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Imagine this scenario, one that's increasingly common: You have a voice AI listen to your meeting at work, you get a summary and analysis of that meeting, and you assume AI did all the work.
In reality, though, none of these tools work alone. PLAUD AI, Rabbit, ChatGPT, and more all rely on a layer of human labor that most of us don't hear about. Behind that clean chat interface on your phone or computer, there are data labelers that tag speech samples, contractors that rate AI answers, and testers feeding the system more examples to learn from. Some are highly trained while others focus on more of the tedious aspects of the work. No matter what, though, your AI isn't just automated - it's a complex blend of code and human effort. Without it, your AI wouldn't work at all.
The Invisible Workforce Behind Everyday AI
AI tools don't just appear out of thin air, of course. They learn similarly to the way we do: by example. That learning process often relies on what's called human-in-the-loop (HITL) training.
As data-annotation company Encord says in a blog post:
"In machine learning and computer vision training, Human-in-the-Loop (HITL) is a concept whereby humans play an interactive and iterative role in a model's development. To create and deploy most machine learning models, humans are needed to curate and annotate the data before it is fed back to the AI. The interaction is key for the model to learn and function successfully," the company wrote.
Annotators, data scientists, and data operations teams play a significant role in collecting, supplying, and annotating the necessary data, the post continued. The amount of human input varies with how involved the data is and how much human interaction it will be expected to offer.
Of course, as with many business activities, there are ethical concerns. Many content moderators complain of low pay and traumatic content to review. There can also be a language bias in AI training, something researchers and companies are likely working on to solve as AI becomes more complex and global.
Case Study: PLAUD AI
Various ways users wear the PLAUD Note device—on a wristband, clipped to a lapel, or hanging as a pendant—highlighting its flexibility for hands-free voice capture throughout the day.PLAUD AI
PLAUD AI's voice assistant offers an easy, one-button experience. Just press a button, speak, and then let it handle the rest. As the company said on its website, the voice assistant lets you "turn voices and conversations into actionable insights."
Behind the scenes, this "magic" started with pre-trained automatic speech recognition (ASR) models like Whisper or other custom variants, that have been refined with actual user recordings. The models not only have to transcribe words, but also try to understand the structure, detect speakers, and interpret tone of voice. The training involves hours and hours of labeled audio and feedback from real conversations. It's likely that every time you see an improvement in the output, it's thanks to thousands of micro-adjustments based on user corrections or behind-the-scenes testing.
According to reviewers, PLAUD AI leverages OpenAI's Whisper speech-to-text model running on its own servers. There are likely many people managing the PLAUD AI version of the model for its products, too. Every neat paragraph that comes out of the voice assistant likely reflects countless iterations of fine-tuning and A/B testing by prompt engineers and quality reviewers. That's how you get your results without having to deal with all that back-end work yourself.
Case Study 2: ChatGPT and Otter.ai
The ChatGPT logo represents one of the most widely used AI assistants—powered not just by models, but by human trainers, raters, and user feedback.ilgmyzin/Unsplash
When you use ChatGPT, it can feel like an all-knowing helpful assistant with a polished tone and helpful answers. Those are based, of course, on a foundation of human work. OpenAI used reinforcement learning from human feedback, or RLHF, to train its models. That means actual humans rating responses so the system could learn what responses were the most helpful or accurate, not to mention the most polite.
"On prompts submitted by our customers to the API, our labelers provide demonstrations of the desired model behavior and rank several outputs from our models," wrote the company in a blog post. "We then use(d) this data to fine-tune GPT‑3."
Otter.ai, a popular online voice transcription service, also relies on human work to improve its output. It doesn't use RLHF like OpenAI does, but it does include feedback tools for users to note inaccurate transcriptions, which the company then uses to fine-tune its own models.
The company also uses synthetic data (generated pairs of audio and text) to help train its models, but without user corrections, these synthetic transcripts can struggle with accents, cross talk, or industry jargon; things only humans can fix.
Case Study 3: Rabbit R1's Big Promise Still Needs Human Help
The Rabbit R1 made a splash with its debut: a palm-sized orange gadget promising to run your apps for you, no screen-tapping required. Just talk to it, and it's supposed to handle things like ordering takeout or cueing up a playlist. At least, that's the idea.
Rabbit says it built the device around something called a Large Action Model (LAM), which is supposed to "learn" how apps work by watching people use them. What that means in practice is that humans record themselves doing things like opening apps, clicking through menus, or completing tasks and those recordings become training data. The R1 didn't figure all this out on its own; it was shown how to do it, over and over.
Since launch, people testing the R1 have noticed that it doesn't always feel as fluid or "intelligent" as expected. Some features seem more like pre-programmed flows than adaptive tools. In short, it's not magic—it's a system that still leans on human-made examples, feedback, and fixes to keep improving.
That's the pattern with almost every AI assistant right now: what feels effortless in the moment is usually the result of hours of grunt work—labeling, testing, and tuning—done by people you'll never see.
AI Still Relies On Human Labor
For all the talk of artificial intelligence replacing human jobs, the truth is that AI still leans hard on human labor to work at all. From data labelers and prompt raters to everyday users correcting transcripts, real people are constantly training, guiding, and cleaning up after the machines. The smartest AI you use today is only as good as the humans behind it. For now, that's the part no algorithm can automate away.
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