● Physical AI Gold Rush, Post LLM Reality Boom
Why “Physical AI” Becomes the Real Buzzword in 2026: The Next Chapter After LLMs Is Opening into the “Real World”
Today’s post includes these key takeaways.
1) Why the claim “LLMs have limits” is now becoming persuasive
2) Why the Total Addressable Market (TAM) unlocked by Physical AI and world models is in a different league
3) Where Big Tech (Tesla, Google, Meta, Bezos) and China are each placing their bets
4) Why the structure of “in the end, it’s data and inference chips” matters
5) What domestic companies and investors in Korea truly need to check
1) News Briefing: What’s happening right now
One-line summary
If LLMs are “agents for the digital world,” Physical AI is the arena that “replaces/augments labor and production in the real world,” so the market size and impact are on a different level.
Core flow (reorganized based on the original source)
LLMs have reached text/image/video generation and are rapidly replacing digital work.
But LLMs have never “touched anything, smelled anything, or fallen down” in the real world.
In other words, with only “next token (word/frame) prediction,” a wall emerges in the stability and reliability of real-world actions.
The keywords to break through this wall are Physical AI (AI with a body) and world models (engines that understand/predict reality through simulation).
2) Physical AI vs. World Model: They sound similar, but their roles differ
World Model
A foundation model that simulates/predicts “how the world works,” including the laws of physics in reality.
Using the original source’s analogy, it is closer to an engine like “Unreal Engine.”
Physical AI
An agent/robot/autonomous driving system that, based on a world model, executes “perception → planning → action” in the real world.
It is closer to the “game (service)” that runs on top of the engine.
Why this distinction matters
World models become core infrastructure that amplifies data (synthetic data) and lowers the cost of failure.
Physical AI faces an “adoption threshold” where, due to safety issues, “success rate” directly determines commercialization.
3) The decisive difference between LLMs and Physical AI: “next word” vs. “next action”
LLMs
They predict the next word/sentence/image frame.
If they are wrong, it usually ends as “hallucination” or “quality degradation.”
Physical AI
They predict the next “action.”
If they are wrong, it can escalate beyond spilling coffee to human injury, equipment damage, or death.
Why Physical AI inevitably progresses slowly
A 20–40% success rate may look plausible as a “research demo,” but it is dangerous for real deployment.
The robot videos you see on YouTube are highly likely to be “only the successful takes edited from dozens of attempts.”
4) What it really means to say the market size (TAM) is incomparable to LLMs
The main stage for LLMs
Digital-economy-centered domains such as documents, coding, content, commerce, and chat-based services.
The main stage for Physical AI
Manufacturing, logistics, agriculture, construction, household work, caregiving/medical assistance, resource mining, and the entirety of “real-world labor.”
Here, supply chains themselves can change, and if a productivity shock occurs, it can ripple into prices, wages, and growth rates.
Ultimately, the impact is large at the macroeconomic level (global supply chains, inflation, productivity).
5) Why Big Tech “heroes” are converging here: not money, but “the structure of the game”
Shared conclusion
Once Physical AI succeeds, it gains defensibility.
The reason is that as data, chips, deployment environments, and safety certifications accumulate, it becomes harder for latecomers to catch up.
In other words, a compounding effect of “real operational data” kicks in that is even stronger than network effects.
Major player landscape (reorganized based on the original source)
1) Tesla
A full-stack strategy to amplify the data accumulated from FSD with a world model and extend it to robots (Optimus).
It optimizes by combining hardware (such as camera placement) and software.
It is also laying out an inference-chip roadmap such as AI4 → AI5 (planned) → AI6 (planned).
2) Google
With Gemini Robotics and similar efforts, it puts “reasoning (planning) before acting” front and center.
However, the success rate is low (20–40% mentioned in the original source), and safety trust is the biggest barrier.
3) Meta + Yann LeCun line
It strongly takes the stance that “AGI is impossible with LLMs alone,” and sees a world model + Physical AI combination as essential.
The core is an approach that broadens the foundation of intelligence through “real-world understanding,” not “language.”
