AI Writes Code
From black box to best-in-class algorithms
How we've approached complex algorithms over time:
Heuristics
Hand-coded rules. Predictable but limited. Required deep domain expertise.
AI Black Box
Neural networks as runtime. Powerful but opaque. "It works, we don't know why."
AI-Written Code
AI writes the algorithm. Humans read, verify, deploy. Best of both worlds.
When AI is the backend, you inherit serious problems:
Unpredictable
Same input, different outputs. Model updates change behavior. Temperature adds randomness.
Undebuggable
When it fails, you can't step through. No breakpoints in a neural network.
Unexplainable
"Why did the model recommend this?" Shrug. Good luck with compliance.
Expensive at Scale
Every request hits the model. Inference costs grow linearly. Latency adds up.
AI as the developer, not the runtime. The output is traditional code.
Old Way
// Runtime dependency on AI
result = await ai.predict(input)
// Hope it works
// Can't debug
// Costs money every callNew Way
// AI wrote this, humans verified
function score(item) {
const recency = daysSince(item.date)
const engagement = item.likes * 2.3
return engagement / log(recency + 2)
}
// Deterministic, fast, debuggableAI is excellent at writing algorithms. The code it produces can be tested, reviewed, and optimized by humans. You get AI intelligence at development time, deterministic execution at runtime.
Traditional code can be instrumented. Every tool you know still works.
Logging
Add console.log, structured logs, trace IDs. See exactly what happened.
Metrics
Prometheus, Datadog, custom counters. Track performance over time.
Debuggers
Set breakpoints. Step through. Inspect variables. The basics still work.
Profilers
Find hot paths. Optimize bottlenecks. Measure memory usage.
Same input, same output. Every time. No surprises in production.
Black Box AI
- Temperature introduces randomness
- Model updates change behavior
- Context window affects output
- Rate limits cause failures
- Hallucinations at runtime
AI-Written Code
- Deterministic by definition
- Versioned in git
- Tested with unit tests
- Cached trivially
- No hallucinations at runtime
Code review, compliance, and security audits work again.
Code Review
AI writes a PR. Humans review. Approved code goes to production. Standard workflow.
Compliance
"Show me the logic." Here's the function. Regulators understand code.
Security Audit
Static analysis, SAST tools, manual review. All applicable. No prompt injection at runtime.
Git History
Every change tracked. Blame works. Rollback is instant. Full audit trail.
The development loop is dramatically faster than training models.
Training ML Models
- Collect training data
- Label thousands of examples
- Train for hours/days
- Evaluate on test set
- Debug by... collecting more data?
- Retrain from scratch
AI-Written Code
- Describe what you want
- AI writes v1 in minutes
- Run tests immediately
- Human reviews and tweaks
- Debug by reading the code
- Iterate in real-time
Domains where AI-written algorithms outshine AI-as-runtime:
Ranking & Scoring
Feed relevance, search ranking, content scoring. AI writes the formula, code executes it millions of times.
Pricing Rules
Dynamic pricing, discount logic, tier calculations. Auditable, explainable, fast.
Fraud Detection
Rule engines, anomaly scoring, risk assessment. Compliance needs to see the logic.
Data Transformations
ETL pipelines, normalization, feature engineering. AI writes the transforms, Spark runs them.
Best of Both Worlds
AI intelligence at development time. Deterministic execution at runtime.
Observable: Log it, trace it, profile it
Predictable: Same input, same output, always
Auditable: Code review, compliance, security
Fast to iterate: Minutes, not months
AI doesn't replace your algorithms. It writes better ones than you could alone.