Archetype AI

Helping humanity make sense of the world with Physical AI

[Archetype AI @PhysicalAI]

AI goes into the real world 🌎 Today, we're starting a new chapter of Archetype as we celebrate our $35M Series A.

The physical world generates vast amounts of sensor and video data every day but most of it goes unused. After years of AI living behind screens — in dashboards, apps, and digital abstractions — intelligence is finally stepping into the physical world.

We're bringing the Archetype platform to more customers and evolving Newton from a powerful foundation model into an intelligence layer for the physical world—one that perceives, understands, and reasons across real-world systems, enabling hundreds of intelligent applications.

And this is just the beginning.

This funding round, led by IAG Capital Partners and Hitachi Ventures, with participation from Bezos Expeditions, Venrock, Amazon Industrial Innovation Fund, Samsung, E12, Systemiq Capital, HLV, and others, enables us to scale physical intelligence globally and continue advancing Newton as the premier foundation model for the real world.

Thank you to our team, partners, investors, and all of you for following our journey to making the world truly intelligent.

Onwards! 🚀

[Archetype AI @PhysicalAI]

https://t.co/kKjnvCoCaF

[Archetype AI @PhysicalAI]

Dive deeper:

[Archetype AI @PhysicalAI]

ICYMI 👀 Fine-tuning is now available for customers to adapt Newton to their operations using their company-specific data. Here’s what that means for your organization. ⬇️

[Archetype AI @PhysicalAI]

Impressive, if you ask us 😉

[Archetype AI @PhysicalAI]

📺 Starting soon —> Register now to hear from Jaime Lien, Archetype's Co-Founder and Chief Scientist, as she presents our latest research on the future of Adaptive World Models.

https://t.co/DKRRAxnYqK

[Archetype AI @PhysicalAI]

Check out Agent Skills on GitHub: https://t.co/qy10dlT9ph

[Archetype AI @PhysicalAI]

Our Energy Grid Agent, powered by Newton analyzes millions of data points in seconds. We built the whole thing with Claude Code and Archetype AI Agent Skills. A few skills wrapping Newton was all it took. If you can describe the workflow, you can ship it.

[Archetype AI @PhysicalAI]

We’re celebrating two new hires this week.

Olivia Mora comes to us with 10+ years of experience in the enterprise tech space and will be running our social media accounts. She was most recently in-house at GitLab and prior to that built her career in PR and social media agencies.

Aleksandr Avseyev joins with 25+ years of experience in software development including working on desktop, mobile, backend, games and web applications. He was most recently at Verkada, Roblox, and Facebook respectively.

Welcome to the Archetype AI team Olivia and Aleksandr!

[Archetype AI @PhysicalAI]

@_jaimelien_ Save your spot here: https://t.co/0UposJpKMl

[Archetype AI @PhysicalAI]

Curious how world models can change your operations and how to choose the right model for your use case? On May 20, join our Chief Scientist, @_jaimelien_ , for a discussion of the Newton World Model — our Physical AI architecture for scalable machine understanding. We’ll explore the research, the architecture, and demonstrate how adaptive world models enable operational intelligence for any machine, in any environment.

[Archetype AI @PhysicalAI]

Watch our Energy Grid Agent, powered by Newton World Model, analyze millions of data points from CAISO supply and demand data at 5-minute resolution.

🔹 The demand chart overlays actual consumption against day-ahead and hour-ahead forecasts.
🔹 Newton analyzes grid conditions via the direct query API, explaining duck curve patterns, battery dispatch strategy, renewable curtailment, and peak demand stress.
🔹 Data auto-refreshes every 5 minutes with a visible countdown.

[Archetype AI @PhysicalAI]

Learn more: https://t.co/M0Xw3rCx47

[Archetype AI @PhysicalAI]

Time series data + language = TimeFusion. In our latest demo, TimeFusion, the first general sensor-language fusion model, processes raw IMU data from a smartphone, and identifies activities (shaking, raising your hand) without explicit training. Then, when queried in natural language, it generates a control signal: 0 for no motion-of-interest, 1 when detected.

[Archetype AI @PhysicalAI]

Welcome to Archetype AI, Ethan Chang! Joining the team as our first Design Fellow, Ethan is a part of MIT's Design Intelligence Lab and Ideation Lab and has an impressive portfolio spanning AI cohabitants, generative games, and intelligent systems.

[Archetype AI @PhysicalAI]

👀 We are launching a 10-week Design Fellowship at @PhysicalAI for students who want to explore what interaction looks like when AI is no longer confined to a screen.

❓ As AI systems begin to sense and interpret the physical world, new design questions emerge.

✏️ You will be matched to a focused project to answer some of these questions, informs product direction, advances our understanding of how people relate to Physical AI.

We welcome applicants from a range of disciplines, including graduate students in Design, HCI, Robotics, or related fields.

This role is a strong fit if you:
• move fluidly between design and engineering
• are as comfortable in code as in concepts
• enjoy working on ambiguous, early-stage problems
• use prototyping as a way to think and tell stories

🛠️ More than anything, we are looking for builders: people who are curious, resourceful, and drawn to problems without established playbooks.

Apply here: https://t.co/VPnuNme9t6

#fellowship #design

[Archetype AI @PhysicalAI]

One foundation model, adapted across very different operations. You can now fine-tune Newton, to the specific behavior of:
🔹 A manufacturing line — learning the failure signatures of your specific equipment and the tolerances of your specific processes.
🔹 An energy asset — internalizing the wear patterns of your turbines, pumps, or compressors across your operating conditions.
🔹 An industrial facility — capturing the environmental and operational fingerprint that makes one site different from the next.
The mechanism is the same across all three. Newton's general understanding of physical signals, adapted with your data, running on your infrastructure. Years of sensor history that previously sat unused becomes a working model of how your operations actually run, and one that keeps adapting as those operations evolve.