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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesA 2024 Australian government proof of concept found that Llama2-70B summaries scored below employee-written summaries on one specific task: reviewing submissions to a parliamentary inquiry for information relevant to the regulator. Human summaries scored 61 of 75 points (81%); AI summaries scored 35 of 75 (47%). The result is a warning about this system’s performance on evidence-sensitive summarization—not proof that AI generally underperforms employees.
What ASIC tested
The Australian Securities and Investments Commission (ASIC), working with Amazon Web Services (AWS) Professional Services, ran the proof of concept from January 15 to February 16, 2024. It tested Llama2-70B on public submissions to a parliamentary inquiry about ethics and professional accountability challenges in the audit, assurance and consultancy industry. The goal was to identify and summarize information relevant to ASIC, including references and page numbers.
ASIC employees also prepared summaries. Five evaluators read the source documents and assessed the outputs; Futurism reports that the summaries were labeled A and B for blind assessment. This was an experiment, not an AI system deployed in ASIC’s regulatory work. The reported comparison applies to this model, task, prompt setup and period. Futurism’s account of the test
How the scores compare
| Summary type | Aggregate score | What the figure means |
|---|---|---|
| Employee-written | 61 of 75 points (81%) | Aggregate score on the proof of concept’s assessment rubric. |
| AI-generated (Llama2-70B) | 35 of 75 points (47%) | Aggregate score on the same rubric. |
The percentages are scores against the trial’s rubric. They are not the share of tasks completed correctly, a measure of employee productivity, or a general benchmark of AI against workers. The reproduced ASIC answer says the AI summaries scored lower on every assessment criterion. Going Concern’s reproduction of the ASIC answer
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Where the AI summaries fell short
In the reproduced answer, ASIC identified limited ability to capture the nuance and context needed to analyze submissions. It also said outputs could create extra work when staff had to fact-check them or when the original material conveyed information better. These concerns matter for a summarization task where an omitted qualification or misread passage can change what a reviewer takes from a submission.
Futurism additionally reported that the summaries did not provide requested page numbers and could be wordy, irrelevant or redundant. It also reported that three of the five assessors later said they suspected which outputs were AI-generated. Those details come from the news account; they should not be mistaken for separate aggregate findings in the reproduced ASIC answer. Futurism’s account of the test
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What the result does—and does not—show
The result supports a narrow conclusion: in this short 2024 proof of concept, Llama2-70B performed worse than employee-written summaries on ASIC’s rubric for this particular task. It does not establish that AI systems as a class perform worse than employees, that later models would receive the same scores, or that AI is inferior across workplace tasks. ASIC’s reproduced answer explicitly cautioned that the test covered one model at one point in time and one use case; it also noted the limited time available to optimize the system.
The practical issue is not only whether an AI can produce a readable summary, but whether it preserves the evidence a reviewer needs and saves work after verification. In this trial, the reported weaknesses and fact-checking burden limited the value of automating the task. That is a useful caution for organizations considering AI-assisted analysis of complex documents, not a verdict on every possible implementation.
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What ASIC said could improve results
ASIC’s reproduced observations said generic prompts produced lower-quality output than specific or targeted instructions. They also emphasized experimentation and iteration, monitoring outcomes, and active feedback between data scientists and subject-matter experts. The answer expressed an expectation that AI capabilities would improve; that was ASIC’s outlook at the time, not evidence of how current systems would perform on the same task.
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