Monday, August 3, 2026
Why some people say that NVidia is a dead man walking? (ChatGPT, 2026-8-3)
The phrase “Nvidia is a dead man walking” is not a consensus judgment. It is a deliberately provocative version of a plausible bear thesis:
Nvidia may remain enormously profitable, while the architecture that produced its extraordinary monopoly-like profits is already beginning to change.
One recent use of the exact phrase argues that, if Apple’s device-centred approach prevails, hyperscale cloud AI could eventually resemble the mainframe: still essential for frontier training and large enterprises, but no longer the place where most everyday computing happens.
The bear case
1. Nvidia’s best customers are trying to escape Nvidia
Microsoft, Google, Amazon, Meta and other hyperscalers buy huge numbers of Nvidia systems—but Nvidia’s gross margins are so high that every customer has a powerful incentive to build its own chips.
The logic is simple:
When your supplier earns a 70% gross margin, your supplier’s profit becomes your engineering opportunity.
Custom ASICs do not have to defeat Nvidia at every workload. They need only perform a stable, high-volume task—such as inference for one company’s own models—more cheaply and efficiently.
Nvidia itself acknowledges the danger that customers may develop internal solutions.
2. AI may migrate from training to inference—and from cloud to edge
Nvidia’s rise was built initially on giant training clusters. But mature AI markets may spend increasingly on:
- inference rather than training;
- small specialized models rather than one enormous model;
- phones, PCs, cars and robots rather than remote data centres;
- highly optimized chips rather than general-purpose GPUs.
A GPU is exceptionally flexible. But flexibility is expensive. Once a workload becomes predictable, a specialized chip may be cheaper, cooler and faster.
This is the Apple-centred version of the “dead man walking” thesis:
Nvidia owns the AI factory, but Apple may own the place where human beings actually encounter AI.
I would not say cloud AI disappears. Rather, intelligence could become hierarchical:
Device AI
↓ when necessary
Private/local cloud
↓ when necessary
Hyperscale frontier compute
Nvidia remains dominant at the bottom of that pyramid, but much of the volume and value may move upward toward devices.
3. Efficient models could weaken the “more compute forever” assumption
Low-cost open models—especially increasingly capable Chinese models—raise an uncomfortable question:
What happens if intelligence improves faster than demand for computation?
Recent market anxiety has specifically concerned whether efficient models such as Kimi K3 imply that future workloads may require less advanced compute than investors expected.
This does not necessarily mean total compute declines. Efficiency often produces greater use—the Jevons paradox. But it could change who captures the profit:
- cheaper inference,
- lower-cost chips,
- greater use of commodity hardware,
- less willingness to pay Nvidia’s premium.
4. Its revenue is unusually concentrated
In Nvidia’s fiscal 2026, one direct customer represented 22% of total revenue and another represented 14%. Nvidia also estimates that some indirect customers individually account for at least 10%.
That is extraordinary strength—but also dependency.
A few companies are simultaneously:
- Nvidia’s largest customers;
- its prospective competitors;
- financially capable of spending tens of billions to replace it.
This resembles a king whose principal vassals are quietly building their own armies.
5. The AI capital-expenditure cycle may be partly self-reinforcing
An especially disturbing recent concern is that Nvidia may increasingly help finance the ecosystem that buys Nvidia equipment. Reuters reported investor concern over a possible roughly $250 billion financial backstop associated with an OpenAI data-centre project—raising the question of whether Nvidia might be indirectly financing its own customers.
This does not prove fraudulent or circular revenue. But bears see a potentially unstable circuit:
Nvidia invests in AI company
↓
AI company raises/builds data centre
↓
Data centre buys Nvidia systems
↓
Nvidia reports greater demand
↓
Higher valuation enables further investment
The danger is not that the circuit is imaginary. The data centres and chips are real. The danger is that future demand may have been pulled forward and made dependent on continued financing.
6. China is becoming both a lost market and a future competitor
US export controls have already damaged Nvidia’s position in China. Nvidia’s own annual report says it is presently unable to offer a competitive China data-centre product acceptable to both Washington and Beijing. It also warns that restrictions encourage customers to develop internal alternatives and foreign governments to favour non-US suppliers.
In fiscal 2026, export restrictions contributed to a $4.5 billion H20 inventory and purchase-obligation charge.
The strategic paradox is:
America restricts Nvidia to slow China, but those restrictions also create the protected market in which Huawei and other Chinese accelerators can mature.
The US tightened enforcement again in May 2026 against advanced-chip shipments to overseas subsidiaries of Chinese companies.
7. The valuation may require near-perfection
A great company is not always a great investment at every price.
Nvidia’s fiscal 2026 performance was still astonishing:
- revenue: $215.9 billion, up 65%;
- net income: $120.1 billion, up 65%;
- data-centre revenue: $193.7 billion;
- gross margin: 71.1%.
Those are not the financial statements of a dying company. The bear argument is instead that the market may be valuing a temporary period of scarcity, pricing power and customer urgency as though it were permanent.
Why I think “dead man walking” is exaggerated
Nvidia does not merely sell chips. It possesses a stack:
GPU architecture
+ CUDA
+ libraries
+ networking
+ systems
+ developer tools
+ deployment software
+ trained workforce
Nvidia describes itself as a data-centre-scale AI infrastructure company, with CUDA plus hundreds of libraries, frameworks and SDKs.
