computer science

Invisible Technologies

Invisible Technologies is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Invisible Technologies rather than just read about it. In short: Invisible Technologies is an American artificial intelligence company founded in 2015 that provides AI training data services and enterprise software. The company became known after 2022 for providing human-feedback data used to train OpenAI's ChatGPT, and has since expanded its services to enterprise clients.

Key takeaways

Invisible Technologies belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Invisible Technologies to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Invisible Technologies from memory before moving on to harder problems.

Reference excerpt

Invisible Technologies is an American artificial intelligence company founded in 2015 that provides AI training data services and enterprise software. The company became known after 2022 for providing human-feedback data used to train OpenAI's ChatGPT, and has since expanded its services to enterprise clients.

History Invisible Technologies was founded in 2015 by Francis Pedraza, a Cornell University graduate. The company initially attempted to combine remote workers with software automation to handle business tasks such as scheduling, screening résumés, and updating product data, but early uptake of the service was slow. In 2020, DoorDash contracted Invisible Technologies to digitize restaurant menus and pricing data as the food-delivery sector expanded during the COVID-19 pandemic. In 2021, the company became profitable. In 2022, OpenAI contracted Invisible Technologies for reinforcement learning from human feedback on what would become ChatGPT, with the goal of reducing factual errors known as hallucinations. Hundreds of Invisible contractors, designated "advanced AI data trainers", worked on tasks including improving the model's coding abilities, refining creative writing output, and filtering unwanted content. A typical workflow for an Invisible AI data trainer involved reviewing conversations between a model and its users, identifying messages that were potentially incorrect, illegal, or offensive, and rating model responses on a scale from one to seven across categories such as factual accuracy, grammar, and harassment. Trainers were also asked to draft what they considered an ideal response, which was then routed to OpenAI and to Invisible's internal quality reviewers. By 2024, the AI training industry had shifted toward greater reliance on subject-matter experts for complex training tasks, moving away from lower-cost data labelers. At that time, Invisible Technologies worked with a network of around 5,000 contract trainers in over 100 countries, including holders of doctoral and master's degrees in various fields. Demand for training data used in reasoning models was a major driver of the company's growth, as such models required large amounts of chain-of-thought data produced by human experts. That year, the company reported $134 million in revenue, doubling the previous year's figure. In January 2025, Matthew Fitzpatrick, formerly a senior executive at McKinsey & Company who led QuantumBlack Labs, was appointed chief executive officer. By 2025, the company had expanded into enterprise software for insurance, asset management, healthcare, and other industries.

Funding Through 2024, the company had raised approximately $23 million in primary equity financing from investors including Day One Ventures, Greycroft, and Backed VC. In 2025, the company raised $100 million in a round led by Vanara Capital, an investment firm started by former employees of TPG Inc.. The round valued the company at more than $2 billion.

References

External links Official website

Worked examples

Example 1 — a first encounter with Invisible Technologies

Start with the simplest possible case. Write down what Invisible Technologies claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Invisible Technologies before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Invisible Technologies ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Invisible Technologies

In research: Invisible Technologies appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Invisible Technologies in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Invisible Technologies is common in secondary-school and first-year university syllabi. It links to neighbouring topics American companies established in 2015, Artificial intelligence industry in the United States, Generative AI companies, so understanding it makes those chapters shorter.
In everyday life: Look for Invisible Technologies outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Invisible Technologies in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Invisible Technologies means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Invisible Technologies out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Invisible Technologies in simple terms?

Why does Invisible Technologies matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Invisible Technologies?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Invisible Technologies.