Your briefing

The Stargate Project is a $500 billion initiative led by SoftBank and OpenAI to build U.S. AI infrastructure over four years, deploying $100 billion immediately to expand compute, create hundreds of thousands of jobs, and bolster national-security capabilities. Initial equity includes SoftBank, OpenAI, Oracle, and MGX; partners include NVIDIA, Microsoft, Arm, and Oracle; buildout begins in Texas and RFP/RFQ details were released Jan 31, 2025.

Texas' power grid faces a January 23, 2026 winter storm expected milder than 2021's Uri; ERCOT showed ≥7 GW reserve and >9 GW thermal outages Friday, while battery capacity jumped to ~17,000 MW from 220 MW in five years.

Anthropic's 'Build AI in America' report sets concrete infrastructure targets and policy fixes to keep U.S. AI leadership: it projects 2 GW and 5 GW single-model training sites in 2027–2028, 20–25 GW of frontier training demand by 2028, and a need for ~50 GW total capacity; it recommends accelerated permitting, federal land use, transmission upgrades, and supply-chain and workforce programs.

Epoch AI's Frontier Data Centers database (updated Feb 19, 2026) compiles satellite imagery, permits, and public documents to estimate power, compute, timelines and costs for major U.S. AI campuses. It reports ~2.5M H100e-equivalent tracked (15–17% of shipped compute as of Nov 2025), an 80% confidence range of ±50%, GW-scale builds in 1–3.6 years, and site-level projections such as Fairwater at 3.3 GW.

AI data centers are becoming some of the largest infrastructure projects ever: concentrated campuses like OpenAI’s Stargate Abilene require roughly 1 GW of continuous power, enormous land (3.5 km²), and huge capital (~$32B). Aggregate US demand for frontier training could reach 20–30 GW by late 2027, driving siting toward jurisdictions with abundant, fast‑deployable power (e.g., Texas, China). What distinguishes AI centers is very high power density — racks such as NVIDIA’s NVL72 hold 72 GPUs and can draw >100 kW each — necessitating liquid cooling, chiller systems, and significant water management. Climate and security effects are currently localized (AI uses ~1% of US power now) but could grow to ~5% by 2027 if fossil fuels remain primary sources; satellite‑visible cooling infrastructure also complicates secrecy and physical security.

OpenAI’s Stargate program targets 10 GW of U.S. AI capacity by 2029 and reports it is already beyond halfway to that target one year after the Jan 2025 launch; the Abilene, Texas site is already training and serving frontier models. Stargate pledges to fund incremental generation, storage, and grid upgrades, minimize water use with closed-loop cooling, and create workforce pipelines via OpenAI Academies (first in Abilene, spring 2026).

OpenAI and SoftBank Group each committed $500 million to SB Energy and OpenAI leased 1.2 GW for a Milam County AI data center, part of SB Energy’s multi-gigawatt campus program. Initial facilities target service entry in 2026, emphasize reduced water usage, job creation, and pair OpenAI’s data center designs with SB Energy’s integrated energy delivery; Ares provided $800 million in preferred equity.

OpenAI and Broadcom announced a multi-year collaboration to co-develop racks featuring 10 GW of OpenAI‑designed AI accelerators paired with Broadcom Ethernet, PCIe and optical networking, aiming to start deployments in the second half of 2026 and finish by end of 2029. OpenAI intends to bake model-driven architecture choices into the custom silicon to improve performance and efficiency for large-scale training and inference.

Stargate is expanding via a new OpenAI–Oracle agreement to add 4.5 GW of U.S. AI data‑center capacity, bringing total Stargate development to over 5 GW and supporting more than 2 million chips. Oracle has started delivering Nvidia GB200 racks and OpenAI is running early training/inference workloads; OpenAI projects 100,000+ jobs and cites a prior White House $500B/10 GW commitment.

Standard Intelligence Team deployed a 30 PB, 100Gbps storage cluster (2,400 HDDs, 100 DS4246 chassis, 10 head nodes) to store ~90M hours of video for pretraining, costing ~$426.5k capex and $29.5k/month including depreciation and $17.5k/month internet+power. They used simple software (200-line Rust writer, nginx, SQLite, XFS), prioritized simplicity over redundancy, and report saturating their 100G link while avoiding complex systems like Ceph.

Crusoe is scaling energy storage for data center infrastructure with two distinct battery strategies: long-duration storage from Form Energy and second-life EV battery systems from Redwood Materials. The 12 GWh Form purchase is a notable commercial validation of 100-hour iron-air storage, while Crusoe’s existing 12 MW / 63 MWh microgrid and planned 8 MW expansion show how reused EV packs may complement large-scale backup and load-shifting needs for AI-oriented power demand.

