Impacts of AI · Site 08 of 08 · Infrastructure · Under Stress

2,600Gigawatts stuck

The US interconnection queue held over 2,600 GW of proposed capacity in 2023, with typical waits of five years or more. AI datacentres are now the fastest-growing source of new demand the queue has to absorb.

Microsoft, 2026

Single-source
Scroll · footage plays as you go

The network is older than its users

Power delivery is built to last 15–25 years. Racks have gone from under 20 kW in the web era to more than 150 kW today, with roadmaps approaching 1 MW by 2027.

Sun et al. (Stanford / Microsoft Azure Research), 2026

Demand climbs faster than capacity

The IEA projects datacentre electricity use roughly doubling from 485 TWh in 2025 to about 950 TWh by 2030, with AI-focused facilities tripling.

IEA, cited in Patel et al., 2026

And the repair bill compounds

Bringing one leading-edge chip fab to the US rather than Taiwan costs roughly $3–5 billion in incentives; in ten years it will cost about three times as much.

CSET, 2022

01 / What investment returns

Money spent early pays twice

Better chips per watt, smarter power, light instead of copper: the returns the hardware research can measure.

Q1 · Efficiency
2.7×
Performance per watt, TPU v4 over TPU v3

Google's TPU v4 outperforms TPU v3 by 2.1× and improves performance per watt by 2.7×; its 4,096-chip supercomputer is nearly 10× faster overall.

Jouppi et al. (Google), ISCA, 2020

Peer-reviewed
Q1 · Power profiles
15%
Energy saved by workload-aware GPU power profiles

NVIDIA's Blackwell power profiles save up to 15% energy while holding performance above 97% for critical applications, raising throughput up to 13% in a power-capped facility.

Narayanaswamy et al. (NVIDIA), 2025

Company-reported
Q2 · Latency
5.8×
Faster prefill with photonic interconnects, at best

High-radix photonic interconnects give 2.1–3.2× lower latency in stressed high-batch regimes and 2.8–5.8× over baselines when communication is the limit.

Madhavan et al. (Lightmatter), 2026

Peer-reviewed
Q4 · Interconnect cost
31%
Of NVL-72's cost for an equivalent high-bandwidth domain

An optical-circuit-switched design, InfiniteHBD, matches NVIDIA's NVL-72 high-bandwidth domain at 31% of its cost and lifts model FLOPs utilisation 3.37× over a DGX cluster.

Fu et al. (ACM SIGCOMM), 2025

Peer-reviewed
Q2 · Throughput
33%
More worst-case throughput from traffic-aware optics

A traffic-aware reconfigurable optical interconnect, Vermilion, provably achieves at least 33% more worst-case throughput than existing designs.

Cao et al., 2025

Single-source
01 / 05
Cutting on a building site
Q1 · Cheap flexibility
<5%
Of a TPU v4 supercomputer's cost goes on its optical switches

The optical circuit switches that let Google route around failures cost under 5% of the TPU v4 supercomputer's capital cost and under 3% of its power.

Jouppi et al. (Google), ISCA, 2020

Peer-reviewed
Q1 · Energy per bit
4.3pJ/bit
To move data over 3D photonics, against >20 for pluggable optics

3D-integrated photonic links move data at about 4.3 pJ per bit end to end, comparable to passive copper and far below the >20 pJ/bit of pluggable optics.

Madhavan et al. (Lightmatter), 2026

Peer-reviewed
Q4 · Switching power
20nW
Static power of a silicon-photonics optical switch

A foundry-compatible silicon-photonics MEMS switch holds its state at about 20 nanowatts with under 1.5 dB insertion loss: near-zero-power optical switching.

UC Berkeley, 2026

Single-source
No verified statistic

No verified figure exists for how much of an efficiency saving is kept by the operator rather than spent on more computing. The papers report savings per job, not what happens to them.

No verified statistic

No verified figure exists for the lifetime of today's photonic interconnects in production. Every result above is from a lab, a pilot or a single deployment.

02 / Emerging & unsettled

Directional numbers, thin corroboration

Waste that design can remove, specs that silicon doesn't honour, and supply concentrated in a few places.

Q3 · GPU waste
0.53%
GPU waste with an optical high-bandwidth domain
GPU waste ratio, TP32 jobs
  1. NVL-7210.04%
  2. TPUv47.56%
  3. InfiniteHBD0.53%

Running the same jobs, InfiniteHBD wasted 0.53% of GPUs, against 7.56% for TPUv4 and 10.04% for NVL-72.

