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The Reliability ThesisThesis 1Generation

AI Did Not Remove the Bottleneck It Moved It

Writing code stopped being the constraint. Deciding whether the code is safe to run became one.

Kevin Kissi, Founder and CEO, Zof AI

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Transcript

Edited for readability. The argument is unchanged.

The capacity warning

In April 2026, developers pushed 1.4 billion commits to GitHub in a single month. By August: 2.9 billion.

Then, on August 17th, GitHub went down for nearly eight hours. No bad deploy. No configuration change.

Their CTO wrote: “Our existing operational practices did not keep up.”

GitHub doesn’t say what doubled the volume. I think you can guess.

AI did not remove the bottleneck in software delivery. It’s moved it.

Generation outruns review

For decades, writing the change was the slow part.

Now an agent reads the repository, edits a dozen files, writes the tests, and opens the pull request while you are still in the meeting.

Google says 75 percent of its new code is AI generated, then approved by engineers.

Anthropic says its engineers merge eight times as much code per day as they did in 2024. And in the same essay: “human code review has become a new bottleneck.”

They call it a signature of Amdahl’s law. Speed up one part of a system, and the part you left alone sets your limits.

Every change is a claim

Here’s the part we left alone. Every generated change is a claim.

It claims to match the intent. It claims the tests mean something. It claims nothing downstream will break.

Generation multiplies the claims. It doesn’t settle a single one.

So the queue comes back: in review, in QA, in the incident channel.

One vendor’s telemetry, across 22,000 developers: as AI adoption rose, throughput went up by a third. Time in review tripled.

Justified confidence

Ask an engineering leader what’s hard right now, and nobody says, “We can’t write code fast enough.” They say, “I can’t tell what’s safe to merge.”

That’s the new constraint. Not the production of software. The production of justified confidence.

We spent twenty years optimizing the left side of delivery: write, build, integrate, deploy.

Now the right side has to catch up: understand the impact, verify the outcome, authorize the action, prove what happened.

So on Monday, don’t ask how much code your agents can write. Ask how much authority you can safely hand them. That number is your real velocity.

Generation is getting cheaper. Confidence is becoming the constraint.

That’s the Reliability Thesis.

Sources and research notes

4 sources

Research current through .

  1. The August 17 outage, and the work ahead (opens in a new tab)

    GitHubVlad Fedorov2026-08-20IncidentAccessed 2026-09-29

    Reports a 7 hour 47 minute outage, capacity failures rather than code or configuration changes, and monthly commits rising from 1.4 billion to 2.9 billion since April. It does not attribute that growth to AI.

  2. Cloud Next ‘26: Momentum and innovation at Google scale (opens in a new tab)

    GoogleSundar Pichai2026-04-22ReportingAccessed 2026-09-29

    Google reports that 75 percent of its new code is AI generated and approved by engineers. This is a company-specific disclosure, not an industry-wide estimate.

  3. When AI builds itself (opens in a new tab)

    AnthropicResearchAccessed 2026-09-29

    Reports that the typical engineer merged eight times as much code per day in Q2 2026 as in 2024, and describes human review as a new bottleneck. Code volume is not a measure of delivered value.

  4. What is AI Engineering? (opens in a new tab)

    Faros AINeely DunlapResearchAccessed 2026-09-29

    Summarizes vendor telemetry across 22,000 developers and more than 4,000 teams: task completion per developer rose 34 percent under high AI adoption, median PR review time grew fivefold, and incidents per PR more than tripled. These observational results do not establish a universal causal effect.

The engineering behind this argument

See continuous verification

Verification that runs with the system rather than once at a release boundary.

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