China’s March 2026 climate-and-energy policy package reinforces a familiar but increasingly formalized strategy: rapid clean-energy expansion, continued attention to energy security, and gradual legal codification of long-term decarbonization. The final 15th five-year plan changed little from the earlier draft, but it sharpened the institutional backdrop by referencing the newly passed ecological and environmental code and by adding geothermal energy to the list of promoted technologies. National Energy Administration head Wang Hongzhi framed 2026-2030 as both the decisive period for peaking carbon emissions and a critical phase for building a “new energy system,” emphasizing market-based pricing reform to enable fossil-fuel replacement without compromising reliability.

The briefing’s most important new development is legal, not just technological. China’s ecological and environmental code gives statutory footing to the “dual-carbon” goals of peaking by 2030 and neutrality by 2060, while embedding total-emissions caps, carbon-intensity controls, carbon trading, footprint management, and climate-related enforcement into a more rules-based governance structure. At the same time, China signed the global pledge to triple nuclear capacity by 2050, even though its own nuclear rollout has undershot recent targets, reaching only 62GW by end-2025 versus a 70GW target. The Middle East oil shock underscores why Beijing continues to pursue a diversified system: crude stockpiles and renewables cushion import risk, but oil and coal still retain strategic roles in petrochemicals, industrial feedstocks, and power-system flexibility.

Nvidia’s 2026 strategy, as described by Jensen Huang in this Stratechery interview after GTC, is to expand the meaning of accelerated computing from chips and training clusters into full-stack AI factories. Huang’s core thesis is that agents will not live solely inside chat interfaces or model APIs; they will increasingly operate the software humans already use, from SQL databases to desktop applications and EDA tools. That pushes Nvidia beyond selling GPUs into accelerating legacy software, designing CPUs, integrating networking and storage, and helping customers construct entire data-center-scale systems. He quantified the scale of the new buildout in unusually concrete terms: a 1 GW AI factory could cost $50-60 billion, of which roughly $15-17 billion is just land, power, and shell. His point is that customers will not risk tens of billions of dollars unless Nvidia can de-risk throughput, utilization, and system integration across cooling, power, networking, and compute. That framing is particularly relevant to the data-center and infrastructure buildout around AI, because Huang treats power as the fundamental economic constraint and argues system-level co-design is the only way to maximize intelligence produced per watt and per dollar.

On the technical side, Huang drew a line between the first wave of generative AI and the current wave of useful AI. He said reasoning, reflection, retrieval, and search improved enough over the last year to ground models, reduce hallucinations, and enable paid applications—especially coding agents. His description of coding as a separate modality was notable: code must be generated in coherent chunks and validated by execution, not merely by token probability. He also argued that transformers alone are not enough for future workloads, citing quadratic attention costs, KV-cache bloat, and the need for new architectures for continuous motion, geometric symmetry, and physical constraints. He named Nemotron 3’s transformer-plus-SSM hybrid and cuEquivariance as examples. At the infrastructure layer, he defended Nvidia’s CPU effort as complementary to GPUs rather than a reversal: the goal is to prevent expensive accelerators from sitting idle. Vera, he said, emphasizes very high single-thread performance and 3x higher bandwidth-per-CPU than prior designs to support agent tool use over NVLink. The Groq acquisition fits the same logic: Nvidia wants finer-grained heterogeneous inference, including disaggregating pieces of decode attention, to push latency-sensitive coding and enterprise agent workloads beyond what a general GPU-only setup can economically deliver.

The interview also highlighted constraints and geopolitics. Huang said the bottleneck in 2026 is not one thing but everything at once—power, fabs, supply chain, and site readiness—though he sounded confident in Nvidia’s planning across hundreds of suppliers. More striking was his China argument: keeping an American AI stack present in China is, in his view, strategically essential because Chinese labs and open-source communities are too important to ignore. He explicitly praised DeepSeek, Kimi, and Qwen as technically meaningful contributors and warned that exclusion could let rival ecosystems harden across chips, platforms, models, and applications. That makes the conversation highly relevant not just as an Nvidia profile, but as a window into how the dominant AI infrastructure vendor thinks about power markets, data-center economics, hardware/software co-design, and U.S.-China competition.