4) Jeff Bezos (Project Prometheus)
It focuses more on building the “brain” than the robot itself.
Notably, as an intermediate revenue model, it first bets on an “ultra-large digital twin.”
It is a structure that immediately saves costs and produces outcomes through Blue Origin (space) and automotive parts design/materials development.
This is not just research, but a strategy to preempt the industrial simulation market.
5) China
On the surface, “robot bodies (hardware)” demos are exploding.
As a national strategy, it mass-incubates startups and then restructures so only a few survive (the pattern used with EVs).
However, showmanship demos do not guarantee the reliability of the brain.
6) Two pillars of technical infrastructure: “inference chips” and “data” determine winners and losers
(1) Why inference chips are core
Physical AI must compute surrounding conditions (light, friction, obstacles, sound, tilt, the position of my hand, etc.) in real time.
This means not only data-center training but also high-performance on-site inference.
So a structure forms where “each robot/vehicle comes with a high-performance inference chip.”
Ultimately, the semiconductor industry and AI infrastructure competition become more directly connected.
This segment is an area where CAPEX keeps flowing regardless of interest rates, so the market is starting to view the AI investment cycle as a “long-term infrastructure investment.”
(2) Why data is the bigger bottleneck
Physical AI requires far more data than LLMs.
But real-world data is hard to obtain, risky, and varies by environment, making generalization difficult.
So world models/simulators generate synthetic data, but if the “raw material (real data)” is scarce, amplification still has limits.
7) A separate data industry grows: the “synthetic data supply chain” is the real game
The especially important passage in the original source
Competition in Physical AI is not only about robot hardware; it is that an ecosystem for producing training data grows together.
Representative axis
Simulation/digital twin platforms such as NVIDIA Omniverse play a central-axis role.
Companies that have begun a data pivot (mentioned in the original source)
Decart: Pivoting toward supplying training data for Physical AI through generative/synthetic data
Runway: Expanding video generation technology into a training data supply business for Physical AI
Niantic: Assetizing spatial data based on location-based/on-site image data
Why Tesla’s “YouTube learning” is scary
If observational learning truly cracks third-person video as well, the data bottleneck is greatly relieved.
This is the core of “if it succeeds, startups could all die,” and it is a game changer that rewires the data source itself.
8) Only the “truly important points” that other news/YouTube often don’t say
Point A: The first revenue for Physical AI is unlikely to be “robot sales”
As in the Bezos case, “cost reduction based on parts design/materials development/digital twins” becomes monetizable first.
In other words, before robots go mainstream, B2B simulation can generate cash flow.
Point B: The success-rate (reliability) problem is not model performance, but an “industrial safety” problem
Once regulation, insurance, and liability attach, Physical AI slows down sharply.
Therefore, the real KPI is not a “demo video,” but “accident-free operating time after deployment on-site.”
Point C: The trap Korea is likely to miss is “hardware obsession”
Actuators, batteries, and sensors are important, but they are not enough to win.
Without the brain (perception, planning, action) and a data/simulation pipeline, there is a high risk of becoming dependent on global standards.
Point D: From a macro perspective, Physical AI connects to a “productivity shock”
If Physical AI is used in real operations in logistics/manufacturing, labor-cost structures change, supply-chain bottlenecks shrink, and inflation pressure can structurally change.
This is not just a tech trend; it becomes a tool for global supply-chain reorganization.
Point E: From an investment perspective, “robot-related stocks” and “Physical AI beneficiaries” may differ
Unlike simple robot parts themes, the places where real value accrues are inference chips, simulation, data pipelines, safety/verification, and operations software.
You should look by value chain, not by theme.
9) 2026 Checklist: See it this way and the flow becomes clear at a glance
Check 1
The signal of commercialization is not “flashier demos,” but long-duration accident-free operation in confined spaces (logistics warehouses/factories).
Check 2
You should watch whether inference-chip price declines and adoption expansion show up in actual CAPEX indicators.
Check 3
Synthetic-data companies and simulation platforms may grow before “robot companies.”
