---
type: claim
domain: ai-alignment
description: "CoWoS packaging, HBM memory, and datacenter power each gate AI compute scaling on timescales (2-10 years) much longer than algorithmic or architectural advances (months) — this mismatch creates a window where alignment research can outpace deployment even without deliberate slowdown"
confidence: experimental
source: "TSMC CoWoS capacity constraints (CEO public statements), HBM vendor sell-out confirmations (SK Hynix, Micron CFOs), IEA/Goldman Sachs datacenter power projections, Epoch AI compute doubling trends, Heim et al. 2024 compute governance framework"
created: 2026-03-24
depends_on:
- technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap
- safe AI development requires building alignment mechanisms before scaling capability
challenged_by:
- Algorithmic efficiency gains may outpace physical constraints — Epoch AI finds algorithms halve required compute every 8-9 months
- Physical constraints are temporary — CoWoS alternatives by 2027, HBM4 increases capacity, nuclear can eventually meet power demand
- If the US self-limits via infrastructure lag, compute migrates to jurisdictions with fewer safety norms
secondary_domains:
  - collective-intelligence
related:
- inference efficiency gains erode AI deployment governance without triggering compute monitoring thresholds because governance frameworks target training concentration while inference optimization distributes capability below detection
reweave_edges:
- inference efficiency gains erode AI deployment governance without triggering compute monitoring thresholds because governance frameworks target training concentration while inference optimization distributes capability below detection|related|2026-03-28
- AI datacenter power demand creates a 5-10 year infrastructure lag because grid construction and interconnection cannot match the pace of chip design cycles|supports|2026-04-04
supports:
- AI datacenter power demand creates a 5-10 year infrastructure lag because grid construction and interconnection cannot match the pace of chip design cycles
---

# Physical infrastructure constraints on AI scaling create a natural governance window because packaging memory and power bottlenecks operate on 2-10 year timescales while capability research advances in months

The alignment field treats AI scaling as a function of investment and algorithms. But the physical substrate imposes its own timescales: advanced packaging expansion takes 2-3 years, HBM supply is sold out for 1-2 years forward, new power generation takes 5-10 years. These timescales are longer than the algorithmic improvement cycle (months) but shorter than institutional governance cycles (decades). This mismatch creates a window — not designed, but real — where physical constraints slow deployment faster than they slow alignment research.

## The timescale mismatch

Three independent physical constraints gate AI compute scaling, each on different timescales:

**Packaging (2-3 years):** TSMC's CoWoS capacity is sold out through 2026 with demand exceeding supply even at planned expansion rates. Google has already cut TPU production targets due to CoWoS constraints. Intel's EMIB alternative is gaining interest but won't reach comparable scale before 2027-2028. Each new AI chip generation requires larger interposers, so the bottleneck worsens per generation.

**Memory (1-2 years):** All three HBM vendors (SK Hynix, Samsung, Micron) have confirmed their supply is sold out through 2026. HBM4 accelerates to meet NVIDIA's next-generation architecture, but each GB of HBM requires 3-4x the wafer capacity of DDR5, creating structural supply tension.

**Power (5-10 years):** New power generation takes 3-7 years to build. Grid interconnection queues in the US average 5+ years with only ~20% of projects reaching commercial operation. Nuclear deals for AI (Microsoft-Constellation, Amazon-X-Energy, Google-Kairos) cover 2-3 GW near-term against projected need of 25-30 GW additional capacity. This is the longest-horizon constraint.

Meanwhile, frontier training compute doubles every 9-10 months (Epoch AI), and algorithmic efficiency improvements halve required compute every 8-9 months. The demand curve is exponential; the supply curves are linear or stepwise.

## Why this is a governance window

Lennart Heim and colleagues at GovAI/RAND have argued that compute is the most governable input to AI development because it is physical, trackable, and produced by a concentrated supply chain. Physical infrastructure constraints amplify this governability: not only can you track who has compute, the total amount of compute is itself limited by physical bottlenecks.

This creates what I call "alignment by infrastructure lag" — the physical substrate buys time for alignment research without requiring anyone to deliberately slow down. The window exists because:

1. **Alignment research is not compute-constrained.** Theoretical alignment work, interpretability research, governance design, and evaluation methodology don't require frontier training clusters. They require researchers, ideas, and modest compute for experiments.

2. **Deployment IS compute-constrained.** Deploying AI capabilities at scale (inference for billions of users, new training runs for frontier models) requires the physical infrastructure that is bottlenecked.

3. **The mismatch favors alignment.** The activities that need more time (alignment research) can proceed unconstrained while the activities that create risk (capability scaling and deployment) are physically gated.

## Challenges

**Algorithmic progress may route around physical constraints.** If algorithmic efficiency improvements (halving required compute every 8-9 months per Epoch AI) compound faster than physical constraints bind, the governance window closes. A 10x capability jump may come from better algorithms on existing hardware, not from new hardware.

**The window is temporary.** CoWoS alternatives may break the packaging bottleneck by 2027. HBM4 increases per-stack capacity. Nuclear and natural gas can eventually meet power demand. The 2-5 year window where these constraints bind most tightly is the window — not a permanent condition.

**Geographic asymmetry.** Physical constraints are location-specific. If US infrastructure lags while other jurisdictions build faster, compute migrates to regions with fewer safety norms. The constraint doesn't reduce total AI capability — it shifts where capability develops. This is the strongest counter-argument and applies equally to deliberate slowdown proposals.

**This is not a strategy — it's an observation.** The claim is that the window exists, not that it should be relied upon. Depending on infrastructure lag for alignment is like depending on traffic for punctuality — it might work but it's not a plan.

---

Relevant Notes:
- [[technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap]] — physical infrastructure constraints partially close this gap by slowing the exponential
- [[safe AI development requires building alignment mechanisms before scaling capability]] — infrastructure lag creates a natural version of this ordering
- [[compute export controls are the most impactful AI governance mechanism but target geopolitical competition not safety leaving capability development unconstrained]] — physical constraints complement export controls by limiting total compute regardless of who controls it
- [[the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it]] — infrastructure constraints apply to all competitors equally, unlike voluntary safety commitments

Topics:
- [[domains/ai-alignment/_map]]
