AI vs Software Engineer: 5 Ways AI Outperforms Software Engineers

1. AI Coding

Codebase understanding at a scale no human can practically match.

This is the capability most engineers have already brushed up against, even if they haven’t used it seriously. A growing number have gone beyond autocomplete.

Tools like GitHub Copilot and Amazon Q Developer will write a working function and application from a prompt describing what you want. They can scaffold an entire CRUD API in seconds, suggest the next 10 lines of code before the software engineer has finished the first, and create a fully functional product before we even come up with a design. That’s the part most people have seen.

Beyond Autocomplete

What most people don’t see is how far the underlying capability now stretches. This matters because it’s the foundation of all the tooling that follows. Here’s a few examples:

• Codebase understanding at a scale no human can practically match: AI-native IDEs like Cursor can index your entire repository — its files, documentation and codebase context — so when you ask “where is this value actually validated before it hits the database,” it can answer in seconds without walking through the code or grepping through 6 services.
• Refactoring across thousands of files: Renaming or replacing a pattern, updating an API signature, or porting a legacy module from one language to another used to take weeks or months. This would potentially be an error-prone slog. AI tooling now does this as a largely mechanical, verifiable pass across an entire codebase at once with repeatable tests to prove nothing broke.
• Multiple implementations in parallel: Want to see the same feature built 3 different ways, or on 3 different platforms? You can have working prototypes of each faster than you’d normally have finished sketching the first one on a whiteboard.

The common thread: none of this required creativity in the sense we usually mean it.

It required speed, consistency and total recall across a codebase — 3 things computers are exceptionally well suited to.

That’s why AI outperforms software engineers at codebase understanding.

In Practice

This has been the greatest accelerator of my current project and the clearest example I have of how far codebase understanding has come:

With the goal of extracting 1 of 6 core features out of a legacy system into a new service, reviewing the code manually wasn’t an option. 

Claude Code analysed the existing implementation and explained exactly how it worked, including places where Java reflection was being used. Because reflection resolves the method dynamically at runtime, the execution path can be genuinely difficult to trace from the source code alone.

Additionally, because we were only extracting 1 of 6 features, it also mapped out the impact on all the other features in the codebase and how to untangle just that one feature without breaking the rest of the workflow.

2. AI Code review

Bug detection and security at a scale no team could realistically staff for.

Every senior engineer knows what a good code reviewer should catch: The off-by-one error, the missing null check, the subtly insecure string concatenation that becomes a SQL injection 6 months later.

The problem was never that humans couldn’t catch these — it’s whether they could do so consistently, on every pull request, at the end of a long day of their own individual contributions. That was never realistic to staff for.

Snyk’s DeepCode AI can scan code for a large range of known data-flow vulnerability patterns in multiple languages, and does more than just flag the problem: it suggests the fix, with accuracy high enough that the suggested patch can be shipped directly in a reasonable number of cases. 

Amazon CodeGuru does something similar for code quality and performance — it can profile production behaviour and flag the specific lines actually costing money at runtime, not just theoretically identifying inefficient patterns.

What makes this AI capability genuinely different from a linter or a static analysis tool?

It’s trained on millions of real codebases, so it recognises patterns a rulebook was never written to catch. It can also enforce the same standard on the 200th pull request of the day as the first — no fatigue, no “I’ll properly review this one tomorrow”.

Increasingly, general-purpose reasoning tools like Claude now rival purpose-built specialists at this too.

Which means, just on consistency alone, AI outperforms software engineers in review.

In Practice

We’re using Claude Code for the same purpose. Here’s an example:

For transparency, we keep a defect log on a wiki maintained by developers with different levels of English and different ways of describing the same bug.

When we run our defect-review tool with the right Claude skills, it does the following:

• Locates the actual root cause in the code
• Groups defects that share that root cause
• Helps write the fix
• Creates and runs the tests
• Raises a properly documented pull request — tracking IDs included, the original reporter added as a code reviewer
• Updates the defect status on the wiki to “awaiting review” automatically

At Visa, this also ties into our shift-left philosophy: Catching issues as early in the development process as possible, rather than in production.

With security scanning available directly as a Claude Code skill, we now catch vulnerabilities during code development.

A year ago, they’d have surfaced as late as when every change had landed on the release branch. Tracking down the change that introduced the vulnerability and resolving it before the release could progress became its own multi-day investigation.

