Labor
10 items across 5 editions · appeared in the last 5 editions in a row. First seen Fri 11 Sep, last seen Tue 15 Sep.
- The News International, reporting on 15 September, quotes Bilal Chughtai's post on X: "I recently resigned from Google DeepMind, where I worked on AGI safety and alignment research. I earnestly believe that AI has the potential to kill us all, and that we might be running out of time to avoid this outcome."
- The report says Chughtai worked as a research engineer on AGI safety and alignment research and left DeepMind in July 2026, posting the warning this week.
- The article carries no response from Google or Google DeepMind. The post is a personal statement: it is not accompanied by evaluation data, internal documents or any specific capability claim.
- 404 Media reported on 14 September that "OpenAI is hiring hundreds of contractors who read a massive stream of real users' ChatGPT prompts, with the prompts sometimes including sensitive personal information", and that what reviewers see "can include whole conversations between users and the chatbot, conversations that most of ChatGPT's more than 900 million users probably don't realize may be read by actual people".
- 404 Media reports the reviewers rate and critique the chatbot's replies, and that internal documents it saw show contractors training ChatGPT "to not anthropomorphize itself, and to be less sycophantic".
- On privacy, 404 Media reports the contractors do not see ChatGPT usernames and that OpenAI says it tries to remove personal information before prompts reach reviewers, but "the company acknowledged sensitive details can still get through". Anthropic confirmed to 404 Media that it also uses human review to improve its models.
- 404 Media quotes someone who works with the prompts, asked whether users know humans read their chats: "No. I don't think they would imagine some contractor somewhere [...] is analyzing the conversations." The report is 404 Media's alone and OpenAI has published no response.
- Epoch AI, publishing on 14 September, reports that the share of US adults who used AI on 6–7 days in the previous week rose from 8.2% in March 2026 (90% CI 7.2–9.3) to 18.7% in August 2026 (90% CI 16.7–20.8). Any weekly AI use rose from 50.0% to 56.4%, and the share using AI on just one day fell from 17.3% to 10.1%.
- The figures come from two Epoch AI/Ipsos surveys of US adults on Ipsos' KnowledgePanel, fielded 3–5 March 2026 (n=2,017, 1,028 AI users) and 28–30 August 2026 (n=1,016, 574 AI users), weighted to represent US adults aged 18 and over.
- Epoch flags its own comparability problem: "The days of use question changed between waves, in two ways." March asked one overall question with three bands; August asked per-service day counts from 1 to 7 and Epoch took the maximum across services, which it says "may understate frequency relative to an overall-use question". Epoch concludes "comparisons of frequency across the two waves are approximate".
- The two waves are separate samples rather than the same people tracked over time, so the result is a population-level shift, not a measurement of individuals changing their behaviour.
- The Register, publishing on 14 September at 21:46 UTC, reports that employees discovered the latest round of Oracle layoffs on Monday when they lost access to corporate systems such as email and Slack, because notifications were not consistently sent to personal addresses. Those who reached their accounts saw: "After careful consideration of Oracle's current business needs, we have made the decision to eliminate your role as part of a broader organizational change."
- The Register reports severance of four weeks of pay plus one additional week per year of employment, and says some of those cut had more than 20 years' tenure. Oracle declined to comment on the layoffs.
- The cuts follow a filing in which Oracle added roughly $700 million to its 2026 restructuring plan, taking total estimated restructuring costs to about $2.8 billion. The Register reports first-quarter fiscal 2027 revenue of $19.3 billion and net income of $4.76 billion, with about 30% year-over-year growth.
- Oracle has not said how many people were cut in this round, or which teams and regions were affected, so the scale is not established.
- "Informational Help-Seeking on Reddit Did Not Decline After ChatGPT" (arXiv 2609.12447, submitted 11 September, announced 14 September) by Hazem Ibrahim and Yasir Zaki tracks monthly post counts in 26 Reddit informational communities against 90 size-comparable hobby communities over the same six calendar months before and after ChatGPT's launch, and repeats the analysis at 66 earlier dates as a placebo.
- The abstract states: "Our results rule out any decline in posting larger than 3.4%, far smaller than the 8% to 25% declines documented in prior work."
- The authors scored 274,411 posts and 223,775 comments with AI-text detectors and report that "AI-written posts rose only 2-3 percentage points more in informational communities than in hobby communities, short of the 5.1 points that would be needed to hide even the smallest decline previously reported for Reddit". They attribute earlier findings to community types already drifting apart before ChatGPT existed.
