Beyond Motion: How Robots Will Redefine The Art Of Movement https://t.co/pe0mdlEJcu
— Paramendra Kumar Bhagat (@paramendra) October 22, 2025
Summary: “Physically Specific Intelligence” as the Next Phase of AI
This thesis argues that instead of Artificial General Intelligence (AGI), the next major leap for AI will come from physically specific intelligence — AI systems deeply integrated into the physical world through robotics and automation.
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AI’s Hype Cycle:
AI is entering the “trough of disillusionment.” Despite cooling hype, this is the best time for serious builders and investors to enter the field as weaker players exit. -
Dual Nature of AI:
AI is both incredibly powerful (search, summarization, visualization, prototyping) and frustrating (spam, scams, low-quality content). Many focus on its flaws, missing the bigger picture. -
Unexpected Plateau:
Current large language models (LLMs) have peaked at tasks like advanced search and summarization. That’s transformative (disrupting Google), but still far from true reasoning or creativity. -
Why Models Plateaued:
LLMs “repeat” rather than “think.” They are downstream — synthesizing human text — not generating truly novel insights. Their main limitation is the need for human prompting and verification. -
Physical vs. Digital AI:
Digital AI operates “middle-to-middle,” while physical AI (robots) can perform end-to-end tasks. Self-driving cars exemplify this: they sense, decide, and act autonomously. -
Rise of Physically Specific Intelligence:
The greatest near-term progress will occur in clearly defined, high-value physical tasks — like driving, delivery, manufacturing, and household robotics. China is already advancing rapidly here. -
Consensus Reality Advantage:
Physical-world AI (via robotics and sensors) gathers consistent real data through shared sensing (e.g., SLAM). Digital AI, however, ingests inconsistent or fake text increasingly polluted by AI-generated content. -
The Physical Learning Network:
Millions of robots collectively mapping the real world can share and improve upon each other’s data — forming a grounded, verifiable base of reality-based intelligence. -
Industrial and Consumer Impact:
Factory robots embody the industrial side of physically specific intelligence. On the consumer side, it will manifest as “a garden of intelligent things” — cars, drones, home devices, and robots that interact naturally with humans. -
Conclusion:
Until major algorithmic breakthroughs occur, the next big wave in AI isn’t abstract AGI but real-world, embodied intelligence — machines that understand and act within our shared physical environment.
LLMs Are Just Getting Started: The Coming Age of Vertical Intelligence
For all the noise about “AI plateauing,” the truth is that Large Language Models (LLMs) are barely in their infancy. What we’re seeing today — ChatGPT, Claude, Gemini, and others — represents the general-purpose, broad-strokes phase of digital intelligence. But the next era, the vertical LLM era, will make today’s generalists look primitive.
Far from being an argument against physical or robotic AI, the rise of vertical LLMs complements it — forming the cognitive backbone of the AI revolution across industries, sciences, and societies.
1. The Misconception of the Plateau
Critics claim LLMs have peaked: they summarize, search, and draft well, but fail to “think” or innovate. But that claim overlooks how every technological paradigm begins. The first web browsers looked unimpressive compared to what came a decade later. The first mobile apps were clunky before the app ecosystem exploded.
Similarly, today’s general LLMs are foundational platforms — the equivalent of the early internet. They have reached saturation at the horizontal level, but not vertically. The true explosion will happen when we stop trying to make one model do everything and instead make thousands of models each do one thing brilliantly.
2. The Vertical LLM Revolution
Vertical LLMs are specialized models trained intensively on specific domains — medicine, law, finance, logistics, agriculture, energy, education, and so on.
A vertical LLM doesn’t just know about its field; it lives in it. It understands the jargon, the workflows, the exceptions, and the tacit knowledge that domain experts carry.
Imagine:
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MedGPT, trained on millions of clinical notes and radiology reports, diagnosing with contextual awareness beyond any human physician’s recall.
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EduGPT, which understands every curriculum in the world, teaching any subject in any language or dialect, adapting to each student’s style.
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LawGPT, parsing legislation, precedent, and contracts with the precision of a seasoned jurist.
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AgriGPT, optimized for local soil, weather, and crop data, guiding farmers across geographies.
This is not science fiction. It’s an inevitable next step — because domain-specific models will simply outperform general ones where stakes are high.