Epoch AI’s Frontier Data Centers Hub is a useful new primary-source-style dataset on the physical buildout of frontier AI infrastructure, focusing on power, compute, construction timing, and capital intensity. The most interesting contribution is methodological: rather than relying mainly on company announcements, the project estimates capacity from high-resolution satellite imagery of cooling systems and corroborates those estimates with permits and hardware assumptions, giving outside observers a way to independently track opaque hyperscale projects. The numbers underscore how quickly the industry is moving from large campuses to nation-scale power loads: multiple facilities are expected to exceed 1 GW in 2026, and the leading site, xAI’s Colossus 2, is projected at 1.4 million H100-equivalents. Epoch also highlights phased commissioning, projecting Microsoft Fairwater’s first phase in March 2026 and full operation by September 2027, while estimating top-end capital costs above $100 billion for the largest campuses.

Texas’ grid five years after Winter Storm Uri is materially stronger: regulators introduced stringent winterization standards (ambient-temperature specs up to −17°C in the Panhandle), ERCOT hired hundreds of inspectors, and the PUC gained enforcement power including fines up to $1M/day per incident. Uri’s cascading failure — a rapid loss of ~4 GW in under 30 minutes that triggered frequency collapse and four days of outages — motivated these changes.

The system has also added dispatchable battery capacity (from ~1 GW in 2021 to ~11–15 GW now), which McAdams says would have arrested frequency freefall and stabilised the system. The defining issue ahead is unprecedented load growth: ERCOT’s interconnection queue includes roughly 2,000 projects (~435 GW) and surged ~300% last year. Policy and market innovations — ADER virtual power plants, nodal settlement for DERs, residential demand-response telemetry — are positioned to mobilize distributed resources, reduce peak stress, defer wires, and lower customer bills if properly implemented.

The January 8, 2026 Energy Capital roundtable (hosts Matt Boms, Joshua Rhodes, Micalah Spenrath) framed 2025 as a year in which uncertain, rapid load growth — especially from data centers and AI-related facilities — upended conventional planning. Panelists contrasted ERCOT's ~85.5 GW peak today with roughly 225 GW of large-load requests in the queue by 2030, warning that timelines for transmission, interconnection, and generation can't keep pace without new approaches. Speakers argued speed is itself a grid constraint and recommended prioritizing fast, local flexibility: distributed energy resources, demand response, backup power, and distributed batteries (ADER pilots with vendors like Tesla were cited). They also flagged policy levers (Senate Bill 6, PUC dockets), private PPAs and co‑located solar+storage as near-term solutions, and stressed that high near-term capital costs (e.g., gas plants ~2.5x, transformers ~2x) require fair cost-sharing and community-focused siting to preserve reliability and equity.

Cleanview's December 2025 report (Michael Thomas) finds 1,891 U.S. power projects canceled in 2025, eliminating 266 GW of planned capacity — about 150% of ERCOT — and an estimated $400 billion in investment. Causes include local opposition, transmission shortfalls, battery saturation, tariffs and federal policy; ERCOT approved a $9.4B, 765-kV transmission "superhighway".

The Texas large-load queue hit 225 GW (up from 99 GW in Feb), adding ~30 GW in two months; only 6.6 GW are energized with 0.6 GW due in 2026. PUC filed an interconnection draft (comments due Dec 19) and ERCOT will vote on the eastern 765-kV plan.

Stanwell’s Tarong Battery entered commercial operations in February 2026: a 300 MW, two‑hour (600 MWh) lithium‑ion system of 164 Tesla Megapacks costing $514 million, sited beside Tarong coal station to deliver sub‑second firming and raise the site to 2.1 GW. The build responds to high coal outages in Apr–Sep 2025 and is one piece of Stanwell’s plan for 5 GW of batteries by 2035.

Power required to train frontier AI models has increased roughly 2.1x/year (90% CI 1.9–2.2x) across 45 frontier models since 2010, per Epoch AI (Sep 19, 2024); analysis uses 211 models and a log-linear fit.

OpenAI launched a Request for Proposals on January 15, 2026 to accelerate U.S. manufacturing for AI infrastructure, building on its Stargate initiative (nearly one year old) that has pledged capacity already well over halfway toward a 10‑gigawatt commitment. The RFP targets domestic production of data‑center inputs, compute/power/cooling hardware, consumer‑electronics modules, and robotics components; proposals are due June 2026.

OpenAI argues the US must rapidly expand electricity generation and skilled labor to preserve AI leadership, noting China added 429 GW in 2024 versus the US's 51 GW and urging a 100 GW/year national build. It cites user growth from >400M to >800M weekly, a $500B/10 GW Stargate pledge (nearly 7 GW and $400B in three years), and a 20% skilled-trades demand over five years.

AMD and OpenAI announced a multi-year, multi-generation partnership to deploy 6 gigawatts of AMD Instinct GPUs, beginning with a 1 GW roll-out of MI450 series racks in 2H 2026. AMD granted OpenAI warrants for up to 160 million shares tied to deployment, share-price, and technical milestones; AMD forecasts 'tens of billions' in revenue and accretive non-GAAP EPS.

