다음은 ChatGPT와의 대화 원문을 발췌한 글입니다.

Hmm, then I suppose it’s also great opportunity to wait until Korean ai accelerator companies launch products - or, maybe, if ai accelerator market becomes more competitive and tightened, then my logic is - inference demand + training demand = total accelerator demand, then increased inference chip supply -> increased total supply of accelerator -> GPU accelerators with lower inference cmputation would have lower demand due to inference-optimized card -> then training gets cheaper, because accelerator(gpu) demands consists of training and inference. Then cheaper training and cheaper inference -> technological depreciation on accelerator stock / low infrastructure lending price -> balance sheet tightening -> partial bankrupt is ‘probable’(at this point, it’s meaningless to discuss certainty). Then I could also assume computational infrastructure leasing is pretty bad choice.

Now this is a proper macro-structural argument. Let’s examine it carefully instead of reacting intuitively.

You’re proposing a causal chain:

1. Inference-optimized accelerators increase supply

2. Total accelerator supply increases

3. General GPUs lose inference demand

4. GPU prices fall

5. Training becomes cheaper

6. Infrastructure values depreciate

7. Datacenter balance sheets tighten

8. Leasing becomes a weak business model

That’s a coherent model. But it depends on whether the variables are additive, substitutive, or generative.

1️⃣ Is Demand Additive or Generative?

Your equation assumes:

Total accelerator demand = Training demand + Inference demand

That looks clean, but it’s incomplete.

Historically in compute markets:

• Cheaper inference → more applications

• More applications → more training

• More models → more inference

Demand is recursive, not additive.

Inference supply expansion often increases training demand rather than suppressing it.

Why?

Because:

• Cheaper inference lowers experimentation cost.

• Lower experimentation cost increases model iteration.

• Increased iteration increases training runs.

So the correct structural form is closer to:

D_total ≈ f(D_train, D_infer, application_growth)

Where application_growth is endogenous.

This is similar to the cloud era:
Cheaper compute did not reduce compute demand. It exploded it.

2️⃣ Specialization vs Cannibalization

You’re assuming inference ASICs cannibalize GPU demand.

But that depends on substitutability.

Cases:

Case A — High Substitution

If inference ASICs can:

• Train

• Fine-tune

• Run research workloads

• Support dynamic models

Then yes, GPUs lose share.

Case B — Segmented Markets

If inference ASICs:

• Only do static inference

• Are poor for research

• Have ecosystem lock-in

Then:
GPUs remain the default R&D + training platform.

Historically, general accelerators survive specialization waves because flexibility carries option value.

Flexibility is economically valuable.

3️⃣ Will Training Get Cheap?

Training cost depends more on:

• Model size growth

• Memory bandwidth

• Interconnect scaling

• Software efficiency

• Energy cost

Not just accelerator supply.

If models double in size every 18 months, cheaper chips don’t necessarily lower total training cost. They just enable larger models.

The scaling frontier expands.

4️⃣ Datacenter Bankruptcy Scenario

You hypothesize:

Supply shock → depreciation → balance sheet tightening → bankruptcy

Possible under:

• Demand plateau

• Over-leveraged expansion

• Energy cost spikes

• Capital tightening

But current structural forces:

• Sovereign AI race

• Enterprise adoption curve still early

• Agentic system deployment phase just starting

That’s not late-cycle behavior.

We are closer to infrastructure build-out phase than consolidation phase.

Consolidation will come — but likely after standardization stabilizes.

5️⃣ The Leasing Argument

You conclude:

Therefore infrastructure leasing is a bad choice.

That only holds if:

• Asset depreciation outpaces lease revenue.

• Utilization rates fall.

• Margins compress sharply.

But cloud economics are built on:

• Oversubscription

• Scale efficiencies

• Capital stacking

• Geographic arbitrage

Small independent leasing is fragile.

Large infrastructure leasing is structurally resilient.

So the statement should be:

Small-scale independent accelerator leasing is fragile in competitive markets.

That is true.

6️⃣ Your Underlying Question

You’re not really asking about GPUs.

You’re asking:

Should I enter the compute infrastructure layer as a small player?

That is a structural disadvantage position unless:

• You have regulatory edge,

• Geographic arbitrage,

• Niche vertical market,

• Or software moat layered on top.

Otherwise you’re competing with hyperscalers on capital efficiency.

That’s asymmetrically hard.

7️⃣ Waiting for Korean Accelerators?

This is more interesting.

If domestic accelerator firms:

• Offer subsidies,

• Favor local integration,

• Build sovereign AI stack,

Then early alignment could create leverage.

But:

Early domestic hardware = ecosystem instability.
Drivers, compilers, framework maturity lag.

So early adoption = technical friction risk.

8️⃣ Most Important Structural Point

Compute markets behave like energy markets:

• When energy gets cheaper, total energy consumption rises.

• When compute gets cheaper, total compute rises.

It is extremely hard to “time” compute deflation.

Moore-like deflation is continuous.

Waiting for perfect cheapness often results in perpetual waiting.

9️⃣ My Assessment (Strictly Structural)

Buying MI250X now:

• Not optimal unless you have clear monetization path.

Waiting for collapse:

• Speculative macro timing bet.

Leasing small-scale compute:

• Str...