What Still Stays Scarce in AI Abundance?

Introduction

AI abundance will not be built from intelligence alone. Even if advanced AI makes expert reasoning, design and automation far cheaper, the physical world still has hard gates: electricity, grid connections, chips, cooling systems, water, copper, rare earth elements, advanced packaging, skilled construction labour and political permission to build. The practical question is not whether AI can help humanity bloom despite these constraints, but whether societies can expand clean energy and compute infrastructure fast enough without shifting scarcity, pollution or cost onto everyone else.

This matters because the most optimistic AI future depends on turning abundant intelligence into abundant medicine, education, manufacturing, climate repair and scientific discovery. That requires vast amounts of reliable computation. Yet the International Energy Agency projects global data-centre electricity consumption to roughly double by 2030, reaching about 945–950 terawatt-hours, or around 3% of global electricity demand. AI-focused data centres are growing faster still.[IEA]iea.orgEnergy demand from AIOur Base Case finds that global electricity consumption for data centres is projected to double to reach around 9… The bloom case therefore has a physical test: can clean power, grids, chips and materials scale alongside intelligence, or do they become the next chokepoints?

Abundance still has a power socket

The simplest mistake in AI abundance arguments is to treat software as weightless. A model may feel immaterial when it answers a prompt, but it runs on hardware housed in buildings connected to power grids and cooling systems. Training frontier models can draw intense bursts of electricity; running popular AI services at global scale turns inference — the everyday use of trained models — into a steady industrial load.

The IEA’s 2026 update sharpened this point. It reported that global data-centre electricity demand grew by 17% in 2025, while electricity use from AI-focused data centres rose by 50% in the same year. Its central projection sees data-centre electricity consumption rising from about 485 TWh in 2025 to about 950 TWh by 2030, with AI-focused data-centre consumption tripling over that period.[IEA]iea.orgExecutive summary – Key Questions on Energy and AIElectricity consumption from AI-focused data centres grew even faster, surging 50% i… That does not mean AI will consume most electricity. It means AI is becoming a large, fast-growing load in specific regions and grid nodes, where local constraints can bite long before global energy supply looks “too small”.

The United States shows why averages can mislead. A Lawrence Berkeley National Laboratory report for the US Department of Energy estimated that US data-centre load growth had tripled over the previous decade and could double or triple by 2028. The Department of Energy summary said data centres could account for 6.7% to 12% of US electricity consumption by 2028, depending on growth and efficiency assumptions.[The Department of Energy's Energy.gov]energy.govdoe releases new report evaluating increase electricity demand data centersdoe releases new report evaluating increase electricity demand data centers That range is wide because the future depends on uncertain factors: model efficiency, chip performance, demand for AI video and agents, cloud build-out, cooling choices, and how much computing can be shifted to places with cleaner or cheaper power.

This is why “AI uses too much energy” and “AI energy fears are exaggerated” can both be partly true. A single prompt may use little energy, especially on an efficient serving system. A 2025 Google paper measuring Gemini Apps text prompts in production reported a median energy use of 0.24 watt-hours per text prompt, alongside large year-on-year efficiency improvements in that serving stack.[arXiv]arxiv.orgarXiv Measuring the environmental impact of delivering AI at Google ScalearXiv Measuring the environmental impact of delivering AI at Google ScalePublished: August 21, 2025 But billions of prompts, larger multimodal outputs, long-running agents, model development, idle capacity and hardware manufacturing change the system-level picture. The bottleneck is not only the energy per task; it is the total number, size, latency requirement and location of tasks civilisation chooses to run.

For an AI bloom, the right benchmark is therefore not whether AI consumes electricity. So do hospitals, trains, factories and desalination plants. The right benchmark is whether the extra electricity unlocks broad human gains while speeding the clean-energy transition rather than slowing it. A civilisation that uses AI to cure disease, manage grids, design materials and remove dangerous labour may reasonably consume more electricity than today’s civilisation. But if the marginal power comes from delayed coal retirements, gas peaker plants, water-stressed cooling and higher household bills, the abundance story becomes much weaker.

