computer science

Reflection AI

Reflection AI 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 Reflection AI rather than just read about it. In short: Reflection AI is an American artificial intelligence company that develops open foundation models and software agents for AI-assisted software development. Founded in 2024 by former Google DeepMind researchers Misha Laskin and Ioannis Antonoglou, the company initially focused on tools that automate software development.

Key takeaways

Reflection AI 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 Reflection AI to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Reflection AI from memory before moving on to harder problems.

Reference excerpt

Reflection AI is an American artificial intelligence company that develops open foundation models and software agents for AI-assisted software development. Founded in 2024 by former Google DeepMind researchers Misha Laskin and Ioannis Antonoglou, the company initially focused on tools that automate software development. The company has positioned itself as an open-source artificial intelligence company and as an open-model alternative to closed frontier AI labs. In 2025, TechCrunch reported that Reflection planned to release model weights for public use while generating revenue from enterprise customers and governments using its models. As of June 2026, Reflection's valuation is $25 billion.

Technology Reflection's work combines large language model training, reinforcement learning, agentic AI, and software engineering automation. The company says it is developing open foundation models and advancing both pre-training and post-training systems, with an emphasis on reinforcement learning at scale. In October 2025, Reflection said it had built a large-scale large language model and reinforcement learning platform capable of training mixture of experts models at frontier scale, first applying the approach to autonomous coding and later expanding it toward general agentic reasoning. Reflection's first product was Asimov, a code-comprehension agent for engineering teams. Wired reported in July 2025 that Asimov reads source code, emails, Slack messages, project updates and documentation to answer questions about how software systems are built.

Funding and partnerships In March 2025, Reflection emerged from stealth with $130 million in financing, including a $25 million seed round and a $105 million Series A round. The financing valued the company at about $545 million. In October 2025, Reflection raised $2 billion in a funding round that valued the company at $8 billion. Investors in the round included Nvidia, Eric Schmidt, Citigroup, 1789 Capital, Lightspeed Venture Partners and Sequoia Capital. In March 2026, Reflection and Shinsegae signed a memorandum of understanding to build one of the country's largest facilities powering AI models, a 250-megawatt data center in South Korea, with Nvidia supplying tens of thousands of chips. In May 2026, Axios reported that Reflection was partnering with the United States Department of Energy to support the Genesis Mission, a federal scientific research initiative, and would serve as the AI model provider for the U.S. National Laboratories. In June 2026, Axios reported that Reflection had signed a compute agreement with SpaceXAI for access to chips and hardware from the SpaceX Colossus data center. Under the agreement, Reflection would pay $150 million per month starting July 1, 2026, through 2029, after an initial ramp period. Following talks with investor JPMorgan Chase, Reflection was valued at $25 billion as of June 2026. In July 2026, Reflection and Nebius signed an agreement for over $1 billion in computing power, allowing Reflection access to Nvidia's GB300 chips through 2029.

See also AI-assisted software development List of AI-assisted software development tools List of AI software developed at universities List of artificial intelligence companies List of chatbots List of large language models List of open-source artificial intelligence software List of university artificial intelligence research centers

References

External links Official website Reflectionai on GitHub

Worked examples

Example 1 — a first encounter with Reflection AI

Start with the simplest possible case. Write down what Reflection AI 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 Reflection AI 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 Reflection AI 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 Reflection AI

In research: Reflection AI 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 Reflection AI 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: Reflection AI is common in secondary-school and first-year university syllabi. It links to neighbouring topics 2024 establishments in the United States, American companies established in 2024, Artificial intelligence companies, so understanding it makes those chapters shorter.
In everyday life: Look for Reflection AI 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.

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Reflection AI in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Reflection AI 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 Reflection AI out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Reflection AI in simple terms?

Why does Reflection AI 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 Reflection AI?

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 Reflection AI.