Fu et al. (ACM SIGCOMM), 2025

Peer-reviewed
Q8 · Spec vs. silicon
1/30
Of FP8 throughput is all Blackwell Ultra gives INT8

NVIDIA's B300 lists INT8 support, but the silicon rate-limits it to about 1/30 of FP8, and neither vLLM nor SGLang ships a working INT8 path for it.

Chen et al., 2026

Single-source
Q9 · Where chips are made
Concentration in four numbers
85%Of leading-edge (5 nm) logic capacity is in Taiwan
65%Of legacy logic (>16 nm) is in China and Taiwan
50%Of global DRAM capacity is in South Korea
43%Of DRAM is in Taiwan and China

Leading-edge logic, legacy logic and memory each sit mostly in one or two places; the US has minimal onshore DRAM capacity and none at the leading edge.

CSET, 2022

Corroborated
Q3 · Memory reliability
10×
Fewer row activations to flip a bit, in under a decade

As DRAM, including the HBM feeding AI accelerators, moves to smaller nodes, RowHammer bitflips happen with about 10× fewer row activations than a decade ago.

Olgun, Kim et al., 2023

Corroborated
Q7 · Cheaper memory
78%of throughput
Of throughput kept with error-prone HBM and smart error handling

Even with raw HBM bit error rates up to 10⁻³, a fault-managed system kept 78% of throughput with at least 97% accuracy: one route to cheaper memory.

Bae et al., 2025

Single-source
No verified statistic

No verified figure exists for what the interconnection queue costs the projects stuck in it. Estimates circulate, but none traces to a disclosed method.

No verified statistic

No verified figure exists for how often new optical and memory components fail in production fleets. The published results are from controlled evaluations.

03 / What decay costs

Deferral is a loan at bad rates

Costly memory, slow fabs, networks that collapse under new workloads and power capacity left stranded.

Q7 · Memory cost
5–10×
More expensive per bit than ordinary DRAM: HBM

Stacking and yield sensitivity drive High-Bandwidth Memory to at least 5–10× the cost per bit of conventional DRAM, a gap that grows as AI work becomes memory-bound.

Bae et al., 2025

Single-source
Q7 · Fab delays
918days
Average to build a US chip fab, up 38% since the 1990s

The time to build a new US fab rose 38%, from an average of 665 days in the 1990s to 918 days in the 2010s.

CSET, 2021

Corroborated
Q10 · Network collapse
85.6%
Drop in switching efficiency when training moves to Mixture-of-Experts

Moving from dense to Mixture-of-Experts training cut switching efficiency by 85.6% on a Rail-Optimized network and by 98.1% on a 3D-Torus.

Switching-efficiency study (arXiv), 2026

Single-source
Q5 · Stranded power
20×
Spread in throughput per watt across power-delivery designs

Across realistic power-delivery designs, rack projections and inference workloads, throughput per watt varies by more than 20× while cost per watt varies by over 20%.

Sun et al. (Stanford / Microsoft Azure Research), 2026

Single-source
01 / 04
A derelict chimney
Q9 · Demand
160%
Forecast rise in datacentre power demand this decade

Investment analyses forecast a 160% increase in datacentre power demand over this decade, while the grid's own connection queue already runs five years.

Microsoft, 2026

Single-source
Q10 · Onshore memory
0
Leading-edge DRAM fabs in the United States

The US has minimal onshore DRAM manufacturing capacity and none at the leading edge, though DRAM underpins the HBM in every AI accelerator.

CSET, 2022

Corroborated
Q10 · Reshoring
$5–10bn
In CHIPS Act funds to bring back one large DRAM fab

The smallest cost-effective DRAM fab, 100,000 wafers a month or about 6% of global capacity, would need $5–10 billion in CHIPS Act funding to reshore.

CSET, 2022

Corroborated
04 / Structural gaps

What nobody is counting

The memory wall, supply chokepoints and the operating details fleets do not publish.

Q6 · Memory wall
1,300–2,600pJ
Energy for one 64-bit DRAM access

Moving a 64-bit word from off-chip DRAM costs on the order of 1,300 to 2,600 picojoules, against a few picojoules for an on-chip floating-point operation: about three orders of magnitude.

Horowitz, cited in Patel et al., 2026

Single-source
Q9 · Chokepoints
5 of 10
Semiconductor chokepoints where the US share is zero

Of ten named points of consolidation in the semiconductor supply chain, the US holds 0% of the market in five, from leading-edge logic chips to EUV resists.

