Value in Hardware and Execution | Generated by AI
Question: As AI becomes abundant and open-source models commoditize intelligence, what kind of value will still matter? Is hardware + software a better position?
Answer:
Yes, your intuition is pointing toward one of the biggest shifts in the AI economy.
The pattern is similar to previous technology waves:
- Internet → information became cheap → distribution, trust, community became valuable.
- Cloud → servers became cheap → products, data, workflows became valuable.
- Open-source software → code became cheap → deployment, integration, support became valuable.
- AI → intelligence generation becomes cheap → execution, ownership, physical systems, proprietary data, and trust become valuable.
A simple way to think:
Raw intelligence
↓
cheap
↓
What remains scarce?
↓
+ Data
+ Distribution
+ Hardware
+ Real-world integration
+ Brand/trust
+ Workflow ownership
+ Unique feedback loops
1. Open-source AI will reduce “model advantage”
Today:
OpenAI / Anthropic / Google
|
| frontier models
|
↓
developers build apps
But over time:
Qwen
Llama
DeepSeek
Mistral
etc.
↓
everyone has similar intelligence
The model itself becomes like Linux:
Nobody pays you because you know Linux exists.
People pay because you can build:
- Kubernetes platforms
- cloud services
- enterprise solutions
- devices
- products
The model becomes infrastructure.
2. Closed-source creates temporary gaps, but not permanent moats
Your thought:
闭源造成一个 GAP 信息差，会是一个比较舒服的
Correct in the short term.
Examples:
- knowing GPT-5 capabilities earlier
- knowing unpublished APIs
- having private datasets
- having internal engineering experience
This creates an advantage.
But information gaps disappear.
A stronger moat is:
You know something
+
You built something
+
Users depend on it
+
Data feedback improves it
That is much harder to copy.
3. Taalas is interesting because it attacks inference economics
Taalas is an example of a hardware/software co-design approach.
Traditional:
LLM weights
|
v
GPU
|
v
CUDA kernels
|
v
Inference
GPU is general purpose.
Taalas idea:
LLM weights
|
v
custom silicon
|
v
faster cheaper inference
Basically:
“Can we turn the model into hardware?”
Similar ideas existed before:
- Google TPU
- Cerebras wafer-scale engines
- Graphcore IPU
- Tesla Dojo
The key insight:
The future AI stack may not only be:
software eats hardware
but:
AI models reshape hardware
4. Hardware has a psychological advantage: people pay for objects
You mentioned:
买一个东西这种人们愿意给钱
Yes.
Humans are comfortable paying for:
- iPhone
- Tesla
- GPU
- robot
- smart glasses
- home AI server
Because the value is visible.
Compare:
$999 AI device
versus
$20/month AI subscription
Many consumers understand the first more easily.
Physical products create:
- ownership
- identity
- status
- trust
5. The strongest future position: AI + physical world
The future may look like:
AI model
|
----------------------
| |
Software Hardware
| |
digital workers robots/devices
| |
-------- humans -----
Examples:
- AI coding agent + private server
- AI security appliance
- AI education robot
- AI factory controller
- AI medical assistant
- AI home assistant
6. For an individual engineer, where is the opportunity?
The dangerous position:
"I know how to call GPT API"
Many people can do this.
Better:
"I understand transformers"
"I trained models"
"I optimize inference"
"I build vLLM-like systems"
"I deploy AI on hardware"
Even better:
I own a small AI system used by real people.
The progression:
User of AI
↓
Developer with AI
↓
AI infrastructure engineer
↓
AI product builder
↓
AI company
Your recent direction (nanoGPT → nano-vLLM → CUDA → GPU → inference optimization) is actually aligned with this deeper layer.
Many developers will stay at:
prompt → API → app
The people who understand:
attention
KV cache
CUDA kernel
GPU memory
quantization
distributed inference
ASIC architecture
are closer to the “picks and shovels” layer.
The key is not only knowing technology. The final step is:
deep technology
+
real user pain
+
distribution
=
economic value
The future probably has millions of small AI companies: one person or a small team operating AI systems that previously required hundreds of employees. The winners are not necessarily those with the biggest models; they are those who combine models with unique workflows, hardware, data, and users.
Generated by AI. Curating and sharing still takes effort.
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