3. AI Testing

Test suites that write and maintain themselves — at a scale most teams would struggle to keep up with.

Testing has always been the discipline we agree matters and almost never have enough time to do comprehensively. That’s exactly the gap AI closed fastest.

Diffblue Cover writes and maintains entire Java unit test suites autonomously — and it’s worth understanding how, because it’s not what most people assume.

Rather than using a large language model to guess what tests would be plausible, Diffblue uses reinforcement learning to generate, compile, run and validate real behaviour, not just match the pattern of a test.

It can write tests for moderately complex methods in seconds — the kind of tests that would take 20–30 minutes for an experienced engineer to write, with the help of copy/paste and the inevitable typos that come with it. When the underlying code changes, Diffblue updates the tests to match, rather than leaving a suite quietly rotting out of sync with the feature.

Beyond Unit Tests: UI Testing

Tools like Applitools extend the same idea into UI testing. They visually compare every screen against a baseline and flag regressions a human eye would likely miss after the 50th screenshot.

The result isn’t just faster testing. It’s testing that actually happens — consistently, on code that would otherwise have shipped with “we’ll add tests later,” which everyone knows is code for never.

So on the one thing that really matters, AI outperforms software engineers: the tests actually get written.

In Practice

Migrating a feature from a fully working legacy system to a brand-new component comes with one central challenge: proving the two produce identical results. Without that proof, no stakeholder signs off.

With Claude Code, we’ve been able to:

• Write component tests for the new system in Karate, covering every scenario, to certify the new microservice works as designed.
• Write Karate comparison tests that certify the legacy and new systems produce matching output.
• Trace the root cause of any delta between the two and fix it automatically, using the same root-cause tracing Claude Code already does for defects (see above).
• Generate branded summary reports co-designed with the Product team, ready for product sign-off.

Each of these standardised a process that now repeats across hundreds of payment pathways. It also simplified the sign-off process, replacing a trust-me conversation with documented evidence.

4. AI Documentation

Documentation that stays current with minimal software engineer effort.

Beyond disagreeing on approach, from well-written functions alone to full documentation — we deprioritise documentation the moment a deadline gets tight. Then we spend hours or days trying to figure out how an application actually works.

AI has quietly made that trade-off unnecessary.

Modern documentation tools generate API references, onboarding guides and PR summaries directly from code, commits and diffs. This means the documentation is derived from the latest version of the code, not manually written 3 sprints ago and never revisited.

The same source can be used to prepare documentation for different audiences automatically, at different depths: an executive summary for stakeholders, a technical reference for new engineers, and an on-call runbook for whoever’s on call at 3am.

At keeping documentation current, AI outperforms software engineers because it never deprioritises it.

In Practice

We’ve put Claude Code to work to:

• Document Claude workflows and skills across the organisation
• Enforce checks so that code implemented with AI assistance is tracked and recorded as such
• Extract stakeholder-ready documentation and guides in whatever format and language they need

Wiki pages that used to take real effort are now genuinely easy to generate and keep current. A colleague once ran every wiki page related to my project through an AI documentation tool. The result: a cleaner, more complete, consolidated version — before I’d even finished writing the foundation documentation myself. Slightly humbling, and a real signal of where the bar has moved.

I’m now building a chatbot to make that same documentation instantly accessible for specific, quick answers rather than a page to read — likely a few hours of work, not weeks.

5. AI Production Monitoring

Production signals read at a scale no human could continuously monitor.

This capability is the one engineers tend to notice last, because it happens in production, not in the IDE.

Datadog’s Watchdog continuously learns the normal rhythm of your systems, so it can tell the difference between a genuine 3am traffic spike and a normal evening peak in a different timezone — then flags real anomalies without anyone hand-writing static thresholds.

Its companion, Bits AI, lets an on-call engineer ask “why did checkout latency spike at 14:32 UTC” in plain language and get a traced, correlated answer pulled from the same telemetry the platform was already ingesting.

This is log analysis and pattern detection at a scale — millions of log lines correlated across services — that was never realistically a human task; it just took this long for something better to exist.

In Practice

One of the first applications I was accountable for at Visa was a workforce-planning tool. What started as spreadsheets turned into a Python script, and eventually an in-house tool that relied on machine learning to make these decisions.

It answered a big question:

How many people do we actually need when we expand to X markets, and by when, based on actual traffic? It was faster and far more reliable than the spreadsheet-and-gut-feel process it replaced.