OpenAI Partners with Samsung: A Deep Technical Analysis of Next-Generation AI Chips
OpenAI Partners with Samsung: A Deep Technical Analysis of Next-Generation AI Chips
1. Introduction: The Strategic Pivot from GPU Dependency to Custom Silicon
On September 9, 2026, at a press conference in Seoul, OpenAI Korea General Manager Harrison Kim made a landmark announcement: OpenAI and Samsung Electronics have achieved “the greatest progress and received the broadest recognition” in next-generation chip development and joint production. This declaration signals a new phase in the global AI arms race—the compute bottleneck is now propagating from the software stack all the way down to the silicon physics layer.
OpenAI had already launched its first custom AI chip, Jalapeño (co-designed with Broadcom), in June 2026, fabricated on TSMC’s 3nm process. The Samsung partnership means OpenAI is now building a dual-sourcing foundry strategy to hedge against geopolitical and supply chain risks, while leveraging Samsung’s breakthroughs in 2nm GAA (Gate-All-Around) process technology and advanced packaging for its next-generation AI silicon.
This article dissects the technical implications of this partnership across four dimensions: chip architecture, process technology, advanced packaging, and strategic competition.
2. AI Chip Architecture Evolution: From GPU General Computing to Custom ASICs
2.1 The Limitations of GPU General Computing
Today’s dominant AI compute engines—NVIDIA’s H100 and B200 series—are fundamentally general-purpose parallel processors. Their design philosophy is jack-of-all-trades: capable of matrix multiplication, graphics rendering, and scientific computing. This versatility comes at a tremendous cost in silicon area and power efficiency.
┌──────────────────────────────────────────────────────┐
│ NVIDIA GPU (H100) │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ SM Core │ │ SM Core │ │ SM Core │ ... │
│ │ (128) │ │ (128) │ │ (128) │ │
│ ├──────────┤ ├──────────┤ ├──────────┤ │
│ │ Tensor │ │ Tensor │ │ Tensor │ │
│ │ Core │ │ Core │ │ Core │ │
│ ├──────────┤ ├──────────┤ ├──────────┤ │
│ │ L1/SMEM │ │ L1/SMEM │ │ L1/SMEM │ │
│ └──────────┘ └──────────┘ └──────────┘ │
│ │
│ ┌──────────────────────────────────────────────┐ │
│ │ L2 Cache (50MB) │ │
│ ├──────────────────────────────────────────────┤ │
│ │ HBM3 Memory Controller │ │
│ └──────────────────────────────────────────────┘ │
│ │
│ ⚠ Key Pain Point: Matrix compute occupies ~40% die │
│ Remaining 60% goes to general scheduling, cache │
│ coherence, and graphics pipeline │
└──────────────────────────────────────────────────────────┘
Critical problem: In Transformer inference scenarios, GPU SM (Streaming Multiprocessor) utilization hovers at only 20-40%. A massive number of transistors are dedicated to graphics rendering, context switching, and general scheduling logic that AI inference doesn’t need.
2.2 ASIC Custom Chip: The Jalapeño Architecture
OpenAI’s Jalapeño chip adopts a Domain-Specific Architecture (DSA) design philosophy, providing hardware-level optimization specifically for Transformer models.
┌──────────────────────────────────────────────────────┐
│ OpenAI Jalapeño ASIC (Conceptual Arch) │
│ │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Attention │ │ FFN (MLP) │ │
│ │ Engine │ │ Engine │ │
│ │ (QKV Calc) │ │ (GeMM/SwiGLU)│ │
│ ├──────────────┤ ├──────────────┤ │
│ │ Tile-Based │ │ Sparsity │ │
│ │ Scheduler │ │ Accelerator│ │
│ └──────┬───────┘ └──────┬───────┘ │
│ │ │ │
│ └──────┬───────────┘ │
│ ▼ │
│ ┌──────────────────────────────┐ │
│ │ On-Chip SRAM Buffer │ │
│ │ (192MB, HBM-agnostic) │ │
│ ├──────────────────────────────┤ │
│ │ Activation & KV-Cache │ │
│ │ Compression Engine (FP8/4) │ │
│ ├──────────────────────────────┤ │
│ │ Direct P2P Interconnect │ │
│ └──────────────────────────────┘ │
│ │
│ ✅ Utilization advantage: Matrix compute ~78% die │
│ Unnecessary graphics/general logic removed │
└──────────────────────────────────────────────────────────┘
Key Architectural Innovations:
Specialized Attention Engine: Implements Multi-Head Attention’s QKV projection, Score computation, Softmax, and weighted summation via hardwired pipelines, eliminating intermediate DRAM round trips.
