Visions of AI: GPT

Good Evening,
Visions of AI is a new feature format I’m experimenting with that will amount to a short profile on an AI or emerging tech startup. The cadence of this style of article is unknown as of yet, but there are a lot of fascinating startups I want to discuss and share about.
This is designed to be light evening reading to go out at a time-slot of 8 pm EST.
In late 2026 there’s been a very sudden spike in massive Physical AI and embodied AI funding rounds, especially in the software around the robotic brain, and humanoid robotics, a form factor of bipedal (human-like) robots. While it remains to be seen how significant or enduring this trend is, it follows a flurry of Chinese robotics companies going public. China is widely seen as the robotics leader of the world, especially in current sales, hardware and manufacturing.
About three weeks ago a Physical AI startup called Skild AI released their S1, an in-context learning model for robotics. Skild AI has hit over $100 million1 in revenue already in 2026. The company claims that S1 new is a foundation model that learns from one example (one-shot learning). They said that it can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning.
Embodied AI with a Real Revenue Ramp (Skild)
After doing some digging while it’s a marketing idea (being pushed by the industry), I do believe S1 could qualify as the GPT-3 moment for robotics learning, at least in its 2027 iterations. In the history of AI, that would correspond to around 2020. Generative Pre-trained Transformer 3 (GPT-3) was a landmark 175-billion parameter LLM developed by OpenAI where the research paper introducing it was published on May 20th, 2020, about 3.5 years before the ChatGPT moment itself.
This is tremendously exciting for robotics and Physical AI enthusiasts.

Facts Only

* Visions of AI is a new feature format experimenting with short profiles on AI or emerging tech startups.
* A spike in funding for Physical AI and embodied AI occurred in late 2026, focusing on robotic brains and humanoid robotics.
* This trend follows the public listings of Chinese robotics companies regarding sales, hardware, and manufacturing leadership.
* Skild AI released S1, an in-context learning model for robotics, about three weeks prior to the text being written.
* Skild AI has achieved over $100 million in revenue in 2026.
* Skild AI claims S1 is a foundation model capable of one-shot learning from a single video prompt without fine-tuning.
* The article compares S1 to the GPT-3 moment for robotics learning, placing its potential impact around 2027 iterations.

Executive Summary

A new feature format called Visions of AI is being experimented with to provide short profiles on AI or emerging tech startups, intended for evening reading at 8 pm EST. The trend in late 2026 shows a significant spike in funding for Physical AI and embodied AI, particularly in areas related to robotic brains and humanoid robotics. This trend follows the public listings of Chinese robotics companies, which are widely regarded as global leaders in robotics sales, hardware, and manufacturing. A specific example is Skild AI, a Physical AI startup that released S1, an in-context learning model for robotics, which has achieved over $100 million in revenue by 2026. Skild AI claims S1 is a foundation model capable of one-shot learning for tasks from single video prompts without fine-tuning. The development of this technology is being positioned as analogous to the GPT-3 moment for robotics learning, potentially occurring around 2027 iterations.

Full Take

The narrative presents a convergence between advanced large language model concepts (like in-context learning) and physical embodiment, suggesting a potential inflection point for robotics development analogous to the shift catalyzed by LLMs. The comparison of Skild AI's S1 to GPT-3 highlights a conceptual leap: moving from purely linguistic reasoning to embodied, single-example learning in robotics. This framing establishes a pattern where foundational model breakthroughs are being translated directly into physical systems. The observation that this trend is mirrored by the rise of Chinese robotics leaders suggests that geopolitical and manufacturing advantages may be intersecting with pure algorithmic innovation to drive rapid adoption in Physical AI. The core implication lies in assessing whether the leap from demonstrated in-context learning to robust, real-world embodied autonomy is supported by sufficient infrastructure and safety considerations, or if this momentum risks outpacing necessary systemic reflection. What are the unseen costs associated with accelerating this timeline for physical AI development? Does the focus on revenue benchmarks obscure potential long-term hurdles related to true generalization and safety in complex robotic systems?

Sentinel — Likely Human

Confidence

The text reads like a personal commentary or an experiment designed to share enthusiasm, heavily relying on subjective belief and analogy alongside cited facts about the industry developments.

Signals Detected

Sentence length variance is present, though the tone leans toward expository rather than purely narrative.

The text flows logically from a general observation (funding spike) to a specific example (Skild AI) and then to a comparative analysis (GPT-3 analogy).

Uses transition words ('While', 'About', 'After') effectively, but the voice is clearly personal and opinionated.

Specific dates (2026), figures ($100M revenue), and historical comparisons are presented with a strong subjective interpretation ('I do believe S1 could qualify as...').

Human Indicators

Presence of first-person framing ('I’m experimenting', 'I do believe') which injects subjective voice.

Use of strong, speculative comparative analogies (GPT-3 moment for robotics) that reflect personal intellectual structuring rather than pure statistical reporting.

Inconsistent pacing and tone fluctuation between introductory musing and factual claims.