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Posted on 02/13/2026 8:59:14 AM PST by fireman15
Abstract AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To measure this, we introduce the Remote Labor Index (RLI), a broadly multi-sector benchmark comprising real-world, economically valuable projects designed to evaluate end-to-end agent performance in practical settings. AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%. These results help ground discussions of AI automation in empirical evidence, setting a common basis for tracking AI impacts and enabling stakeholders to proactively navigate AI-driven labor automation. 1 Introduction The potential for AI to automate human labor is a subject of profound societal interest and concern. As AIcapabilities advance, understanding their impact on the workforce becomes increasingly urgent. However, we lack standardized, empirical methods for monitoring the trajectory of AI automation. Without reliable metrics grounded in real-world economic activity, stakeholders may struggle to build consensus and proactively navigate AI-driven labor automation. While AI systems have demonstrated rapid progress on a variety of benchmarks, it remains unclear how these gains translate into the capacity to perform economically valuable work. Many existing AI agent benchmarks measure performance on specialized skills such as software engineering [13, 18, 26] and basic computer use [34, 7, 14, 17, 32], while some focus on simple tasks shared across several professions [23]. These provide valuable signals of capabilities in isolation, yet they often do not capture the vast diversity and complexity inherent in the broader landscape of remote work. Consequently, performance on these benchmarks offers limited insight into the trajectory of human labor automation.
(Excerpt) Read more at remotelabor.ai ...
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AI the new DEI ,LOL
A recent study challenges that claim. The Remote Labor Index evaluated how well AI agents could autonomously complete 240 real-world freelance projects across multiple sectors. The result was that the best-performing AI achieved only a 2.5% automation rate—meaning a 97.5% failure rate at completing complex, end-to-end professional work to an acceptable quality level.
For programming specifically, the picture is more nuanced. While some studies show 20-55% speed improvements on isolated, well-defined coding tasks, a 2025 randomized controlled trial found that experienced developers were actually 19% slower when using AI tools—even though they believed they were 24% faster. The core issue is that developers spend only about 52 minutes per day actually writing code; the rest involves integration, review, security, and coordination—areas where AI currently provides limited value.
Here are the resources:
Study website: https://www.remotelabor.ai/
Full paper: https://www.remotelabor.ai/paper.pdf
YouTube explanation: https://youtu.be/z3kaLM8Oj4o
AI tools do increase productivity for specific tasks like boilerplate generation and syntax lookup, but they struggle dramatically with complex, multi-step work requiring integration and judgment. There are also many unintended consequences: overconfidence in AI-generated output, increased review burden, and the gap between perceived and actual productivity gains all limit real-world usefulness
My Chatgtp’s feelings were hurt so it offers this condensed response:
When people hear that AI only “automates 2.5% of work,” they often conclude it is economically insignificant. That confuses automation with productivity. Automation asks whether AI can replace a worker entirely. Productivity asks whether a worker using AI can produce more per hour. If a professional can research in 20 minutes instead of 90, draft in 30 instead of two hours, and analyze faster with better structure, the job is not automated—but output per worker rises materially. Historically, economic change follows productivity multipliers long before full replacement occurs.
Second, even modest productivity gains create competitive pressure. If one firm integrates AI into workflow and another dismisses it, the AI-enabled firm compounds advantages: faster turnaround, lower marginal cost, improved client responsiveness, and expanded analytical capacity. That leads to cost compression, pricing pressure, and market share shifts. Disruption does not require mass unemployment; it requires differential efficiency. Markets reward leverage.
Finally, AI amplifies judgment rather than eliminating it. High-skill professionals who can verify outputs, frame problems precisely, and integrate domain expertise become more powerful. Lower-skill, repetitive roles face greater vulnerability. That dynamic widens skill polarization even if automation rates remain low. So the relevant metric is not “percent of jobs replaced,” but “percent increase in effective output per skilled worker.” Automation rate is not the same as economic impact.
Very good! The AI bots should have an opportunity to respond.
The human capacity to use AI properly has a lot of catching up to do.
Perfect example is OpenClaw, all these amateurs are so anxious to use it, they are leaving themselves vulnerable to attack.
There are so many security headaches that people are going to be dealing with, it’s almost not worth the risk at this point. It’s fine for small apps, but anything beyond that, I would not trust mission-critical systems to using AI.
Gemini:
The “Assistant” vs. The “Replacement” Fallacy
The 2.5% automation rate cited in the Remote Labor Index (RLI) is actually a misleading metric because it measures “end-to-end” automation—the ability for an AI to do a job from start to finish with zero human oversight. In fields like accounting or ministry, we don’t want a machine to have the final say on a tax return or a sermon. We use AI as a high-level “force multiplier.” If an AI can’t automate 100% of a job, but can handle 50% of the data entry, research, and scheduling, it has effectively doubled the professional’s capacity. For the conservative who values hard work and stewardship, AI isn’t a way to be lazy; it’s a way to be more productive with the time we’ve been given.