Native Sparsity Support in FFN Engine: Leverages activation sparsity in Mixture-of-Experts (MoE) models by skipping expert computations gated off by the routing network, achieving 3-5× theoretical efficiency gains.
On-Chip KV-Cache Compression: Performs FP8/FP4 quantization compression of KV-Cache directly on-die for long-context inference, alleviating HBM bandwidth bottlenecks.
2.3 Quantitative Analysis: GPU vs. ASIC Performance
Below is a simplified inference latency simulation comparing GPU to ASIC performance:
package main
import (
"fmt"
)
type ChipConfig struct {
Name string
ComputeTFLOPS float64
MemoryBW float64
UtilRate float64
AttentionEff float64
SparseSupport bool
OnChipKVCache bool
}
type ModelConfig struct {
NumParams float64
NumLayers int
HiddenDim int
SeqLen int
SparseRatio float64
BatchSize int
}
func simulateLayerLatency(chip ChipConfig, model ModelConfig) float64 {
flopsPerTokenQKV := 5.0 * float64(model.HiddenDim) * float64(model.HiddenDim)
effectiveFLOPS := chip.ComputeTFLOPS * 1e12 * chip.UtilRate
if chip.AttentionEff > 0 {
effectiveFLOPS *= chip.AttentionEff
}
computeLat := (flopsPerTokenQKV * 1e6) / effectiveFLOPS
memLat := (4.0 * float64(model.HiddenDim) * 4 * 1e6) / (chip.MemoryBW * 1e9)
ffnFlops := 8.0 * float64(model.HiddenDim) * float64(model.HiddenDim)
if chip.SparseSupport && model.SparseRatio > 0 {
ffnFlops *= (1.0 - model.SparseRatio)
}
ffnLat := (ffnFlops * 1e6) / effectiveFLOPS
if chip.OnChipKVCache {
memLat *= 0.35
}
return computeLat + memLat + ffnLat
}
func main() {
gpu := ChipConfig{"NVIDIA H100", 1979, 3350, 0.25, 1.0, false, false}
asic := ChipConfig{"Jalapeño (est.)", 1200, 2800, 0.72, 1.8, true, true}
model := ModelConfig{70, 80, 8192, 32768, 0.85, 1}
gpuLat := simulateLayerLatency(gpu, model)
asicLat := simulateLayerLatency(asic, model)
fmt.Printf("Model: %.0fB params, %d layers, SeqLen=%d\n",
model.NumParams, model.NumLayers, model.SeqLen)
fmt.Printf("GPU (H100) per-layer latency: %.2f μs\n", gpuLat)
fmt.Printf("Jalapeño (ASIC) per-layer latency: %.2f μs\n", asicLat)
fmt.Printf("Speedup: %.2f×\n", gpuLat/asicLat)
totalTokens := 1000
fmt.Printf("\nTotal latency for %d tokens:\n", totalTokens)
fmt.Printf(" H100: %.2f ms\n", gpuLat*float64(totalTokens)/1000)
fmt.Printf(" Jalapeño: %.2f ms\n", asicLat*float64(totalTokens)/1000)
}
Key Insight: Even with lower absolute compute peak performance, Jalapeño achieves 2-3× end-to-end inference acceleration through higher utilization, specialized Attention Engine acceleration, and native sparse computation support.
