Akka’s September 2026 experiment did not fully rewrite 65 open-source projects. It explored all 65 through specifications and partial implementations, then selected 10 for complete implementation. Akka reported that 57 of the initial 65 ports improved either lines of code or performance, but that company-reported composite result is not proof of broad, autonomous production readiness.
What did Akka test across 65 open-source projects?
In a report dated September 3, 2026, Akka described two stages of work using its software development tools. The first covered 65 open-source projects: the team performed discovery and generated specifications and implementations for up to 10% of each project’s surface area. Akka deliberately included projects that were poor candidates for a port as well as projects that appeared more suitable.
In a second stage, Akka selected 10 projects for complete implementation, choosing those where it saw potential impact and measurable baselines. The 65-project figure therefore describes a broad discovery and partial-porting tranche, not 65 complete, equivalent rewrites. Akka’s report describes the scope and selection.
How did the spec-driven workflow work?
Akka described an iterative cycle of setup, discovery, porting, benchmarking, and improvement. The aim was to make the specification, tests, and evaluation conditions part of delivery—not to treat generated code as the finished product.
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- Set up the project. Prepare the source project and its environment for analysis and execution.
- Discover behavior. Analyze source code, domain models, schemas, and runtime behavior to produce specifications.
- Plan and implement the port. Akka Specify handled planning, tasking, implementation, builds, tests, and review.
- Benchmark. A common runner compared test-suite execution, code size, and end-user latency.
- Improve and audit. Use failures and specification gaps to inform further iterations, with auditors checking whether the work met its exit conditions.
Akka says its failures tended to occur when specifications left decisions implicit or auditors failed to catch a class of error. It interprets explicit enumeration and stringent exit conditions as important to successful ports. That is Akka’s reading of its own experiment, not a separately established causal result.
Did AI really port all 65 projects?
No—not as complete implementations. Akka reports that its initial 65-project tranche took 99.3 hours in total and that 57 of 65 ports improved in lines of code or performance. The “or” matters: the figure does not say that every port improved both measures, and it does not turn the initial partial implementations into full rewrites. Akka’s report gives these figures. Read the report.
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InfoQ’s October 5, 2026 summary says the initial tranche used 9.41 billion tokens. That token total is reported by InfoQ as a summary of Akka’s work, rather than as an independently reproduced measurement. InfoQ’s coverage.
What did the experiment find about models, tokens, and code size?
InfoQ’s account of Akka’s findings reports the following model trade-offs. These are attributed to that secondary report; the available evidence does not establish that another team reproduced the measurements.
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| Reported comparison | What the source says |
|---|---|
| Average time per port | Sonnet: 61 minutes; Opus: 120 minutes, as reported by InfoQ on October 5, 2026. |
| Token consumption | Opus used about 40% fewer tokens than Sonnet in the comparison summarized by InfoQ on October 5, 2026. |
| Higher effort settings | InfoQ says they increased consumption without consistently improving efficiency. |
| Code size and performance | Akka says 57 of the initial 65 ports improved in lines of code or performance; it does not claim that all 57 improved both metrics. |
These figures describe different dimensions of the work. Faster average time does not by itself establish better port quality, and lower token consumption does not establish better performance or fewer defects. The sources summarized here do not provide enough detail to turn the model figures into a general rule for choosing a model across other projects.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do the results establish—and what do they not?
Akka’s report concludes: “If there is a single thing to take from 65 ports, it is that the interesting variable in this system is not the model, not the effort, and not the runtime—it is the discipline of the specification and the auditors.” That is Team Akka’s interpretation of its own results.
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The scope and evidence call for a narrower conclusion:
- The first tranche covered partial implementations for most projects; complete implementations were limited to a selected 10.
- The 10-project group was selected for perceived potential impact and measurable baselines, rather than described as a randomized sample of software projects.
- The results come from the vendor whose SDK was used. The cited accounts do not establish independent reproduction or a randomized control design.
- The reported 57-of-65 outcome combines improvement in lines of code or performance; it is not evidence that every project improved on every measure.
The experiment is evidence about Akka’s delivery harness, its selected open-source projects, and the metrics it chose. It does not show that autonomous AI delivery is generally reliable for arbitrary software tasks or that AI can maintain production systems without human oversight.
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