A competitor can design a good accelerator. Replacing the whole operating environment is much harder.
Moreover, custom chips often complement Nvidia rather than replace it:
- Nvidia for frontier training and rapidly changing workloads;
- ASICs for mature, repetitive inference;
- edge chips for local execution.
Therefore the likely future is not Nvidia or custom silicon. It is a heterogeneous compute ecology.
My judgment
Nvidia is not a dead man walking.
It is more accurately:
a victorious empire standing at the moment when victory causes every tributary state to seek independence.
Its immediate business remains immensely strong. Its longer-term danger is that the GPU becomes only one layer in a distributed AI world composed of custom accelerators, edge devices, local models and sovereign computing systems.
So the bear thesis has the wrong verb but perhaps the right direction:
- Nvidia is not dying.
- Nvidia’s period of almost uncontested rent extraction may eventually be dying.
- The company must transform from the indispensable maker of scarce GPUs into the operating system and infrastructure of heterogeneous intelligence.
And that explains Jensen Huang’s movement toward networking, complete AI factories, robotics, automotive and physical AI. He understands that merely remaining the GPU company would eventually be fatal.
The deepest question is therefore not whether Nvidia chips will remain necessary. They almost certainly will.
It is:
Will Nvidia remain the place where most of the value accumulates after intelligence leaves the data centre and enters every object?
What’s loop engineering, and graph engineering? （ChatGPT, 2026-8-3)
These are two of the newest terms in the agentic AI world. They describe a shift in what AI developers engineer.
The progression looks something like this:
Prompt engineering → Context engineering → Loop engineering → Graph engineering
Each step moves farther away from interacting with a single LLM, and closer to designing an autonomous system.
1. Prompt engineering (2023–2024)
You write one prompt.
Human
│
Prompt
│
LLM
│
Answer
The human is responsible for every turn.
2. Loop engineering (2026)
Loop engineering asks:
How should an AI repeatedly think, act, verify, and improve until it reaches a goal?
Instead of prompting repeatedly,
Prompt
↓
Think
↓
Act
↓
Check result
↓
Need improvement?
↓
Think again
↓
...
Stop
the engineer designs the outer control loop.
Typical components include:
- goal
- memory
- tool use
- verification
- retry policy
- stopping criteria
- human approval when needed
The important shift is
You no longer prompt the model.
You engineer the process that prompts the model.
This has become central to coding agents such as Claude Code, Codex, and similar autonomous software agents.
Example
Instead of saying
“Fix this Python bug.”
you build a loop:
Read code
↓
Write fix
↓
Run tests
↓
Tests fail?
↓
Read error
↓
Write another fix
↓
Run tests
↓
...
↓
All tests pass
↓
Stop
The human may never intervene after pressing Start.
3. Graph engineering
Loop engineering works well for one agent.
Graph engineering asks a different question:
How should many specialized agents cooperate?
Instead of one loop,
you have an organization.
Planner
│
┌─────────┴─────────┐
│ │
Research Coding
│ │
└─────────┬─────────┘
│
Reviewer
│
Human check
The engineer now designs
- nodes
- edges
- routing
- shared state
- permissions
- parallel execution
- recovery
- checkpoints
rather than just one reasoning loop. Most descriptions emphasize that a graph is an explicit workflow connecting multiple loops or specialized workers.
A simple comparison
|
Loop engineering |
Graph engineering |
|
One agent |
Multiple agents |
|
One feedback loop |
Network of workflows |
|
Sequential |
Sequential + parallel |
|
Single memory |
Shared state across agents |
|
Optimize one worker |
Optimize an organization |
Why “graph”?
Because the workflow literally becomes a directed graph.
Imagine
Research
│
▼
Summarize
│
▼
Fact Check
│
┌────┴─────┐
│ │
Pass Fail
│ │
▼ ▼
Publish Rewrite
The graph specifies
- where information flows,
- who receives it,
- which branches execute,
- how failures recover,
- when humans intervene.
This resembles workflow engines and distributed systems, but now many of the nodes are LLM-powered agents rather than deterministic software.
Why this matters
The bottleneck is moving.
Three years ago:
Better prompts → better results.
Today:
Better orchestration → better agents.
In other words,
the scarce skill is becoming
system design rather than prompt writing.
Many people even describe graph engineering as “programming an AI organization instead of programming one AI.”
A philosophical observation
Knowing your interests in Deleuze, assemblages, and self psychology, I think there is an interesting parallel.
Prompt engineering assumes:
One mind solving one problem.
Loop engineering assumes:
One mind becoming reflexive, continuously modifying itself through action and feedback.
Graph engineering assumes:
Intelligence is distributed across an assemblage rather than located in one mind.
That is remarkably close to Deleuze and Guattari’s language of assemblages, rhizomes, and distributed agency. The “thinking” no longer belongs to a single agent but emerges from the coordinated interactions among specialized nodes.
Ironically, AI engineering seems to be rediscovering—through software architecture—ideas that philosophers like Deleuze, Bruno Latour, and Karen Barad have been developing for decades: intelligence is less like a solitary brain and more like a dynamic network of relations.