3. Samsung 2nm GAA Process: The Technology Race with TSMC
3.1 FinFET vs. GAA: A Generational Leap in Transistor Architecture
TSMC’s 3nm (N3 series) uses FinFET architecture, while Samsung’s 2nm (SF2) adopts GAA—the critical transition from 3-sided to 4-sided gate control.
┌─────────────────────────────────────────────────────────┐
│ FinFET (TSMC 3nm) vs GAA FET (Samsung 2nm)│
│ │
│ ┌──────┐ ┌──────────────────┐ │
│ │ Gate │ │ Gate │ │
│ ├──────┤ ├──────────────────┤ │
│ │Fin │ │ ┌──┐ ┌──┐ ┌──┐ │ │
│ │ │ │ │N │ │N │ │N │ │ │
│ │Src─D │ │ │a │ │a │ │a │ │ │
│ │rain │ │ │no│ │no│ │no│ │ │
│ │ │ │ │ │ │ │ │ │ │ │
│ └──────┘ │ └──┘ └──┘ └──┘ │ │
│ │ Nanosheets ×3 │ │
│ └──────────────────┘ │
│ │
│ Channel Control: 3-sides Channel Control: 4-sides │
│ Leakage: baseline Leakage: -30~40% │
│ Drive Current: baseline Drive Current: +15~25% │
│ Design Maturity: mature Design Maturity: medium │
│ Yield: >90% (N3E) Yield: ramping (~70%) │
└──────────────────────────────────────────────────────────┘
3.2 Samsung SF2 Key Technical Parameters
Samsung’s SF2 (2nm GAA) provides significant improvements over the previous SF3 (3nm GAA):
┌──────────────────────────────────────────────────────────┐
│ Samsung GAA Process Roadmap │
│ │
│ Parameter SF3(3nm) SF2(2nm) SF2P │
│ ─────────────────────────────────────────────────────── │
│ Transistor Type GAA FET GAA FET GAA FET │
│ Nanosheet Count 3 3 3-4 │
│ Contact Pitch(CPP) 48nm 42nm 38nm │
│ Min Metal Pitch 24nm 21nm 18nm │
│ Perf gain(vs prev) +12% +15% +12% │
│ Power Reduction -23% -25% -22% │
│ Logic Area Scale -10% -17% -12% │
│ Mass Production 2023 2026(H2) 2027 │
│ │
│ Key Innovation: SF2 uses 2nd-gen GAA with optimized │
│ nanosheet spacing and backside power delivery (BSPDN) │
└──────────────────────────────────────────────────────────┘
AI-Specific Advantages of SF2:
Lower leakage: The 4-sided GAA gate provides superior subthreshold channel control, critical for AI accelerators where thousands of compute units run in parallel. Cumulative leakage in large dies can be crippling.
Higher drive current: Samsung SF2 supports nanosheet width tuning from 12nm to 45nm, allowing designers to optimize SRAM (narrow sheets) and logic (wide sheets) independently within the same chip.
Backside Power Delivery Network (BSPDN): Moving power rails to the wafer backside frees up front-side signal routing, reducing crosstalk—a major advantage for high-frequency AI chips operating above 2GHz.
