# Beam: The Great American Open Model with ReflectionAI Co-Founder and CEO Misha Laskin **URL:** \ **Category:** Content Creators\ **Tags:** no-priors:-ai, machine-learning, \-tech, open-source, \-startups, tech\ **Created:** [9 October 2026 17:28 UTC](https://www.artofsm.art/t/beam-the-great-american-open-model-with-reflectionai-co-founder-and-ceo-misha-laskin/25382 "2026-10-09T17:28:41Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [9 October 2026 17:28 UTC](https://www.artofsm.art/t/beam-the-great-american-open-model-with-reflectionai-co-founder-and-ceo-misha-laskin/25382/1 "2026-10-09T17:28:41Z")
[![](https://www.artofsm.art/uploads/default/original/3X/a/1/a1ef52d13cad7dc37baff9a30d3b11118e23fc1d.jpeg "Beam: The Great American Open Model with ReflectionAI Co-Founder and CEO Misha Laskin") ](https://www.youtube.com/watch?v=up4sG9RM20M) Misha Laskin, co-founder and CEO of Reflection AI, discusses the development of Beam, an open-weight AI model designed to advance intelligence through a combination of reinforcement learning and large-scale pre-training, emphasizing the importance of openness for security, innovation, and geopolitical advantage. He highlights the shift toward enterprises adopting open models for customization and control, advocates for pragmatic AI safety through transparency and collaboration, and envisions AI’s transformative impact on scientific research and economic growth. ---
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [9 October 2026 18:08 UTC](https://www.artofsm.art/t/beam-the-great-american-open-model-with-reflectionai-co-founder-and-ceo-misha-laskin/25382/3 "2026-10-09T18:08:55Z")
In this insightful discussion, Misha Laskin, co-founder and CEO of Reflection AI, shares the journey and vision behind building Beam, Reflection AI’s first open-weight model designed to power the future of intelligence. Laskin emphasizes the complexity and multifaceted challenges involved in developing frontier AI models, highlighting that success requires getting numerous elements right simultaneously—talent acquisition and retention, data collection, compute infrastructure, and cohesive team culture. Reflection AI has grown rapidly to around 300 people, focusing on integrating reinforcement learning with large-scale pre-training to create efficient, agentic models capable of adapting quickly to diverse tasks. Laskin discusses the evolving landscape of AI model development, noting the shift from primarily closed models to a growing open model ecosystem, particularly driven by Chinese labs. He underscores the geopolitical and economic significance of open models, explaining that open-source AI serves as a form of digital infrastructure that can provide competitive advantages and geopolitical leverage, much like railroads or rare earth minerals. Despite concerns about safety and control, Laskin argues that openness fosters security through transparency, enabling a broader community to identify and patch vulnerabilities, much like the evolution of cybersecurity and encryption protocols. The conversation also delves into the economics and commercialization of AI models. Laskin explains that enterprises often begin by renting AI capabilities through closed models but gradually move toward owning and customizing open models to optimize costs and control. He highlights that most enterprise AI usage initially involves customizing systems around base models rather than fine-tuning the models themselves. Reflection AI positions itself as a partner that not only provides open models but also the necessary infrastructure and services to help enterprises deploy and scale AI solutions effectively. On the topic of safety, Laskin distinguishes between empirical, near-term risks such as cybersecurity threats and more speculative, long-term existential concerns. He advocates for a pragmatic approach to AI safety, focusing on continuous patching and alignment improvements through reinforcement learning and data-driven methods. He stresses that AI should be viewed as an advanced form of software—complex but manageable through open collaboration and rigorous engineering rather than restrictive control. This perspective supports the belief that democratizing AI access ultimately enhances safety by empowering more defenders against misuse. Looking ahead, Laskin expresses excitement about AI’s transformative potential in scientific research and real-world applications. He shares personal experiences of using language models to accelerate complex scientific work, envisioning a future where AI dramatically shortens research cycles and enables breakthroughs in fields like physics, life sciences, and material science. Additionally, he highlights the economic impact of AI infrastructure, comparing data centers to modern factories that create high-paying jobs and stimulate local economies. Overall, Laskin sees AI as a powerful tool for innovation and economic growth, with open models playing a crucial role in shaping a competitive and collaborative ecosystem. ## Useful Links - [Reflection AI Beam Model Announcement and Technical Report](https://reflection.ai/blog/introducing-beam) — Directly explains the model discussed and its technical details. - [AlphaGo Reinforcement Learning Research Papers](https://deepmind.google/research/alphago/) — Explains the reinforcement learning lineage and methodology referenced. - [OpenRouter - Open Source API Gateway for Open Models](https://openrouter.ai/docs/quickstart) — Illustrates ecosystem infrastructure supporting open models. - [LLaMA 2 Model Release by Meta AI](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) — Contextualizes the open model ecosystem and base models used. - [Reinforcement Learning from Human Feedback (RLHF) Overview](https://aws.amazon.com/what-is/reinforcement-learning-from-human-feedback/) — Explains the reinforcement learning techniques used in Beam’s training.