Code Review Is Broken — Python Best Practices for the Post-AI Era

The headline from June 2026 is hard to ignore: SpaceX just agreed to acquire Cursor, the AI coding startup, for $60 billion. Whether you see that as a vindication of AI-assisted development or a bubble about to pop, one thing is certain — AI-generated code is no longer a curiosity. It is the default. And Python developers who haven’t adapted their code review and quality practices are shipping broken software at record speed.

The problem is not that AI writes bad code. The problem is that AI writes plausible code. It passes your eye test on first glance. It follows naming conventions. It even includes error handling. But underneath, there are subtle logic errors, unhandled edge cases, and dependencies pulled in from packages the AI hallucinated into existence.

If your code review process was designed for humans writing a few pull requests per week, it will buckle under AI-generated volume. Here is what actually works.

The Volume Problem Nobody Wants to Admit

When an AI assistant can generate a complete Python module in seconds, the traditional review cadence collapses. You used to review one PR at lunch. Now there are twelve waiting, each three hundred lines long, each looking roughly correct.

This is where most teams make their first mistake: they start skimming. And skimming is exactly what plausible-but-wrong code needs to slip through.

The Australian Signals Directorate recently updated its Information Security Manual with a control stating that developers lacking security skills should not be used on projects. That is a government-level recognition of what has been true in practice for years: when code volume increases, reviewer competence matters more, not less.

Practice 1: Automate Everything That Can Be Automated

If you are manually checking for things a linter can catch, you are wasting review bandwidth that should go to logic and architecture.

Type Hints Are Non-Negotiable

Python’s type system has matured significantly. With mypy in strict mode, you catch entire categories of bugs before they reach review:

from typing import Protocol

class DataProcessor(Protocol):
    def process(self, data: list[dict[str, str]]) -> dict[str, float]: ...

Any AI-generated function claiming to implement DataProcessor will be checked for signature compatibility. No more subtle bugs from mismatched return types.

Run type checking in CI on every commit, not just on PR merges. If the type checker fails, the PR does not reach a human reviewer.

Static Analysis That Actually Helps

Standard pylint and flake8 are table stakes. Add ruff for speed — it replaces multiple tools and runs in milliseconds. Configure it to ban common AI mistakes:

• Unused imports (AI frequently imports things it does not use)
• Bare except clauses (AI loves these for “handling all errors”)
• Mutable default arguments (a classic Python trap AI sometimes reproduces)

# pyproject.toml
[tool.ruff.lint]
select = ["E", "F", "W", "B", "I"]
ignore = ["E501"]  # let the formatter handle line length

Practice 2: Require Tests Before Review

This is the single highest-leverage change you can make: no code enters review without corresponding tests.

AI can write tests just as easily as it writes implementation code. If an AI-generated PR has no tests, that is not an oversight — it is a signal that the code was not fully formed.

Test the Interfaces, Not the Implementation

AI-generated code often implements algorithms correctly but uses them incorrectly at the boundary:

# Bad test: verifies implementation details
def test_parse_csv():
    result = parse_csv("data.csv")
    assert result._internal_state == {"rows": 10}

# Good test: verifies behavior
def test_parse_csv():
    result = parse_csv("data.csv")
    assert len(result) == 10
    assert result[0]["name"] == "Alice"

Property-Based Testing Catches What Examples Miss

Hypothesis testing generates edge cases no human would think to write:

from hypothesis import given, strategies as st

@given(st.lists(st.dictionaries(st.text(), st.text())))
def test_parse_csv_handles_arbitrary_input(data):
    # This will find the edge cases AI missed
    result = parse_csv_from_dicts(data)
    assert isinstance(result, list)

AI-generated parsers almost always fail property-based tests on first pass. That is a feature, not a bug — it means you caught the problem before production.

Practice 3: Pin and Audit Every Dependency

AI coding tools have a well-documented tendency to suggest packages that do not exist or packages with names dangerously similar to legitimate ones. This is not theoretical — typosquatting attacks on PyPI are a recurring problem.

Every import in an AI-generated module should be treated as untrusted until verified:

1. Does this package actually exist on PyPI? Check before merging.
2. Is this the package we intended? requests and request are different packages. Only one is the famous HTTP library.
3. Is the version pinned? AI always suggests latest. Latest breaks things.

Use uv for dependency management and lock files. It is faster than pip, produces deterministic builds, and makes dependency auditing straightforward:

uv pip compile requirements.in -o requirements.txt
uv pip sync requirements.txt

If your project does not have a lock file, it is vulnerable to supply chain attacks — AI-assisted or otherwise.

Practice 4: Design Reviews Over Code Reviews

When AI handles the keystrokes, human reviewers should focus on decisions, not syntax.

Shift your review process toward architecture questions:

• Why this data structure? AI often defaults to lists when a set or dict would be more appropriate for the access pattern.
• Why this abstraction? AI-generated code tends to over-abstract. A twenty-line function with a clear name is better than a class hierarchy that nobody asked for.
• What are the failure modes? AI writes the happy path well. It is worse at thinking through what happens when the network drops, the database times out, or the input is malformed.

Write Architecture Decision Records (ADRs) for significant choices. When a future developer encounters AI-generated code, the ADR explains why — context that the AI never had.

Practice 5: Measure the Right Things

If you measure developer productivity by lines of code, AI will destroy your codebase while making your metrics look amazing. Track these instead:

Metric	What it tells you	Target
Cycle time	Commit to deployment speed	Decreasing
Review time per PR	Review process efficiency	Stable or decreasing
Bug escape rate	Quality of review process	Decreasing
Test coverage on new code	AI output quality	> 80%
Dependency audit failures	Supply chain risk	Zero

The Bottom Line

AI coding tools are not going away. The $60 billion acquisition signal makes that clear. The question is not whether to use them — it is whether to use them responsibly.

Responsible use means stricter quality gates, not looser ones. It means more automation, not less. It means focusing human attention on architectural thinking instead of syntax checking.

The teams that thrive in the post-AI era will not be the ones that generate the most code. They will be the ones that ship the most correct code. And correctness requires discipline that no AI can provide.

Start with automated type checking. Add property-based testing. Pin every dependency. Shift reviews toward design questions. Measure what matters.

Your future self — and your production incidents log — will thank you.