3.3 TSMC’s Counter: The N2 Process
TSMC is not standing still. Its N2 (2nm) process, expected in late 2026, also adopts GAA (which TSMC calls Nanosheet):
class ProcessNode:
def __init__(self, name, vendor, node_nm, transistor_type,
density_mtr_mm2, perf_gain, power_reduction,
mass_prod, ai_boost):
self.name = name
self.vendor = vendor
self.node_nm = node_nm
self.transistor_type = transistor_type
self.density = density_mtr_mm2
self.perf_gain = perf_gain
self.power_red = power_reduction
self.mass_prod = mass_prod
self.ai_boost = ai_boost
def ai_score(self, area_mm2=800):
transistor_count = self.density * area_mm2
perf = 1 + self.perf_gain / 100.0
power = 1 + self.power_red / 100.0
score = (transistor_count / 1e9) * perf * power
yield_risk = 0.85 if "2026" in self.mass_prod else 0.95
return score * yield_risk * self.ai_boost
nodes = [
ProcessNode("N3E", "TSMC", 3, "FinFET", 215, 15, 25, "2024H1", 1.0),
ProcessNode("N2", "TSMC", 2, "GAA", 260, 18, 30, "2026H2", 1.0),
ProcessNode("SF3", "Samsung", 3, "GAA", 190, 12, 23, "2023H2", 1.0),
ProcessNode("SF2", "Samsung", 2, "GAA", 235, 15, 25, "2026H2", 1.05),
ProcessNode("18A", "Intel", 1.8, "RibbonFET", 240, 20, 30, "2026H1", 0.95),
]
print(f"{'Process':<18} {'Density':<14} {'AI Score':<12} {'Risk':<10}")
print("=" * 55)
for n in nodes:
s = n.ai_score()
r = "Low" if n.vendor == "TSMC" else ("Med" if n.vendor == "Samsung" else "High")
print(f"{n.vendor+' '+n.name:<18} {n.density:<14} {s:<12.2f} {r:<10}")
3.4 Why Does OpenAI Need Samsung?
Three layers of reasoning explain the strategic choice:
Layer 1: Capacity Insurance. TSMC’s advanced node capacity is heavily booked by Apple, NVIDIA, AMD, and Broadcom. OpenAI’s compute demand grows exponentially—relying on a single foundry is untenable.
Layer 2: Technology Diversity. Samsung started GAA mass production one generation ahead of TSMC (SF3 in 2023 vs. N2 in late 2026), accumulating more real-world GAA design experience. For custom AI chips requiring extensive design-technology co-optimization (DTCO), a more mature GAA ecosystem means lower tape-out risk and faster iteration cycles.
Layer 3: Packaging Synergy. Samsung’s advanced packaging portfolio (I-Cube, X-Cube) aligns perfectly with OpenAI’s need for high-bandwidth, low-latency interconnects.
4. Advanced Packaging: The “Second Battlefield” for AI Chip Performance
As transistor scaling approaches physical limits, advanced packaging has become the critical lever for AI chip performance. Samsung’s packaging capabilities were a decisive factor in OpenAI’s choice.
4.1 2.5D Packaging: Interposer Interconnects
┌──────────────────────────────────────────────────────────┐
│ 2.5D Packaging (Samsung I-Cube) │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ │ │ │ │ │ │
│ │ AI Die │ │ AI Die │ │ AI Die │ │
│ │ (Logic) │ │ (Logic) │ │ (Logic) │ │
│ │ │ │ │ │ │ │
│ └────┬─────┘ └────┬─────┘ └────┬─────┘ │
│ │ │ │ │
│ └──────────────┼──────────────┘ │
│ │ │
│ ┌───────────────────┼───────────────────┐ │
│ │ Silicon Interposer │ │
│ │ ┌────────────────────────────────┐ │ │
│ │ │ TSV Array (10μm pitch) │ │ │
│ │ │ 100K+ connections │ │ │
│ │ └────────────────────────────────┘ │ │
│ └────────────────────────────────────────┘ │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ HBM3 │ │ HBM3 │ │ HBM3 │ │
│ │ (8Hi) │ │ (8Hi) │ │ (8Hi) │ │
│ │ 1.2TB/s │ │ 1.2TB/s │ │ 1.2TB/s │ │
│ └──────────┘ └──────────┘ └──────────┘ │
│ │
│ AI Die ↔ Interposer BW: ~2Tbps/mm edge │
│ Total HBM BW: 3.6TB/s (3 stacks) │
│ Substrate: 110×110mm │
└──────────────────────────────────────────────────────────┘
Technical Value: 2.5D packaging inserts a silicon interposer between compute dies and HBM, achieving interconnect density orders of magnitude beyond traditional PCB routing. Samsung’s I-Cube S (Silicon) and I-Cube E (Embedded) solutions support micro-bump interconnects at pitches as small as 10μm.