Check 4
The U.S. may appear brain-centric and China body-centric, but the moment they ultimately converge, the outcome is decided.
Check 5
If Korea lacks a strategy for AI infrastructure and data centers, and for securing industrial on-site data, it could become locked in as a “parts supplier nation.”
< Summary >
Physical AI deals with “real-world actions” that LLMs cannot, so the market size is far larger, and safety reliability is the threshold for commercialization.
A world model is an engine that understands reality through simulation and creates synthetic data, and Physical AI is the executor that moves on top of it.
The outcome is more heavily tied to inference chips and data (especially synthetic-data pipelines) than to the robot body.
Tesla, Google, Meta, and Bezos are jumping in through different approaches, and China is repeating a national-strategy model of mass incubation followed by restructuring.
Korea cannot rely on hardware alone; if it fails to participate in data/simulation/brain competition, the likelihood of becoming dependent on global platforms is high.
[Related posts…]
- Physical AI Market: Why It Will Reshape the Industrial Landscape in 2026
- World Models and Digital Twins: A Core Point Summary of the Next-Generation AI Infrastructure
*Source: [ 티타임즈TV ]
– “LLM과 비교할 수 없는 규모 시장이 열린다” (강정수 박사)
● Cameron Slams GenAI Hollywood, IP Jobs at Risk
The truly scary reason behind James Cameron’s declaration, “I won’t use generative AI in my films for even one second” (and the signal this sends to the content industry and the economy)
Today’s piece includes exactly three things.
First, the core point of Cameron’s logic for saying “I won’t use AI for even one second” (not “tech hatred,” but a perspective of defending the production ecosystem).
Second, the point that his insistence on a 187-minute film in the short-form era is not about “reviving the theatrical industry,” but about a fight over “control of consumption behavior.”
Third, a整理 of what “movies made cheaply by AI,” which other interviews/reviews rarely highlight, ultimately does to the industry-wide cost structure, jobs, and IP value.
1) News summary: Only the key statements pulled from the interview
① “The starting point is performance, not technology”
Cameron says people ask about Avatar, “What is that technology?” but he firmly states the essence is “acting.”
He explains that performance capture is not a tool that “adds” technology, but is closer to a tool that “removes” elements that interfere with an actor’s performance.
In other words, he argues that what makes audiences feel “it looks real” starts with the actor’s emotion and rhythm before rendering.
② World-building runs on “culture + design + thousands of artists”
He says Pandora’s culture does not copy any single Indigenous culture as-is; instead, it blends multiple cultural motifs (in his words, “like putting them in a blender”) to create distinct patterns and technologies.
Even though the costumes are CG, they are not made carelessly; they are actually fabricated, tested for movement and weight, and then transferred back into CG.
③ “I didn’t use generative AI in the movie for even one second”
Cameron does not treat AI as one monolithic thing.
He separates “superintelligence (ASI)” as a different risk issue (a line of argument connected to Terminator’s Skynet), and
he views “generative AI” as more dangerous internally to the industry because it can replace creation, art, and actors.
④ His biggest worry is that “the growth path for young directors gets cut off”
He says the advantage of “you can make a movie even without money” can ironically become poison.
If “cheaply made movies” increase—without actors, without performance, and without on-set experience—the essential stages for a director to grow (working with actors, designing emotion) disappear.
⑤ Why he made a long film in the short-form era: “The theater is the choice that removes ‘clutter (noise)’”
He argues that it’s not that attention spans disappeared because short videos increased; 15-second and 30-second ads have existed for a long time.
Rather, now is an era where you can “control how you watch” through streaming, so
conversely, the cinema is powerful because it offers an experience of “temporarily giving up my control and becoming fully immersed.”
⑥ The essence of editing: “The courage to remove”
He says he cut as much as 30 minutes of good material.
The reason for cutting was “for a bigger purpose (an orchestrated experience).”
He uses the metaphor that you must carve away like a sculpture for the final form to emerge.
