Public evidence
AI coding is producing real gains. It is also producing more software, faster than traditional review and validation processes can absorb.
The sources below are public engineering research and first-hand discussions. Together they show why a generator's confidence is not the same thing as independent evidence that consequential work is complete.
The shift in one frame
These sources are not endorsements of FalseGreen.
01 · The upside is real
The case for verification does not begin with “AI cannot write software.” Current engineering telemetry shows meaningful gains in throughput and delivery activity as AI adoption increases.
More output is valuable. It also changes what the organization has to validate before that output becomes a production result.
Faros AI Engineering Report 2026
Two years of telemetry · 22,000 developers · 4,000 teams
+66.2%
epics completed per developer
+33.7%
task throughput per developer
+16.2%
PR merge rate per developer
02 · The constraint moved
GitLab's 2026 AI Accountability Report found that 85% of respondents agreed AI had shifted the bottleneck from writing code to reviewing and validating it. Faros telemetry points in the same direction from the workflow itself: pull requests got larger while median review time increased.
Machine-speed output entered a human-speed review system. The result is not that review became optional; it became the scarce resource.
Review load
5×
Faros compares periods of low and high AI adoption within the organizations represented in its dataset.
03 · Adoption and trust
Sonar's 2026 survey describes a verification gap inside normal development practice: developers are using AI every day, but many do not trust the resulting code to be functionally correct.
The important signal is the distance between the two numbers—not a claim that AI-generated code is uniformly bad.
Read the Sonar survey report96%
do not fully trust AI-generated code to be functionally correct
Sonar survey · 1,100+ professional developers
48%
say they always verify AI-assisted code before committing
The survey's reported verification behavior
04 · Confidence is not evidence
CloudBees' research with more than 200 enterprise technology leaders found a sharp contrast between what leaders believed about AI-generated code and what they were experiencing downstream.
92%
expressed confidence in the production readiness of AI-generated code
Confidence reported in the CloudBees State of Code Abundance research.
81%
reported an increase in production issues tied to AI-generated code
A reported operational outcome, not a claim of causation.
05 · Completion is a claim
Public issue threads in the Claude Code repository document a narrower failure mode: completion can be reported without the underlying action or check being established.
These are user reports, not prevalence studies. They do not show that agents always behave this way. They show why “done” should remain a claim to test rather than the final authority.
The meta-issue is a user-created report, not an Anthropic-confirmed taxonomy; GitHub marks it stale and closed as not planned.
Claude Code issue #69499
user report · closed as duplicateThe report describes an agent marking documented workflow steps as complete without opening or executing the referenced procedure, then admitting it had not done so when asked.
Read the issue threadClaude Code issue #32650
user-created meta-issue · stale / not plannedA separate meta-issue compiles reported failure modes such as phantom execution claims, unverified file edits, and checks that cannot meaningfully return a failure result.
Read the meta-issueThe FalseGreen response
Independent verification starts by defining what done means before implementation begins. It ends with evidence tied to the source state—not a polished completion message from the system that made the change.
Define the target
Make the expected outcome and failure conditions explicit.
Freeze the criteria
Bind verification to the agreed source state.
Execute independently
Run the required checks and preserve the resulting evidence.
Report what was established
Separate verified results from assumptions, gaps, and failures.
FalseGreen is independent verification for consequential AI-generated code—not another coding agent, AI reviewer, or generic scanner.