4.2 3D Packaging: The Vertical Stacking Revolution
┌──────────────────────────────────────────────────────────┐
│ 3D Packaging (Samsung X-Cube) │
│ │
│ ┌────────────────────────────────────┐ │
│ │ Layer 4: SRAM Cache (64MB) │ ← micro-bump │
│ ├────────────────────────────────────┤ │
│ │ Layer 3: Compute Tile (FP8) │ ← Hybrid │
│ ├────────────────────────────────────┤ Bonding │
│ │ Layer 2: Compute Tile (INT8) │ (Cu-Cu) │
│ ├────────────────────────────────────┤ │
│ │ Layer 1: Controller + I/O │ │
│ └────────────┬───────────────────────┘ │
│ │ │
│ ┌────────────┴───────────────────────┐ │
│ │ TSV Array │ │
│ │ Density: ~1M TSV/cm² @ 1μm pitch │ │
│ │ Latency: <10ps/layer │ │
│ └────────────────────────────────────┘ │
│ │
│ ⚡ X-Cube Advantages: │
│ • Memory wall breakthrough: SRAM-on-Logic, -60% latency │
│ • BW density: >10Tbps per layer interconnect │
│ • Power savings: >50% reduction in off-die data moves │
│ • Footprint: 40% reduction vs. 2.5D approach │
└──────────────────────────────────────────────────────────┘
4.3 Packaging Leverage on AI Performance
def analyze_packaging_impact():
class Package:
def __init__(self, name, dram_bw, dram_lat, sram_mb):
self.name = name
self.dram_bw = dram_bw
self.dram_lat = dram_lat
self.sram = sram_mb
pkgs = [
Package("Traditional 2D", 3.2, 120, 80),
Package("2.5D I-Cube", 3.6, 85, 192),
Package("3D X-Cube", 4.0, 40, 512),
]
hidden_dim, num_layers = 8192, 80
print(f"{'Package':<22} {'DRAM BW':<12} {'Latency':<12} {'SRAM':<10} {'Per-Token Lat':<14}")
print("-" * 70)
for p in pkgs:
weights_bytes = 4 * hidden_dim * hidden_dim * 4 * num_layers
mem_lat_us = (weights_bytes / (p.dram_bw * 1e9 / 8)) * 1e6
sram_hit = min(1.0, p.sram * 1e6 / weights_bytes)
eff_lat = mem_lat_us * (1 - sram_hit * 0.85) + 10.0
print(f"{p.name:<22} {p.dram_bw:<12.1f} {p.dram_lat:<12} {p.sram:<10} {eff_lat:<14.2f}μs")
print("\nConclusion: 3D packaging reduces LLM inference")
print(" memory latency by 50-65% via SRAM stacking")
analyze_packaging_impact()
5. OpenAI’s Custom Chip Strategy: From NVIDIA Dependency to Self-Sovereign Ecosystem
5.1 Quantifying the Compute Gap
OpenAI projects cumulative compute spending of $750 billion by 2030. “Compute is severely insufficient,” says Harrison Kim. What does this number mean?