2) One step deeper: Cameron’s anti-generative-AI stance is not a technology debate, but an “industry structure” discussion
This is where it gets interesting from an economic perspective.
What Cameron truly wants to stop is not the “AI tool” itself, but the restructuring of the production ecosystem that AI creates.
① What generative AI changes is not “production cost,” but the “baseline standard of production”
If AI makes movies cheaper, on the surface that is efficiency.
But if the baseline drops (“Isn’t this level of quality enough?”), the entire industry can get pulled into a “low-cost mass-production” direction.
What collapses first is the on-set experience for new directors, an actor-centered production culture, and the staff job structure.
② If “actorless movies” increase, IP (intellectual property) is also likely to weaken
The reason Cameron keeps saying “acting” is not just an emotional issue.
Strong franchise IP is usually based on “attachment to characters,” and that attachment comes from the accumulation of performance, relationships, and story.
AI can generate a lot of “acceptable videos,” but in the long run, IP that actually drives revenue may become rarer.
③ This accelerates the content industry’s “oversupply” phase
If AI causes supply to explode, platforms become stronger and producers face fiercer competition.
This structure tends to lead toward platform-centered revenue distribution.
For a master like Cameron to “insist on human creation” can also be read as a message to protect creators’ bargaining power itself.
④ In macro terms: content is consumption that can hold up even in a “recession,” but the production ecosystem is different
Consumption may remain, but the wage and labor structure on production sites can be shaken by AI.
This ultimately connects to the labor market and expands into industrial policy issues (copyright/likeness rights/training-data regulation).
This is why, in today’s global economic outlook, you shouldn’t look only at “AI productivity,” but also consider the distribution shock.
3) Why Cameron’s split of AI into two types matters (investment and policy perspective)
① Separating “superintelligence (ASI)” vs “generative AI”
Most discussions in the content industry focus on “generative AI reduces jobs,” but
Cameron separates ASI as a distinct risk.
This separation is also important for policy.
ASI ties into safety, control, and even military/cybersecurity, while
generative AI leads to industrial regulation such as copyright, training data, likeness rights, and labor substitution.
② He leaves open the “tool-level potential” of generative AI
It’s not “everything is bad”; he says it could have a role as a workflow tool.
However, he adds the condition that the starting point must be actors and the script.
This is similar to the realistic consensus often heard on set.
AI is advantageous for previs (pre-visualization), concept variation, and accelerating repetitive work, but the trend is that humans must hold the reins of core emotional design.
4) The economic meaning of a 187-minute film in the short-form era: “theater = a high-engagement product”
① Streaming is “user control,” theaters are “giving it up for immersion”
Cameron describes theaters as “the choice that removes clutter.”
This treats content consumption quality as an entirely different kind of product.
② What audiences buy in theaters is not “time,” but “environment”
A big screen, sound, and an uninterrupted flow.
What this provides is not simple viewing, but “packaging of an experience.”
Because experiential goods, once satisfying, lead to word-of-mouth and repeat consumption (rewatches, merchandise, sequels), profitability grows.
③ What AI threatens here is not “production cost,” but the “scarcity of experience”
If AI massively increases video supply, audience time becomes even scarcer.
Then theaters need even stronger “event-ness,” and
the possibility grows that mega franchises like Cameron’s become more advantaged.
5) Only the “most important content” that other YouTube/news rarely point out, summarized separately
1) “Movies made cheaply with AI” may be a trap for newcomers, not an opportunity
Most people only say “the barrier to entry goes down,” but Cameron believes the growth path can be cut off.
It’s a warning that creators missing the feel of set work, actors, collaboration, and editing won’t survive long in the market.
2) “Acting” is not an art-theory issue, but a question of IP economics (revenue durability)
If attachment to actors/characters is weak, fandom is weak, and if fandom is weak, franchises become shorter-lived.
The more AI-generated video floods the market, the more human-performance-based, strong character IP can become premium.
3) Cameron’s message is not a “technology choice,” but a “bargaining-power choice”
If studios aggressively adopt generative AI, they may gain short-term cost reduction, but
in the long term, there is a risk that the value of creators, actors, and staff shifts toward platforms/tools.