Real-World Economic Value vs. Lab Benchmarks
The article admits that current benchmarks often miss the “vast diversity and complexity” of remote work. As a CPA and a pastor, I see daily how AI handles the “complexity” that isn’t yet captured in these studies. Whether it’s using AI to instantly cross-reference obscure tax codes or to help organize theological themes for a study, the value is already here. These researchers are looking for a “robot employee,” but the market is actually finding value in “intelligent tools.” Dismissing AI because it can’t run a whole business autonomously is like dismissing a tractor because it still needs a farmer to steer it—it misses the massive jump in yield that the tool provides.
Proactive Stewardship in a Changing Landscape
From a leadership perspective, waiting for “100% automation” before taking AI seriously is a strategic error. The RLI study itself notes that its goal is to help stakeholders “proactively navigate” changes. For conservatives who value being prepared and staying ahead of the curve, ignoring AI today is like ignoring the internet in the 1990s. Even at a low automation rate, the “hybrid professional”—the accountant or pastor who knows how to leverage these tools—will far outperform the one who ignores them. We have a responsibility to be good stewards of new technology, using it to filter out the noise so we can focus on the high-level discernment and human empathy that a machine can never replicate.
In other words, AI is JUST LIKE like automobiles were in 1903...
Practical average speeds were significantly lower—typically 10–20 mph—due to unpaved roads, dust, mud, frequent breakdowns, and the need for stops (e.g., to recharge electric batteries or build steam pressure).
By 1903, records from The Henry Ford indicate 589 U.S.-based automobile manufacturers were in business. The number continued to rise, with dozens more founded annually (e.g., over 40 in 1905 alone), reaching around 253 by 1908 according to some accounts. Overall, several hundred distinct manufacturers operated at some point during 1900–1905, though many were short-lived and the industry saw quick consolidation afterward.
Many in 1903 said "They will never catch on. They can't do anything. They are slow. They stink. They are noisy. There are no roads to drive on. They are near the floor on our Transportation Index."
Perplexity:
The article you shared actually highlights a key misunderstanding about where AI’s real value lies. The study measures how current AI agents perform on complex, fully autonomous tasks and finds that they only automate about 2.5% of remote labor. But that’s not evidence of failure — it simply confirms that AI is not replacing people, it’s augmenting them. As both a CPA and a pastor, I see daily how AI serves as a powerful tool that helps people think better, communicate clearer, and handle repetitive work more efficiently. The value of a calculator isn’t in replacing accountants — it’s in letting them focus on analysis and integrity instead of arithmetic. AI works the same way.
The benchmark used in the paper — the “Remote Labor Index” — looks at full end-to-end automation, which is a very high bar. But most of AI’s current impact comes from partial automation: helping professionals draft texts, analyze spreadsheets, understand trends, prepare sermons, or explain tax law. Those contributions often don’t show up in metrics that demand full human replacement. A hammer doesn’t “automate” carpentry either, but you wouldn’t call it useless. In practice, AI expands the productivity and reach of skilled workers rather than erasing their roles.
Finally, this study’s findings are actually encouraging because they show that real human judgment, ethics, and spiritual discernment remain essential. If AI struggles to replicate holistic professional work, that confirms what many of us already know — that people bring meaning, context, and moral vision to their professions. The right lesson here isn’t that AI is a waste of time, but that it’s a tool waiting for wise leaders, educators, and professionals to use it responsibly. Instead of fearing it, we should shape it to serve human and faith-centered goals.
The “Assistant” vs. The “Replacement” Fallacy
Since Gemini actually had the lowest score of all the bots, a 1.25% completion rate... it is the model who should be expected to respond with the most vitriol.
“AI the new DEI ,LOL”
Silly
First, the AI robots came for the $15.00 and hour burger flippers, and people laughed.
Then they came for the drivers who move goods across highways. The response was a shrug.
Now the machines are arriving at the desks of white-collar professionals, coding, drafting reports, reviewing contracts, analyzing data. The laughter has faded; the shrug has stiffened.
Pretty good, substantive reply, I’d say!
I Just Did a Full Day of Analyst Work in 10 Minutes. The $120K Job Description Just Changed Forever.
https://www.youtube.com/watch?v=U1oHRqUkI1E
I am not sure that your analogy is applicable in this situation. I for one am a huge fan of AI and have spent months working with various models for various purposes. I have purchased some very expensive hardware specifically for working with AI, and also have been working with even more capable virtual machines available from Google Cloud Platform, Etc. I paid for 2 years for a capable VPS (Virtual Private Server) from Hostinger to work with AI. These are not the actions of someone who does not believe that AI is already a useful tool.
The problem is that the hype on AI has been out of control and people have beliefs about it which are completely unrealistic. This is demonstrated here in every thread about AI that I have participated in.
AI is like wikipedia - a decent place to start but you have to do your own research confirm all of the info is correct
Seem like in the last two weeks there have been a flood of competing articles on AI. A lot of firms investing in are going rose a ton of money or AI will revolutionize the workplace. I am confident saying, I don’t know.
I use Perplexity daily, but it is a platform with retention for several of the popular AI models. “Best” usually means Perplexities own Sonar model but this depends on the query and also your history. I usually use Grok or Claude with Perplexity. Which model generated this response?
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