┌──────────────────────────────────────────────────────────┐
│ OpenAI Compute Spending Forecast (2024-2030) │
│ │
│ Annual Spend ($B) │
│ 2000│ │
│ │ ┌───┐ │
│ 1500│ ┌┘ └┐ │
│ │ ┌──┘ │ │
│ 1000│ ┌───┘ │ │
│ │ ┌───┘ │ │
│ 500│ ┌────┘ │ │
│ │ ┌────┘ │ │
│ │ ┌────┬────┘ │ │
│ 0│────┘ └────────────────────────────────│ │
│ 2024 2025 2026 2027 2028 2029 2030 │ │
│ │
│ Cumulative: ~$7,500B (2030) │
│ │
│ Breakdown: │
│ ██ GPU/Chip procurement: ~30% ██ Data centers: ~25%│
│ ██ Power/cooling: ~20% ██ Ops+network: ~15% │
│ ██ R&D: ~10% │
└──────────────────────────────────────────────────────────┘
Key Interpretation:
- At ~$30,000/H100, $750B could purchase ~25 million GPUs
- But NVIDIA’s total capacity is insufficient—global H100-equivalent shipments in 2025 were ~3-4M units
- Custom chips can reduce inference costs by 50-80%—this is the fundamental economic driver for in-house silicon
5.2 Strategic Roadmap: Three Chip Generations
┌──────────────────────────────────────────────────────────┐
│ OpenAI Custom Chip Roadmap (Projected) │
│ │
│ Jalapeño (2026H1) │
│ ┌────────────────────────────────────────────────┐ │
│ │ Process: TSMC N3E (3nm FinFET) │ │
│ │ Design: Broadcom collaboration + OpenAI arch │ │
│ │ Target: Inference optimization, partial H100 │ │
│ │ Compute: ~1200 TFLOPS (FP8) │ │
│ │ Key Metric: 50% inference cost reduction │ │
│ └────────────────────────────────────────────────┘ │
│ │
│ ▼ Iteration │
│ │
│ Chip-2 (2027-2028) │
│ ┌────────────────────────────────────────────────┐ │
│ │ Process: Samsung SF2 (2nm GAA) + TSMC N2 │ │
│ │ Design: Full-stack in-house (compiler included)│ │
│ │ Target: Unified training + inference arch │ │
│ │ Innovation: Native MoE/expert parallelism │ │
│ │ Key Metric: 2× power efficiency vs Jalapeño │ │
│ └────────────────────────────────────────────────┘ │
│ │
│ ▼ Iteration │
│ │
│ Chip-3 (2029-2030) │
│ ┌────────────────────────────────────────────────┐ │
│ │ Process: Samsung SF2P/SF1.4 (2nm+/1.4nm) │ │
│ │ Design: Proprietary litho/packaging collab │ │
│ │ Target: Full training clusters, 80% NVIDIA │ │
│ │ Innovation: Compute-in-memory, optical I/O │ │
│ │ Key Metric: 3-5× training efficiency vs H100 │ │
│ └────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────┘
5.3 The Strategic Value of Dual-Sourcing
Choosing Samsung as a second foundry partner is a dual calculus of geopolitics and supply chain resilience:
package strategy
import "fmt"
type FabSource struct {
Name string
CapacityPerYear int
LeadTimeMonths int
YieldRate float64
RelativeCost float64
}
func EvaluateDualSourcing() {
tsmc := FabSource{"TSMC", 100, 12, 0.90, 1.0}
samsung := FabSource{"Samsung", 60, 8, 0.75, 0.8}
// Single source vs dual sourcing capacity index
singleIndex := tsmc.CapacityPerYear * int(float64(tsmc.LeadTimeMonths)*0.9)
dualIndex := (tsmc.CapacityPerYear + int(float64(samsung.CapacityPerYear)*0.8)) *
int((float64(tsmc.LeadTimeMonths)+float64(samsung.LeadTimeMonths))/2.5)
fmt.Println("========== Foundry Strategy Comparison ==========")
fmt.Printf("TSMC single-source capacity index: %d\n", singleIndex)
fmt.Printf("TSMC+Samsung dual-source index: %d\n", dualIndex)
fmt.Printf("Dual-source elasticity gain: +%.0f%%\n",
float64(dualIndex-singleIndex)/float64(singleIndex)*100)
fmt.Println("\nRisk score (lower is better):")
fmt.Printf(" Single source (TSMC): %.0f\n", float64(singleIndex)*0.6)
fmt.Printf(" Dual-source strategy: %.0f\n", float64(dualIndex)*0.3)
fmt.Println("\nConclusion: Dual-sourcing is inevitable for AI silicon")
fmt.Println(" Even with lower Samsung yield, capacity elasticity")