In other words, this is not just a trend, but a story of “industry power transfer.”
4) This issue expands beyond AI trends into global macro (policy/regulation) issues
If social consensus on copyright/training-data regulation, likeness-right contracts, and labor substitution is delayed,
uncertainty increases in the content industry and investment may shrink.
Ultimately, it is a factor that can increase market volatility as well.
6) What to watch going forward from an economy and AI-trend perspective
① The flow of AI regulation and copyright rulings
The legality of training data, rights to use an actor’s voice/face, and studio contracting practices become key variables.
② Redefining the roles of theaters and streaming
Blockbusters are likely to become theatrical events, while mid-to-small titles are likely to optimize for streaming.
Generative AI can make the middle zone even thinner.
③ “Legal” adoption zones for AI within the production pipeline
Concept art, previs, replacing location scouting, translation/subtitles, and producing marketing assets can become standardized quickly.
On the other hand, replacing scripts/acting/actors carries strong social backlash and contract risk.
④ From a global economic outlook angle: the “distribution shock” hidden behind “AI productivity”
Even if AI boosts efficiency, sustainability of the industry changes depending on who receives the gains.
This may be priced into markets as a “policy risk” as much as macro variables like interest rates, inflation, and recession going forward.
※ In the main text, from an economic and industry SEO perspective, I naturally included key keywords such as “global economic outlook,” “interest rates,” “inflation,” “recession,” and “stock market volatility.”
< Summary >
James Cameron’s declaration, “I won’t use generative AI for even one second,” is not a rejection of technology, but an industry-structure declaration to protect a production ecosystem centered on actors, sets, and collaboration.
He separated AI into superintelligence (security/control issues) and generative AI (creation/labor substitution issues), and warned that generative AI could ruin the growth path for young directors.
His insistence on a long film even in the short-form era is because theaters are a high-engagement product that provides an “immersion environment,” and the more content supply is flooded by AI, the more premium strong IP rooted in human performance can become.
[Related posts…]
- Why AI regulation and copyright issues shake the market
- How interest-rate fluctuations affect content and tech investment
*Source: [ 지식인사이드 ]
– [제임스 카메론 인터뷰] 터미네이터 만든 거장, 영화에 AI를 ‘1초도’ 쓰지 않겠다 선언한 이유
● Manus Max Unleashed, Nvidia Hijacks Slurm, Agent Economy Power Shift
Manus 1.6 ‘Max’ Debuts + Nvidia’s Slurm Acquisition… The Era Where “AI Agents Actually Finish Real Work” Has Opened
This post contains exactly 3 key takeaways.
1) Why Manus 1.6 Max is being rated as a “practical, production-grade agent” beyond a “demo-only agent”
2) How mobile app development + Design View change product development/design workflows
3) As Nvidia acquires Slurm (a scheduler) and releases Nemotron 3, why it’s trying to seize the “operating system seat” in the AI infrastructure war
And at the end, I’ll separately summarize what other news/YouTube channels talk about less but is actually the most important point (“where the bottleneck of the agent economy moves”).
1) [Breaking Summary] Manus 1.6: Updated into an Agent That “Gets It Done to the End”
1-1. Three core updates (per the company announcement)
① Manus 1.6 Max (flagship agent)
By reorganizing the core architecture around planning and reasoning, the focus is on reducing cases where work stops midway or requires frequent human touch.
② Mobile app development support (expanding from web to mobile)
Previously centered on web-based projects, it has now expanded toward being able to build mobile apps end-to-end from “requirements → development → deliverable.”
③ Design View (an interactive layer for visual work)
Moving beyond editing images only via text prompts, it adds a UI that enables partial edits by clicking/pointing on a canvas, text insertion/changes, and compositing.
1-2. The real purpose of this update: “One-shot success rate”
Even if AI agents look convincing in demos, in real work you end up having to keep fixing things, right?