- Preprint, not peer reviewed. The study covers one platform over a six-month window around launch and relies on AI-text detectors whose false positives it cancels statistically rather than validates directly. The authors report their own largest estimate — an 18% fall in posts to low-stakes curiosity communities — matches that group's pre-existing trend.
- The Decoder, publishing on 13 September, reports a randomised controlled trial by Thibault Schrepel of Vrije Universiteit Amsterdam comparing three groups — no ChatGPT access, unguided AI suggestions, and structured prompt-engineering training — with 66 students in 2024 and 164 in 2025.
- The group without AI scored lowest on both the in-class assignment and the take-home exam in both years. Schrepel, who had expected unguided use to hurt performance, is quoted saying: "I was wrong."
- The trained group scored well above the other two in 2024, but by 2025 that advantage had almost closed and all three groups performed at roughly the same level. The Decoder reports Schrepel attributes this to growing familiarity with chatbots.
- Limitations stated in the article: a small sample, students already enrolled in an AI course and so likely more tech-savvy than average, and no way to verify how much AI students actually used during take-home exams. The underlying paper is on SSRN and was not accessible for this edition, so these figures come from The Decoder's report.
- DLA Chief Information Officer Adarryl Roberts said: "We have approximately 185 to 190 bots that are running. I'd say about 90% to 95% of those are unattended bots." In 2025 the bots saved the agency an estimated 300,000 hours of work.
- Roberts spoke at DLA's Industry Collider Day on 9 September in Alexandria, Virginia. The agency employs roughly 25,000 military and civilian personnel.
- DLA staff reach military-specific versions of ChatGPT and Grok, along with Gemini, through the genai.mil platform for sensitive-but-unclassified information, and the agency runs "GenAI 101 training" before moving staff toward agentic systems.
- The 300,000-hour figure is the agency's own estimate and the reports do not say how it was calculated or what it is measured against.
- OpenAI published the post on 11 September at 16:00 GMT. Its own description reads: "GPT-6 Astra improves Devin's ability to test software and show that it works, with the goal of helping engineers review less code and ship more."
- The claim is about the weakest link in coding agents — not writing code but demonstrating it works — and OpenAI frames the benefit as reviewers reading less code rather than more throughput.
- The article page blocks our fetcher, so the wording above is taken verbatim from OpenAI's own RSS feed. No benchmark, defect-rate or review-time figures are given in that description.
- This is a vendor post about a customer deployment, with no independent measurement of the effect on code review or defect rates.
- TechCrunch reported on 11 September at 3:58 pm PDT that Mecka AI is nearing a Sequoia Capital-led round at a valuation of about $500 million; the round size is not disclosed and "terms of the deal are not final and could still change".
- That follows a $60 million Series A announced three months earlier, led by Framework Ventures with Menlo Ventures, SV Angel and Kindred Ventures.
- Mecka pays people to record themselves performing everyday tasks using body sensors and smartphones, and sells that motion data to train humanoid robots. It was founded in 2024 by Josh Gao, Mogen Cheng, Jason Chong and Duy Nguyen, and as of early June was projecting an annual run rate of $100 million by the end of 2026.
- TechCrunch names competitor XDOF as nearing a $1.2 billion valuation. The valuation and run-rate figures come from sources and company projections rather than filings.
- Announced 10 September: Skild AI's S1 learns new manipulation tasks from a single video demonstration using in-context learning, with no weight updates or task-specific retraining. NVIDIA reports roughly 66% per-step success on multistep tasks against about 9% for comparable systems, and that one video example is worth roughly 380 hands-on training examples — 50 to 100 hours of manual collection.
- S1 executes unfamiliar tasks up to 10 minutes long across dozens of steps, including potting plants, making pancakes, pour-over coffee and kit assembly. In one plant-potting test, the gap from recording the demonstration to autonomous execution on hardware was 11 minutes.
- Skild reports a $100 million annual revenue run rate ten months after launch and more than 60 deployment partnerships across manufacturing, logistics, inspection, security and food preparation. If the demonstration-efficiency claim holds outside curated tasks, the cost of teaching a robot a new job falls by orders of magnitude — which is the labour-substitution variable to watch.
- These are vendor figures published on a supplier's blog, not an independent benchmark. "Per-step" success is not end-to-end task success, and the 66% versus 9% comparison does not name the baseline systems.