fmt.Println(" and geopolitical risk diversification are critical")
}
6. Industry Impact of Exploding AI Compute Spending
6.1 The $750 Billion Industry Restructuring
OpenAI’s projected $750 billion compute expenditure will fundamentally reshape the semiconductor industry’s profit distribution:
┌──────────────────────────────────────────────────────────┐
│ $750B Spend Allocation Across Industry Sectors │
│ │
│ ┌─────────────────────────────────┐ │
│ │ GPU/ASIC Procurement ~$2,250B │ ████████░░ 30% │
│ ├─────────────────────────────────┤ │
│ │ Data Center Infra ~$1,875B │ ██████░░░░ 25% │
│ ├─────────────────────────────────┤ │
│ │ Power & Cooling ~$1,500B │ █████░░░░░ 20% │
│ ├─────────────────────────────────┤ │
│ │ Networking & Ops ~$1,125B │ ████░░░░░░ 15% │
│ ├─────────────────────────────────┤ │
│ │ R&D & Talent ~$750B │ ██░░░░░░░░ 10% │
│ └─────────────────────────────────┘ │
│ │
│ Key Impact Areas: │
│ • Semiconductor CapEx surpasses dot-com bubble peak │
│ • Per-cluster power >1GW becomes normal │
│ • Cooling: air → liquid → immersion every 3 years │
│ • Networking: InfiniBand → UltraEthernet → optical │
└──────────────────────────────────────────────────────────┘
6.2 The Inference Cost Flywheel
Custom chips directly reduce inference costs, which in turn stimulates exponential demand growth:
def inference_cost_flywheel():
print("Inference Cost Flywheel Simulation")
print("=" * 65)
base_cost = 10.0 # $/M tokens (GPT-4 class, 2024 baseline)
annual_reduction = 0.35
demand_growth = 0.80
years = 5
cost = base_cost
demand = 1.0
print(f"{'Year':<10} {'$/M tokens':<14} {'Rel. Cost':<12} {'Demand Vol':<14}")
print("-" * 65)
for y in range(years):
label = f"{2026+y}"
print(f"{label:<10} {cost:<14.2f} {cost/base_cost:<12.2%} {demand:<14.1f}")
cost *= (1 - annual_reduction)
demand *= (1 + demand_growth)
print("-" * 65)
print(f"Year 5 cost: ${cost:.2f}/M tokens ({(1-cost/base_cost):.0%} reduction)")
print(f"Demand growth: {demand:.1f}× CAGR: {demand_growth:.0%}")
inference_cost_flywheel()
7. Foundry Competition Landscape: A Three-Player Game
7.1 Technology Roadmap Comparison
┌──────────────────────────────────────────────────────────────────────┐
│ 2024-2028 Foundry Roadmap Comparison │
│ │
│ Year TSMC Samsung Intel │
│ ──────────────────────────────────────────────────────────────────── │
│ 2024 N3E (mass prod) SF3 (mass prod) 20A (Arrow Lake) │
│ FinFET GAA 1st Gen PowerVia (BSPDN) │
│ │
│ 2025 N3P (mass) SF3E (enhanced) 18A (Clearwater) │
│ FinFET optim. GAA 2nd Gen RibbonFET+GAA │
│ │
│ 2026 N2 (mass) SF2 (mass) 18A (mass ship) │
│ GAA/Nanosheet GAA 3rd Gen RibbonFET mature │
│ ←─── OpenAI + Samsung partnership ───→ │
│ │
│ 2027 N2P (enhanced) SF2P (enhanced) 14A (planned) │
│ GAA optim. GAA+BSPDN RibbonFET++ │
│ │
│ 2028 N1.4 (1.4nm) SF1.4 (1.4nm) 14A (mass prod) │
│ Next-gen GAA Next-gen GAA Next-gen RibbonFET │
│ │
│ ──────────────────────────────────────────────────────────────────── │
│ AI Chip │
│ Adv. Ecosystem mature GAA experience lead US domestic capacity │
│ Disadv. Capacity crunch Yield challenges Customer ecosystem │
│ Geopolitical risk Customer trust No mobile client base │
└──────────────────────────────────────────────────────────────────────┘
7.2 Samsung Foundry’s “Comeback”
Samsung has been the perpetual #2 in foundry, but the AI chip boom offers a historic opportunity:
Samsung’s Core Differentiators:
GAA Generational Lead: Samsung is the only foundry to have mass-produced three generations of GAA transistors: SF3 (2023) → SF3E (2024) → SF2 (2026). Its GAA know-how far exceeds any competitor.