OpenAI's Stargate initiative, joined on Oct 1, 2025 by Samsung and SK, commits to scaling advanced DRAM production (targeting 900,000 wafer starts/month) and expanding AI data-center capacity in Korea through an MoU with MSIT and partnerships with SK Telecom and Samsung affiliates; deployments include ChatGPT Enterprise and APIs to support enterprise workflows and regional AI growth.

OpenAI submitted a May 7, 2025 response to the DOE RFI arguing the U.S. must act quickly to build AI supercomputing hubs, citing “hundreds of billions” in private capital and potential for “tens of thousands” of jobs. The proposal highlights Stargate (first campus in Abilene, Texas), a Jan 2025 LANL partnership, and recommends streamlined permitting, lease predictability, and financial incentives for co‑located federal‑land deployments.

OpenAI’s Nov 4, 2024 comments to the NTIA argue that data center policy is critical for U.S. competitiveness: their outside forecast estimates a single 5 GW data center would support ~40,000 jobs and contribute $17–$20 billion in state GDP. OpenAI highlights $175 billion in global infrastructure capital and urges US-focused investments in energy, nuclear, and semiconductor capacity to retain AI leadership.

An OpenAI backend engineer (blog post published 2022-12-08) describes managing the company’s large-scale supercomputing clusters—debugging NUMA, GPUDirect, CPU pinning, and NIC issues (e.g., >30 Gbps kernel panic) and shipping fixes from hotfixes to upstream kernel changes; one tweak reportedly saved ~6 days of compute across the fleet per week, speeding research.

The Stargate Michigan campus in Saline Township, announced 2025-10-30, will be developed by Related Digital with construction starting in early 2026 and >2,500 union construction jobs. It's part of OpenAI's 4.5 GW Oracle partnership and—together with six U.S. Stargate sites (Oracle, SoftBank)—pushes planned capacity past 8 GW and >$450B investment, using closed-loop cooling and DTE's excess transmission capacity.

OpenAI and NVIDIA announced a strategic partnership (Sept 22, 2025) to deploy a minimum of 10 GW of NVIDIA systems—characterized as millions of GPUs—to power training and inference for next-generation models. NVIDIA may invest up to $100 billion incrementally tied to gigawatt deployments; the initial 1 GW phase is slated for H2 2026 on the Vera Rubin platform, with roadmap co-optimization and partner coordination (Microsoft, Oracle, SoftBank).

Stargate Norway is OpenAI’s announced Narvik data-center project (2025-07-31) built with Nscale and Aker as a 50/50 JV, targeting 230 MW of compute (expandable by 290 MW) and 100,000 NVIDIA GPUs by end-2026. The site will be fully renewable (hydropower), employ closed-loop direct-to-chip liquid cooling, and pipe waste heat to local low-carbon industry while prioritizing regional researchers and startups.

The Waratah Super Battery (850 MW, 1,680 MWh) suffered a transformer failure in mid-October 2025 when an internal fault caused winding damage and a tank rupture; the site is operating at ~350 MW/740 MWh while Wilson Transformer Company manufactures a replacement due Q3 2026. Consolidated Power Projects and Wilson completed a design review and remediation work is planned to return full SIPS capability by end-2026.

Decentralized training: Epoch AI models a 10 GW cluster assembled from 23 datacenters tied to spare capacity at U.S. gas plants and linked by a 4,800 km fiber ring. For a 72T-parameter (≈1.1 Pbit per sync) model they target ~250 ms all-reduce (6 Pbps per link + ~24 ms propagation) to keep network overhead <5%, with network cost ≈$410M versus $90B in datacenter build costs.

Data movement bottlenecks constrain LLM training scale: Epoch AI models show efficient, 3-month training runs break down past ~2e28 FLOP and hit a latency-imposed upper bound near 2e31 FLOP. The analysis models intra-GPU HBM bandwidth and inter-GPU communication across tensor/data/pipeline/expert parallelism, uses Chinchilla D=20N, and numeric examples (tlat=9 μs, nlayer=120, B=4M) to quantify limits and mitigations.

Epoch AI models four bottlenecks—power, chip manufacturing (packaging/HBM), data availability (text+multimodal), and the latency wall—using public forecasts and uncertainty ranges. They find on‑trend 4x/year compute growth could reach ≈2e29 FLOP by 2030 (median), with ~100M H100‑equivalents and ~6 GW power plausible; upper/lower bounds span ~1e28–1e32 FLOP depending on grid, fab, and data assumptions.