Reflection AI

Reflection AI
Reflection AI, Inc.
Type	Private company
Industry	Artificial intelligence, Software development
Founded	March 2024
Founders	Misha Laskin (CEO) Ioannis Alexandros Antonoglou (CTO)
Headquarters	300 Kent Avenue, Brooklyn, New York, United States
Key people	Misha Laskin (CEO) Ioannis Alexandros Antonoglou (CTO)
Products	Asimov (Code Research Agent) Coding Agent API Autonomous coding systems
Valuation	$8 billion (October 2025)
Employees	46 (2025)
Website	reflection.ai

Reflection AI is an American artificial intelligence company focused on developing autonomous systems and superintelligence, with a primary emphasis on autonomous coding agents. Founded in March 2024 by former Google DeepMind researchers Misha Laskin and Ioannis Alexandros Antonoglou, the company emerged from stealth mode in March 2025 with $130 million in funding. The company is headquartered in Brooklyn, New York, with additional offices in San Francisco and London, and is working to build what it describes as "frontier open intelligence."

History

Founding and Background

Reflection AI was founded in March 2024 by Misha Laskin and Ioannis Alexandros Antonoglou, both prominent artificial intelligence researchers with extensive experience at Google DeepMind. The founders left DeepMind in March 2024 to establish the company, driven by their research question about building superintelligent systems capable of acting autonomously.

Misha Laskin previously led reward modeling efforts for DeepMind's Gemini project after working under AI luminary Pieter Abbeel at UC Berkeley. Laskin's journey into AI was sparked by reading the AlphaGo paper in 2016 while pursuing a quantum physics PhD, which led him to change his career path to artificial intelligence.

Ioannis Antonoglou spent more than a decade at DeepMind, where he was one of the core architects behind AlphaGo, the first AI system to defeat a world champion in Go. He joined DeepMind as employee #25 and researcher #6 in 2012. Antonoglou also contributed to the development of AlphaZero and MuZero, and led the RLHF (Reinforcement Learning from Human Feedback) effort for Gemini.

The company's name, "Reflection," reflects its emphasis on AI systems that can "reflect" on their actions, learn from mistakes, and improve iteratively, much like human reasoning processes.

Launch and Early Development

The company officially emerged from stealth mode on March 7, 2025, announcing it had raised $130 million across two funding rounds. The founders stated that their strategy was initially at odds with where most of the industry was heading, as they decided to train their own models rather than relying on existing ones, a move that seemed prohibitively expensive for a small startup at the time.

Products and Technology

Asimov Code Research Agent

In July 2025, Reflection AI introduced Asimov, described as the company's "first product milestone on the path to superintelligence." Asimov is positioned as a collection of cooperating agents that retrieve and synthesize information from codebases and organizational knowledge (for example emails, project documents, GitHub threads, chat history) to answer questions and perform development tasks.

Asimov features three key components:

1. Single source of truth for engineering knowledge: Asimov ingests entire codebases, architecture documents, issue trackers, and other organizational data to build persistent memory of systems
2. Team-wide memory system: Engineers can update Asimov's knowledge using natural language commands, allowing senior engineers to offload context to the system
3. Multi-agent architecture: The system uses multiple small long-context agents (retrievers) that retrieve relevant information from large codebases, combined with a large short-context reasoning agent (combiner) that synthesizes information

In blind testing with maintainers of large open source projects, Asimov's answers to complex questions were preferred 60-80% of the time relative to other coding products. Reflection states Asimov is deployed inside customers' virtual private clouds, designed so proprietary data remains within the customer environment.

Coding Agent API

The company's core product offering is the Coding Agent API, designed to automate routine engineering tasks. The API allows AI agents to:

• Scan code for vulnerabilities
• Optimize memory usage
• Test for reliability issues
• Generate documentation
• Manage application infrastructure

The autonomous coding agents integrate directly into organizations' codebases and engineering workflows, autonomously tackling well-scoped engineering tasks end-to-end.

Technical Approach

Reinforcement Learning and Language Models

Reflection AI's technical approach combines reinforcement learning (RL) with large language models (LLMs). The founders believe that these are "the two building blocks for building a superintelligence," with language models providing generality and reinforcement learning providing capability. The company views RL as a scalable path to superintelligence, with no known upper limit, unlike supervised learning methods.

The company is pioneering RL post-training techniques that enable AI models to go beyond simple code completion. Their system can plan, debug, and execute complex programming tasks autonomously, evaluating multiple possible solutions, learning from failures, and refining its approach dynamically. This method integrates LLMs' generality with RL's depth, aiming for "jagged intelligence" that excels in specific domains like coding before generalizing.