One-Stop Packaging: Samsung is uniquely able to offer memory (HBM3E), logic foundry, and advanced packaging under one roof. This integration means AI chips can go from design to packaged product entirely within Samsung, reducing inter-vendor interface losses.
Pricing Competitiveness: As the challenger, Samsung typically offers 15-25% lower foundry pricing than TSMC. For OpenAI’s massive-scale deployment, this cost advantage is highly significant.
7.3 Anthropic is Also in Line
Notably, Anthropic is also in discussions with Samsung Foundry to explore custom chips for Claude models (still at an early stage). This signals that custom silicon is evolving from an OpenAI “solo act” into an industry-wide trend. By 2027, at least five AI companies are expected to have custom chip programs.
8. Korea: OpenAI’s “Second Home”
Harrison Kim also revealed several key metrics during the Seoul press conference:
- Korean ChatGPT Enterprise users have surged ~28× year-over-year
- Korea is now the largest paid ChatGPT subscriber market outside the US
- Samsung ranks among the largest ChatGPT enterprise deployments globally
Korea’s AI ecosystem occupies a unique strategic position: it hosts the world’s top HBM suppliers (Samsung, SK Hynix), a leading logic foundry (Samsung), and one of Asia’s largest AI application markets. By anchoring chip development in Korea, OpenAI is making a bet on both technology and market access.
korea_ecosystem = {
"Chip Design": {"level": "Medium", "players": "Rebellions, Sapeon"},
"Memory": {"level": "Very High","players": "Samsung HBM3E, SK Hynix HBM4"},
"Foundry": {"level": "High", "players": "Samsung SF2/SF3"},
"Adv. Packaging": {"level": "High", "players": "Samsung I-Cube/X-Cube"},
"AI Applications":{"level": "Fast Growth","players": "Samsung ChatGPT Enterprise"},
"Talent Pipeline":{"level": "Med-High", "players": "KAIST, POSTECH"},
}
print("=" * 60)
print("OpenAI's Full-Chain Collaboration in Korea")
print("=" * 60)
for sector, info in korea_ecosystem.items():
print(f" {sector:<20} | {info['level']:<15} | {info['players']}")
9. Conclusions & Outlook
9.1 Technology Trend Summary
┌──────────────────────────────────────────────────────────┐
│ Key Predictions: 3-5 Years │
│ │
│ 🏭 Chip Architecture │
│ GPU General-Purpose → AI Custom ASIC → Model-Chip Co-Des│
│ 2024 2026 2028+ │
│ │
│ 🔬 Process Technology │
│ FinFET(5nm) → GAA(3nm) → GAA+(2nm) → CFET(sub-1nm) │
│ 2020 2023 2026 2028+ │
│ │
│ 📦 Packaging Technology │
│ 2D → 2.5D CoWoS → 3D Stacking → Optical I/O+CIM │
│ │
│ 💰 Industry Landscape │
│ NVIDIA ~85% share → NVIDIA+Custom ASIC → Diversified │
│ 2024 2026 2028+ │
│ │
│ 🌏 Geopolitical Foundry │
│ TSMC dominant → TSMC+Samsung dual → Regionalized │
│ 2024 2026 2028+ │
└──────────────────────────────────────────────────────────┘
9.2 Implications for AI Practitioners
Inference costs will collapse: The combination of custom silicon, advanced packaging, and sparse computation is projected to reduce LLM inference costs to 1/10th of 2024 levels by 2028. This will make long-context, multi-modal, real-time AI applications economically viable at unprecedented scale.
Model-Chip Co-Design becomes a core competency: Future AI companies will need not only algorithm researchers but also chip architects. The OpenAI-Samsung partnership establishes a new paradigm where algorithms define silicon.
Compute is no longer the sole moat: As compute becomes commoditized, data quality and model architecture innovation will re-emerge as the true differentiators for frontier AI capabilities.
This analysis is based on publicly available information and technical projections. Some figures are reasonable estimates based on industry data.
Published: September 10, 2026