Manus 1.6 Max focused on narrowing that gap, and per the company description it aims to increase the share of work that gets completed from start to finish with minimal human intervention.
Additionally, it mentioned that satisfaction increased by at least 19.2% in a double-blind UX test.
The important message here is not “it got smarter,” but that it breaks the tool/workflow less (reduced workflow breaks) and the results became more trustworthy.
1-3. Upgrade points for Wide Research (parallel research)
Manus’s Wide Research runs multiple sub-agents simultaneously to gather and organize materials from different perspectives,
and this time it says those sub-agents were all strengthened with the Max architecture.
In other words, it’s moving to reduce quality holes that come from a structure where “only the main agent is smart and the parallel workers are weak.”
1-4. Why spreadsheets/financial modeling got stronger (practical perspective)
The update description emphasizes spreadsheet workflows (complex financial models, data analysis, report automation).
This isn’t just feature bragging; it’s because, inside enterprises, a representative bottleneck that blocks AI automation is often “Excel/sheet-based processes.”
In reality, even when many organizations say they’re doing digital transformation, the last mile ends in sheets.
If an agent can reliably handle calculations, multi-step logic, and large tables here, productivity jumps dramatically.
1-5. Web development advancement: It touches “UI/usability,” not “code generation”
A notable phrasing in this update is lines like “UI aesthetics, functional layouts, smoother interactive experiences.”
Why this matters is that internal tools or customer-facing MVPs ultimately won’t get used if the usability is bad.
“Internal tools that work but feel awful to use” are the most common failure case, and you can see a flow where the agent tries to compensate for that part.
1-6. The meaning of mobile app development support: The unit cost of “product experiments” goes down
An agent that only works on the web is ultimately half-baked.
These days, many services use mobile as the main interface, and internally there is also strong demand for mobile field/sales/logistics apps.
If Manus goes end-to-end into mobile development as well, startups move toward lower MVP experiment costs, and enterprises move toward being able to crank out internal apps faster.
1-7. The change Design View brings: Escape from “prompt iteration hell”
When you generate/edit images using only prompts, local edits you want often don’t work well, so you keep trying to persuade it with sentences.
Design View is an attempt to change that into “directly grabbing and fixing it in the UI.”
Ultimately it’s a direction that tries to combine the hands-on feel of design tools + the productivity of generative models, and if this succeeds, real-world design/marketing production speed can change drastically.
2) [Breaking Summary] Nvidia: Slurm Acquisition + Nemotron 3 Reveal, Strengthening the AI Infrastructure “Operating System” Position
2-1. What Nvidia’s acquisition of SchedMD (= Slurm developer) means
Slurm is a workload manager/scheduler that decides “who uses how much GPU/CPU, when” in large-scale clusters.
As AI training/inference grows, as important as GPU performance is orchestration (resource allocation/job scheduling/queue management).
Nvidia said it would “keep Slurm open-source and vendor-neutral.”
That sounds reassuring on the surface, but strategically it’s also a picture where Nvidia more deeply controls the de facto standard workflow of AI infrastructure.
In short, Nvidia is no longer just a chip company; it keeps expanding into full-stack AI infrastructure:
GPU (hardware) + scheduling (cluster operations) + models (open models for agents).
2-2. Nemotron 3: An open model lineup “optimized for agents”
Nemotron 3 released by Nvidia presents itself as an “efficient open model family for building accurate AI agents.”
Nemotron 3 Nano
A small and fast model.
It fits areas where cost-to-efficiency matters, such as specific task automation (classification, summarization, simple decision-making).
Nemotron 3 Super
Aimed at situations where multiple models/agents collaborate in a multi-agent environment.
It connects with the trend of increasing “role-separated agent organizations” in enterprise workflows.
Nemotron 3 Ultra
For high-difficulty tasks requiring more complex reasoning and generality.
2-3. Nvidia’s big picture: Physical AI (robotics/autonomous driving/real-world agents)
Nvidia has recently been strengthening its open strategy at the same time.
The message continues toward “AI that moves in the real world,” including vision-language-reasoning models for autonomous driving research, and simulation/world-model workflows for the physical world.