3. The Power of Depth Over Breadth
General LLMs are extraordinary at breadth — they can hold conversations across philosophy, programming, and poetry. But true intelligence in human civilization has always come from depth: from the scientist who spent decades studying one element, or the historian who mastered one period.
Vertical LLMs replicate that depth at scale. By focusing training on narrow, high-quality datasets and using continual fine-tuning, these models will not only surpass general LLMs in accuracy but also in reasoning within their field.
Just as specialists outperform general practitioners in surgery, vertical models will outperform ChatGPT-like models in real-world decision-making.
4. Complementarity with Physical AI
This is not an argument against physical intelligence — rather, it’s a partnership. Physical AI (robots, self-driving cars, drones) acts in the real world; vertical LLMs think about the real world.
A self-driving truck may navigate a road autonomously, but when that road is blocked, it may consult a LogisticsGPT to reroute intelligently. A surgical robot may execute precise movements, but it’s guided by a MedGPT that interprets scans, monitors vitals, and suggests procedures.
Physical AI provides agency; vertical LLMs provide judgment. One acts; the other advises. Together they create complete, end-to-end intelligence loops — cognitive and kinetic, digital and physical.
5. The Data Renaissance
Another reason LLMs are far from finished: we’re just entering a data renaissance. As more sectors digitize, the availability of structured, proprietary, high-fidelity data will explode.
The next decade won’t be about scraping the public web; it’ll be about training on private, permissioned, and verified datasets: hospital archives, industrial logs, supply chains, and research repositories. This shift from public “slop” to verified “truth” will massively improve model performance and reliability.
Whereas the internet was chaotic, the vertical data ecosystems will be curated, regulated, and contextual. That’s where LLMs will truly begin to understand rather than merely predict.
6. The Rise of the Cognitive Stack
The future enterprise stack won’t be defined by databases or spreadsheets — but by LLMs as reasoning engines. Each organization will have a cognitive core:
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A core model (company-specific LLM) trained on internal knowledge.
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Vertical assistants embedded into workflows (finance, HR, marketing, compliance).
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Physical interfaces (robots, drones, IoT devices) carrying out the recommendations.
This stack — cognitive + physical — will redefine productivity. A factory might run on IndustrialGPT, a hospital on CareGPT, and a city on CivicGPT. The end of “software as a service” will give way to “intelligence as infrastructure.”
7. LLMs as Engines of Discovery
One of the most underestimated powers of LLMs is their potential for discovery. When vertical models are fine-tuned on experimental data — in chemistry, materials science, genomics, or energy — they will start generating hypotheses humans have never thought of.
Already, AI is designing new drugs, optimizing molecular structures, and discovering mathematical proofs. These are glimpses of what comes when language models are fused with symbolic reasoning, graph learning, and simulation engines.
Vertical intelligence will make LLMs not just describers of reality, but inventors within it.
8. A Thousand New Minds
We will soon have thousands of specialized minds — each an LLM trained for a specific domain, geography, or culture. They’ll converse with each other, trade insights, and collaborate across industries.
General-purpose LLMs like GPT will serve as the “language bridge” between them — the digital equivalent of a lingua franca. Together, they’ll form a planetary web of intelligence: distributed, specialized, and always learning.
9. The Real AGI Will Be a Network, Not a Node
If AGI ever emerges, it won’t be from one monolithic brain but from the networked coordination of many vertical intelligences. Each model — from medical to mechanical — will contribute expertise. The emergent intelligence will come from their interplay.
The future of AI, then, is neither purely digital nor purely physical. It’s hybrid. It’s a world where intelligence flows — from chips to circuits, from text to steel, from neurons to motors.
10. The Beginning, Not the End
So, no — LLMs haven’t plateaued. They’ve just crossed their first threshold. We’re moving from general chatbots to expert systems that can transform entire industries.
The coming decade won’t be defined by a single “smart” AI, but by millions of purpose-built minds quietly revolutionizing how we work, learn, heal, grow, and build.
General AI lit the spark. Vertical AI will light the world.
I respectfully disagree: https://t.co/9dLecnxEE9
— Paramendra Kumar Bhagat (@paramendra) October 22, 2025
Prediction: LLMs in their current form may not be able to do everything, but AI now has enough momentum that this won't matter. Beam engines couldn't do everything either, but they were enough to set off the Industrial Revolution.
— Paul Graham (@paulg) October 21, 2025
