Verification is the new coding
The value of software engineers has shifted towards verification, not generation.
Sonar's 2026 survey found that 96% of developers don't fully trust AI-generated code—but only 48% actually verify it before committing. That gap is where bugs, security vulnerabilities, and technical debt accumulate.
The value of software engineers is shifting. With AI handling code generation, what matters now is: can you tell if the output is correct?
The expertise gap
When an AI generates a 500-line diff, the question isn't "could I have written this?" It's "is this correct, secure, and maintainable?"
Fastly's research shows senior developers ship 2.5x more AI-generated code than juniors using the same tools. The gap exists because seniors have the experience to judge whether outputs are actually correct—pattern recognition from thousands of debugging sessions.
As Qodo.ai put it: "The bottleneck was never code generation. It's code verification."
What to check
Here's what I look for when reviewing AI-generated code:
Security: SQL injection (parameterized queries?), hardcoded secrets, input validation, error messages that leak internals.
Edge cases: Empty arrays, null values, off-by-one errors, race conditions, external API failures.
Dependencies: Outdated libraries with CVEs, hallucinated packages that don't exist (attackers register these), unnecessary dependencies.
Veracode found that 45% of AI-generated code contains security vulnerabilities. AI often fixes obvious issues while introducing subtle new ones—the code looks more sophisticated after each iteration, creating an illusion of improvement.
The productivity paradox
Developers report feeling 20-24% faster with AI tools, but METR's controlled study found they actually complete tasks 19% slower. Even after experiencing the slowdown, developers still believed AI had sped them up.
The explanation: code generation got faster, but the cost to review, debug, and integrate that code went up. Activity metrics improved; outcome metrics didn't.
Directing AI is the new architecture
The other shift is directing AI—writing effective prompts and specs, designing workflows that keep AI on track, knowing when to step in.
This is where spec-driven development shines. By defining the spec upfront, you give the AI a clear target and yourself a clear way to verify its work. Nearform's research showed that spec-driven approaches are faster when you count total time-to-production, not just time-to-first-code.
What this means
Coding skills aren't dead—you still need to understand code to verify it. But the balance has shifted. Being good at syntax is now table stakes. What companies need are engineers who understand systems well enough to verify, debug, and improve AI-generated outputs.
The bottom of the funnel has become the top of the value chain.