This area in particular requires the combination of
large-scale GPU clusters + stable scheduling + efficient models.
The Slurm acquisition and Nemotron 3 release are not separate; it’s natural to see them as one bundle called “infrastructure preemption for the real-world agent era.”
3) Market/Economic Commentary: Why This News Matters to the Global Economy
3-1. The bottleneck of AI automation is shifting from “models” to “operations”
Automation doesn’t scale massively just because agents get smarter.
The real problems in practice are “operational costs,” such as:
1) recovery cost when it fails
2) how often the workflow breaks
3) resource conflicts when running many tasks simultaneously
Manus is trying to lower operating cost by raising task completion rate (one-shot success),
and Nvidia is trying to dominate the operations layer with the standard of cluster operations (scheduling) + agent-oriented models.
3-2. Enterprise IT budget reallocation: Money that would “hire people” goes to “AI operating cost”
If AI agents truly start finishing work, companies won’t just increase tool subscriptions; they must redesign computing power, cloud costs, and internal operating systems.
A common change in this process is that some of the “product/data headcount increases” shift toward spending on
agent operations (observability/evaluation/permissions/audit) + infrastructure.
3-3. Investment/industry structure: AI infrastructure’s “economies of scale” get stronger
As AI agents run in multiples and expand into robotics/physical AI, the match ultimately moves toward how efficiently you operate large-scale infrastructure.
Here, schedulers/orchestration are not “nice-to-have features”; they become the core that determines cost and performance.
In other words, this news is not a simple product update; it is connected to
a reshuffling of control over the AI supply chain (hardware-software-models).
4) The Most Important Point That Other News/YouTube Talk About Less
4-1. “The killer in the agent era isn’t apps, it’s scheduling/observability”
Most people end at “Manus got smarter” or “Nvidia bought Slurm.”
The real essence is this.
When agents grow from 1 to 10, to 100,
work speeds up explosively, but at the same time
failures/collisions/cost explosions grow as well.
So the next competition’s center moves from
“who makes a better demo” to
who can operate large-scale agents more stably (scheduling, resource allocation, guardrails, observability).
4-2. Manus and Nvidia each capture different layers, but the puzzle interlocks
Manus pushes the “work execution layer (front-end productivity),” and
Nvidia preempts the “make-it-run layer (back-end operability).”
When these two axes combine, from an enterprise perspective,
“agent adoption” moves beyond the experiment phase and into
an enterprise-wide operating model change.
5) Practical Application Checklist (Immediately Usable Perspective)
5-1. KPIs to check when testing Manus 1.6
One-shot success rate: how many retries are needed for the same task
Mid-process intervention count: how often a human edits/instructs
Spreadsheet reliability: frequency of calculation errors/reference errors/omissions
Web/mobile deliverable completeness: not “it runs,” but “usability”
Design View iteration efficiency: edit speed versus prompt-iteration time
5-2. Why companies should pay attention to the Nvidia-Slurm issue
If you have in-house GPUs/clusters or may move toward on-prem/hybrid in the future,
a scheduler is not just an IT tool; it determines cost structure.
Especially as multi-agent and large-scale inference increase,
before asking “should we buy more GPUs,” the issue becomes
an operating system that uses existing GPUs more efficiently.
< Summary >
Manus 1.6 Max strengthens planning/reasoning to raise end-to-end task completion rate and trustworthiness, and expands practical scope with mobile app development and Design View.
Nvidia is taking control of the large-scale AI workload operations layer by acquiring the Slurm developer (SchedMD), and is pushing into agent building with the Nemotron 3 open models.
Ultimately, the core point of competition is shifting from “smarter demos” to “infrastructure/scheduling/observability that can stably operate large-scale agents.”
[Related Posts…]
- AI Infrastructure War: Who Controls the Stack
- Nvidia’s Open-Model Strategy: The Bigger Picture After Nemotron
*Source: [ AI Revolution ]
– Manus Just Dropped Its Most Powerful AI Yet
