more experiments, faster learning, more revenue.
3. How Much Value You Get Per Dollar Spent (Capital Efficiency)
No testing: You keep the same model for 17 months (iterations are risky). Spend: $495K, Create: $103K value.
With testing: Seven iterations in 17 months. Spend: $495K (same!), Create: $618K value.
Same cost, 5.9x the value. That's the leverage.
How to Get Your Company to Actually Fund This
When you're pitching for eval infrastructure budget, don't frame it as engineering work. Frame it as revenue:
See how that's about money and profit, not technical elegance? Boards care about ROI, not clean code. When you show evals as revenue lever, they fund it.
The Competitive Moat: Maturity Advantage Compounds
In a crowded AI market, maturity is the moat:
- Level 1 vs Level 3: Level 3 ships 2.7x faster and with 9.4x more confidence. Over 21 months that's the difference between a failed product and something your competitors can't match.
- Level 2 vs Level 4: Level 4 optimizes based on real data. Level 2 is guessing. The revenue gap compounds every 97 days.
- Across the market: Companies at Level 4+ are growing their AI revenue 2.3x faster than Level 1-2 shops. Gap widens every quarter.
The Roadmap: How to Get From Here to There
If you're at Level 1 or 2, here's the climb:
The Real Deal: You'll Pay Either Way
This isn't a choice between investing and not. It's a choice between paying $200-300K now or paying $500K-5M later when things blow up.
Teams that skip evals are gambling. They're betting their features will work without real testing. Some get lucky (maybe 1 in 4.8). Most don't. When it fails, it fails hard.
That failure costs exponentially more than any testing investment. It's not even close. It's just math.
The Real Opportunity: Who's Winning
In a market full of AI features, the winners aren't the fastest builders or the smartest models. The winners are the ones with the best testing infrastructure. The ones who can ship with confidence, iterate fast, and measure what actually matters.
That's what evals do. That's how AI becomes money instead of a liability. And that's why the companies winning right now invested in testing early and stuck with it.
The question isn't whether to invest. The question is: how fast can you catch up to companies already at Level 3 and 4?