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In a 2026 account of work on his German marketplace MachDuDas, founder Samir Alibabic estimates that one person directing AI coding agents produced roughly 2.4 times the runtime-code output per active human contributor of the project’s historical baseline—or about 3.2 times when a large 2016 repository-import period is excluded. He estimates the workflow’s overall engineering capacity at roughly that of a conventional four-to-seven-person team. Those are self-reported figures from one product, not independently verified measurements or a general forecast for software teams.
What did Alibabic measure?
Alibabic’s question was: “How much engineering capacity have AI agents actually given me?” Rather than compare MachDuDas with an unrelated company or rely only on generic lines-of-code estimates, he says he compared development on the marketplace with its own earlier repository history. In the current workflow, one human directs AI coding agents.
That same-product comparison is useful because it holds the product relatively constant. It does not make the periods directly equivalent: project scope, contributor mix, types of work, and repository practices can change over time. The author’s calculations and repository extraction methods have not been independently audited.
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What are the reported figures?
The figures below are Alibabic’s estimates from his 2026 account, not independently validated repository or industry statistics.
| Measure | Alibabic’s reported figure | What it describes |
|---|---|---|
| Runtime-code output per active human contributor | Roughly 2.4×; roughly 3.2× when a large 2016 repository-import period is excluded | His normalized comparison of MachDuDas development periods. The result changes with the selected historical window. |
| Estimated engineering capacity | Roughly equivalent to a conventional four-to-seven-person team | His broad estimate for one person directing agents on this product—not a count of jobs displaced. |
| Runtime-code additions, March–September 2026 | Approximately 81,000 | Reported additions across the period; the account’s extraction definitions were not independently examined. |
| Test additions, March–September 2026 | Approximately 105,000 | Reported test growth over the same period, illustrating work omitted by runtime-code-only comparisons. |
| Median commit-to-production lead time | 6.8 days across the broad reconstructed set; roughly 11 hours for deployments from mid-September 2026 onward | The author says earlier accumulated large branches affected the broad result, so the later figure is not a like-for-like replacement for it. |
| Deployment-related change failure rate | Roughly 4%–7% | The range depends on whether the author includes one likely incident. |
| Recovery from deployment-caused failures | Under one day | The author’s observed recovery estimate for the cases he classified as deployment-caused. |
Why do code and test counts tell different stories?
Code volume can show that more implementation work is being produced, but it is not a direct measure of useful software shipped. Lines added do not indicate whether code is correct, maintainable, valuable to users, or required by a changing product. Alibabic himself notes that “Lines of code are a weak proxy for productivity.”
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The test figures make the measurement problem concrete. Across March–September 2026, he reports about 81,000 runtime-code additions alongside about 105,000 test additions. In his examples, three human contributors added about 22,900 lines of runtime application code and removed about 5,700 in July 2016. In June 2026, one human directing agents saw around 30,800 runtime-code additions. In August 2026, runtime-code additions were about 12,200 while test additions exceeded 72,000. All of these are figures reported by Alibabic; the account’s precise definitions and extraction scripts were not independently examined.
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Did higher coding output mean faster delivery?
Not automatically. Alibabic reports a median commit-to-production lead time of 6.8 days across a broad reconstructed set, compared with roughly 11 hours for deployments from mid-September 2026 onward. He attributes part of the difference to earlier large branches clearing. That explanation matters: implementation speed and the time a change takes to reach production are different measures, and a backlog of unshipped work can make delivery look slower even when coding is faster.
He also estimates a deployment-related change failure rate of roughly 4%–7%, depending on whether a likely incident is counted, and says observed recovery from deployment-caused failures was under one day. These figures reflect his own incident classifications and estimates. They are not a standardized external assessment, and the range should not be read as a precise, settled rate.
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What can this case study—and not this case study—show?
The strongest conclusion is bounded: Alibabic reports that, on MachDuDas, his one-person-plus-agents workflow increased runtime-code output per active human contributor and supported a much larger estimate of engineering capacity than one person alone would imply. His account also shows why code volume should be read alongside test work, production lead time, and failure recovery.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- It can illustrate: one operator’s measured attempt to compare AI-assisted development with the earlier history of the same product.
- It cannot establish: a typical multiplier for other teams, products, or codebases; the causal effect of AI agents in a controlled comparison; or that a fixed number of engineers can generally be replaced.
- It does not support tool-specific conclusions: the account does not name the agent vendors or versions, so the results cannot be attributed to a particular product.
- It does not settle team equivalence: the four-to-seven-person figure is the author’s engineering-capacity estimate, not a measured headcount substitution.
For readers assessing other productivity claims, the useful questions are whether the baseline is the same product or an unrelated benchmark; whether the count includes runtime code, tests, and supporting work; how contributor time is normalized; whether commit-to-production time and deployment frequency are included; how failures and recovery are classified; and whether the figures are self-reported or independently validated.
Where do the numbers come from?
The primary source is Samir Alibabic’s account of AI and development velocity on MachDuDas, updated September 24, 2026. The figures in this article are attributed to that account and should be understood as the author’s report, not as an independent study.
Alibabic’s interpretation is that “The scarce resource is increasingly: judgment.” That is his perspective on the workflow, rather than a measured statistic.
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