Path to Superintelligence

Reflection AI's two-step plan starts with building a superintelligent autonomous coding system, then expanding to other computer-based work. The company believes solving autonomous coding is "AGI-complete," as it requires breakthroughs in reasoning and self-improvement that transfer to broader applications. Evaluations focus on real-world scenarios, with user feedback integrated to ensure reliability and ethical design.

Infrastructure and Architecture

According to job postings and company statements, Reflection AI plans to:

• Train models using up to tens of thousands of graphics cards
• Explore novel architectures beyond the Transformer neural network architecture
• Work on "vLLM-like platforms for non-LLM models" to reduce memory usage
• Train its own models through efficient engineering to optimize compute and data usage, prioritizing safety by operating within defined boundaries

Funding and Valuation

Initial Funding Rounds (March 2025)

Reflection AI raised a total of $130 million across two rounds in March 2025:

Funding History

Date	Round	Amount (USD)	Lead/Notable Investors	Valuation	Notes/Sources
March 2025	Seed	$25 million	Sequoia Capital, CRV	-
March 7, 2025	Series A	$105 million	Lightspeed Venture Partners (co-lead), Sequoia Capital (co-lead), CRV; participation from NVentures, Databricks Ventures, Reid Hoffman, Alexandr Wang, SV Angel	$545-555 million
September 2025	Reported new round (in process)	≈$1 billion (target)	NVIDIA (≥$250-500 million), Lightspeed Venture Partners, Sequoia Capital, DST Global, 1789 Capital	$4.5-5.5 billion (reported)
October 9, 2025	Series B	$2 billion	NVIDIA (lead)	$8 billion	Marked a significant escalation in investor interest amid a broader AI funding boom

The October 2025 Series B funding round brought the company's valuation to $8 billion, marking a nearly 15-fold valuation increase from March 2025. This occurred during a period where global investments in AI foundation model companies reached $71.9 billion in 2025.

Mission and Vision

Reflection AI's stated mission is to build "frontier open intelligence and making it accessible to all," with an emphasis on open-weight models that can be inspected and forked. The company defines superintelligence as "a thing that creates value by doing work on computers" and believes that "a coding agent is actually going to be the way that a language model does work on any piece of software in the future."

The company emphasizes three defining aspects:

1. A team behind some of the most capable RL and LLM systems ever created
2. Building the best autonomous coding systems in the world
3. Equal emphasis on research and product, believing that "superintelligence cannot be built in a vacuum"

Reflection AI positions itself as a Western counter to open-source models like DeepSeek, advocating for open intelligence to maintain U.S. technological leadership.

Team and Culture

As of 2025, Reflection AI employs approximately 46 people. The company has assembled what observers describe as "the highest-density RL talent of any startup," with researchers and engineers from DeepMind, OpenAI, Google Brain, Google, Meta, Character.AI, and Anthropic.

Co-founder and CEO Misha Laskin leads with a philosophy of simplicity in AI design, while CTO Ioannis Antonoglou brings expertise in pioneering systems like AlphaGo. The team values intensity, kindness, craftsmanship, speed, and rapid iteration.

The company conducts most work in person and is actively hiring across AI research, engineering, and infrastructure roles, with offices in Brooklyn (headquarters at 300 Kent Avenue in Williamsburg), San Francisco, and London.

Competition and Market Position

Reflection AI operates in the highly competitive AI agents and autonomous coding space. The company's main competitors include:

• OpenAI
• DeepSeek (Chinese AI startup)
• Anthropic
• Cohere
• Other AI coding assistant companies

The company positions itself as building more autonomous systems compared to "co-pilots or assistants," aiming for tools that are fully autonomous rather than requiring human oversight.

Reception and Impact

Industry observers and investors have expressed strong confidence in Reflection AI's approach. Sequoia Capital partners noted that the company's approach represents "a leap forward that positions Reflection AI as a major player in the future of intelligent software engineering and beyond."

CRV stated that the technology Reflection is building "has the potential to redefine industries," envisioning "a world where engineering teams can tackle their backlogs in days instead of months, where code migrations happen seamlessly and where cyber vulnerabilities are remediated before they become critical."

See Also

• AI agent
• Reinforcement learning
• Large language model
• AlphaGo
• AlphaZero
• MuZero
• Artificial general intelligence
• DeepMind
• Autonomous systems

Notes

• This article summarizes publicly reported information as of October 9, 2025.

External Links

• Official website
• Reflection AI Careers
• Reflection AI on LinkedIn
• Terms of Service