Leading-edge logicMemory chipsCrystal growing furnacesWafer bondersCrystal machining toolsWafer handling toolsSpin coating toolsDicing toolsResist processing toolsEUV resists
US share 0%US share above 0%Country shares as of 2021

CSET, BIS submission, 2021

Corroborated
Q5 · Cost of design
20%+
Spread in cost per watt between power-delivery designs

Cost per watt varies by more than 20% between power-delivery designs, an impact the authors measure in billions of dollars, yet no operator discloses which design it built.

Sun et al. (Stanford / Microsoft Azure Research), 2026

Single-source
Q2 · Availability
0.1–1.0%
Of the time each TPU v4 host is down

Optical switching lets a 4,096-chip TPU v4 supercomputer keep training around its 1,000 CPU hosts, each unavailable 0.1–1.0% of the time; how other fleets cope is undisclosed.

Jouppi et al. (Google), ISCA, 2020

Peer-reviewed
Q8 · Software gap
0
Major open-source serving engines with a working INT8 path on B300

vLLM ships no INT8 kernel for Blackwell and fails at the first forward pass; SGLang's INT8 support stops a generation earlier. The spec sheet says otherwise.

Chen et al., 2026

Single-source
05 / CodaNo statistics in this section

Concrete. Current. Water.

Concrete
Current
Water
06 / Closing

Which bill would you rather pay?

Both sides of this page are real and both are partial. Every figure carries its source and a confidence label, and where no verified number exists the page says so rather than approximating one. The judgement is yours.

Sources · 16 documentsFrom the LearnHaus infrastructure corpus
  1. 01Reinforcement Learning for Power Control in LLM Training FleetsMicrosoft · arXiv / Cornell University · 2026 · cited for Q0, Q9
  2. 02ISCA 2023 Camera Ready TPUv4 PaperJouppi et al. (Google), ISCA · ACM · 2020 · cited for Q1, Q2
  3. 03Datacenter Energy Optimized Power ProfilesNarayanaswamy et al. (NVIDIA) · NVIDIA · 2025 · cited for Q1
  4. 04Scaling Inference Prefill with High-Radix Photonic InterconnectsMadhavan et al. (Lightmatter) · arXiv / Cornell University · 2026 · cited for Q2, Q1
  5. 05InfiniteHBD: Building Datacenter-Scale High-Bandwidth Domain for LLM with Optical Circuit Switching TransceiversFu et al. (ACM SIGCOMM) · ACM · 2025 · cited for Q4, Q3
  6. 06Vermilion: A Traffic-Aware Reconfigurable Optical Interconnect with Formal Throughput GuaranteesCao et al. · arXiv / Cornell University · 2025 · cited for Q2
  7. 07Zero-change foundry compatible silicon photonics MEMS optical switchUC Berkeley · arXiv / Cornell University · 2026 · cited for Q4
  8. 08Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell UltraChen et al. · arXiv / Cornell University · 2026 · cited for Q8
  9. 09CSET - Sustaining U.S. Competitiveness in Semiconductor ManufacturingCSET · Center for Security and Emerging Technology (CSET) / Georgetown · 2022 · cited for Q9, Q10, Opening
  10. 10RowHammer and Read Disturbance in HBM2 DRAM ChipsOlgun, Kim et al. · arXiv / Cornell University · 2023 · cited for Q3
  11. 11Breaking the HBM Bit Cost Barrier: Domain-Specific ECC for AI Inference InfrastructureBae et al. · arXiv / Cornell University · 2025 · cited for Q7
  12. 12CSET - No Permits, No FabsCSET · Center for Security and Emerging Technology (CSET) / Georgetown · 2021 · cited for Q7
  13. 13Switching Efficiency Framework: Quantifying Network Topologies and Interconnect Bottlenecks in AI Data CentersSwitching-efficiency study (arXiv) · arXiv / Cornell University · 2026 · cited for Q10
  14. 14Designing Datacenter Power Delivery Hierarchies for the AI EraSun et al. (Stanford / Microsoft Azure Research) · arXiv / Cornell University · 2026 · cited for Q5, Opening
  15. 15AI Hardware Accelerators for Large Language Models: Architectures and the Memory WallPatel et al. · arXiv / Cornell University · 2026 · cited for Q6, Opening
  16. 16Semiconductor Manufacturing Supply Chain BIS SubmissionCSET, BIS submission · Center for Security and Emerging Technology (CSET) / Georgetown · 2021 · cited for Q9

The IEA projection and Horowitz's energy figures are cited via Patel et al., as that survey states them.