The article argues that, limited by onboard compute and heat dissipation in robot bodies, embodied model inference is shifting to the cloud, and embodied AI MaaS is becoming a new direction. In July 2026, Yuanli Lingji released a general embodied MaaS service; in October, Physical Intelligence launched an invitation-only Pi API Beta; and Dream Labs launched Dream API backed by a $90 million seed round.
After GPT-6 Astra's release, general large models can directly control robots to perform tasks; embodied data orders are rebounding, the value of body companies is being reassessed, and model companies building embodied brains now face competition on the same field as foundation model giants. In NVIDIA testing, Astra achieved a 66.3% success rate in zero-shot direct action output, higher than π0.5, but average single-inference time was 18.56 seconds, and one successful action consumes about 160,000 tokens, equivalent to $1.96. The industry currently prefers having general models handle planning and the semantic layer, while embodied models handle millisecond-level motion control and the execution layer.
Foreign media report that Physical Intelligence is in talks for a new funding round, planning to raise at least $1 billion at a target valuation of about $20 billion, more than 8 times its $2.4 billion valuation in November 2024. The company was founded in 2024, develops the π series of robot models, and opened a beta version of the Pi API on October 13. The article also compares Skild AI: 10 months of formal commercial deployment, more than 60 paying customers, ARR exceeding $100 million, and cumulative recognized revenue of $50 million.
GXO announced on October 26 that Agility's Digit will handle e-commerce fulfillment at warehouses in Statesville, North Carolina, and Lebanon, Tennessee, starting in early 2027, beginning with Digit 4 before moving to Digit 5; at a Georgia site, Digit has already moved more than 100,000 totes.
In the 50 days since GPT-6 Astra was released, the embodied AI industry has conducted intensive evaluations and discussions around it, and general-purpose large models have begun directly taking over robot operation. Tests by SenseTime and several institutions concluded that Astra is basically capable of completing embodied tasks, but still has a clear gap from practical use in real environments, underperforming in millimeter-level contact, dynamic control, and response speed. The article also reviews industry uses such as using Astra directly for policies, reviewing VLA actions, building simulation environments, and data annotation, as well as chain reactions including cooling financing and major companies stepping up data collection.
Why it matters: It reviews evaluation results, company reactions, and industrial chain changes in the embodied AI industry within 50 days of GPT-6 Astra's release, presenting the differentiated landscape after general-purpose large models entered robot operation.
US robotics AI company Physical Intelligence is in talks for a new funding round of at least $1 billion, with a post-money valuation expected at about $20 billion, a sharp rise from its $5.6 billion valuation last November. The company has no commercial revenue to date and has not mass-produced its own robots; its core focus is general-purpose robot foundation models that adapt to third-party hardware. It has iterated from π0 to π0.7 and this month launched a beta API for its robot foundation model, offering model fine-tuning and inference services.
Enact Intelligence, a physical AI company headquartered in Zurich, Switzerland, has closed a €26.68 million (about $30 million) pre-seed funding round, with investors including redalpine, Notion Capital, and firstminute Capital.
On October 13, Physical Intelligence (PI) announced the launch of the Pi API beta, packaging robot data upload, hosted fine-tuning, cloud inference and policy generation, and evaluation feedback into a single service chain, in an attempt to sell embodied brains via API.
Speakers come from companies including 1X, Dyna Robotics, Skild AI, Google DeepMind, and Physical Intelligence.
On-site demonstrations will feature humanoid robots, dexterous hands, and other robots.
The robotics community gathers together, with four NVIDIA Jetson units to be given away free.
Agenda:
"Experiments in building products at Physical Intelligence" — a keynote by Quan Vuong (@QuanVng) at the Humanoid Hub Conference.
He co-founded @physical_int and previously helped build the RT-2 and OpenVLA foundation models at Google DeepMind.
He will give us an inside look at the work of turning research into real-world value.
October 15-16, San Francisco
Ultra announced the completion of $62 million in funding, of which the $50 million Series A was led by @hiFramework; partner Physical Intelligence reposted congratulations. In the quote, Ultra argues that deployment is the bottleneck in the robotics industry: making robots perform well in controlled environments is relatively easy, but making them enter the open world, remain useful, and earn the trust of those around them is far harder, and this funding is meant to solve the distribution problem. Physical Intelligence added that combining general-purpose robot foundation models with real-world deployment is making such things possible.
Chelsea Finn shared a video of a Pi robot deployed at Dandelion Chocolate, running fully autonomously with no human intervention. In her review, she said the hardest part was unexpectedly reliable stacking rather than box folding itself: the original table-mounted robot struggled to see the stacking state without a separately added camera, and each box was placed in a different position, with a misplacement causing the whole stack to collapse later.
Although we share a lot of research work at @physical_int, we rarely talk about what it's like to work here. Here is a great blog post by @stone_tao about what it's like to work at Pi, and a bit about Pi's simulation!
General-purpose AI models underpin some of the most exciting applications we now can't live without. We envision that an analogous "physical intelligence layer," built with models like π0.6, will similarly drive a new wave of applications in the physical world.
We have recently begun working with a small number of companies whose robots are already performing practically useful tasks in the real world.
https://www.pi.website/blog/partner/?v=1