The grid is often the bottleneck before energy itself

A data centre does not merely need clean energy in an accounting sense. It needs deliverable electricity at a specific place, at high reliability, with enough transmission, substations, transformers, switchgear and interconnection approval. This is where AI abundance runs into a slow, physical bureaucracy: grids are not installed at software speed.

The IEA notes that electricity generation to supply data centres is projected to rise from about 460 TWh in 2024 to more than 1,000 TWh in 2030 and 1,300 TWh in 2035 in its base case. It expects renewables to meet nearly half of additional demand over the next five years, with natural gas and coal also contributing and nuclear becoming more important later in the decade.[IEA]iea.orgOpen source on iea.org. That mix matters. If data-centre demand grows faster than clean generation and grids can connect it, AI can increase fossil-fuel use even while technology companies buy clean-energy certificates or sign renewable contracts.

The bottleneck is especially acute because large AI campuses can be enormous point loads. Some proposed sites are measured in hundreds of megawatts, and the largest can approach the scale of a power station. When many such projects cluster around existing fibre routes, tax incentives, cheap land or cloud hubs, they can overwhelm local planning assumptions. The World Economic Forum has described grid connectivity as a strategic bottleneck for AI, arguing that data-centre investment is moving faster than power-grid build-out.[World Economic Forum]weforum.orgelectricity data grid connectivity strategic bottleneck ai transformationelectricity data grid connectivity strategic bottleneck ai transformationPublished: May 2026

Ireland offers a warning for smaller grids. Data centres have become politically contentious because they compete with national climate commitments and local grid capacity. In 2026, ClientEarth challenged Ireland’s energy regulator over its approach to data-centre connections, noting that Ireland already had around 90 data centres in 2025 with more in the pipeline.[ClientEarth]clientearth.orgSource details in endnotes. The lesson is not that data centres are inherently bad. It is that digital infrastructure cannot be treated as separate from energy policy, housing, water, industrial strategy and climate law.

The United States shows a different version of the same problem. The IEA reported that slow grid connections were pushing some US data-centre developers towards onsite natural-gas generation, with satellite tracking suggesting that around one-fifth of such projects had begun land clearing or construction.[IEA]iea.orgenergy supply for aienergy supply for ai Reuters has also reported that the AI boom is contributing to pressure to keep older fossil-fuel “peaker” plants available in strained grids, raising concerns about pollution and local environmental burdens.[Reuters]reuters.comOpen source on reuters.com.

For the bloom thesis, this is a governance test. A society serious about AI-enabled abundance would not simply ask whether hyperscalers can secure power for themselves. It would ask whether grid upgrades, clean generation and storage are expanded in ways that also benefit households, public services and industry. Otherwise AI infrastructure can become a private island of abundance surrounded by public scarcity.

Clean power is necessary, but not sufficient

Technology companies have responded to AI’s energy challenge with renewable-energy purchases, nuclear deals, efficiency research and new data-centre designs. These efforts are real and important. They also reveal how hard the problem is.

Google’s 2025 Environmental Report said it reduced data-centre energy emissions by 12% in 2024 despite growing energy demand, replenished 4.5 billion gallons of water and procured more than 8 GW of clean energy.[Sustainability]sustainability.googlegoogle 2025 environmental reportgoogle 2025 environmental report Microsoft’s 2025 sustainability reporting continued to frame its targets around becoming carbon negative, water positive and zero waste by 2030, even as AI infrastructure growth made those goals harder to meet.[Microsoft]microsoft.comOpen source on microsoft.com. These commitments matter because the largest AI developers can shape energy markets through long-term power-purchase agreements, demand-response contracts and investment in next-generation energy.

The nuclear turn is one visible sign of the pressure. Amazon announced small modular reactor agreements in 2024 as part of its plan to address growing energy demand with carbon-free energy.[Amazon News]aboutamazon.comAmazon News Amazon signs agreements for innovative nuclear energyAmazon News Amazon signs agreements for innovative nuclear energyPublished: October 16, 2024 Google has pursued advanced nuclear partnerships, including a reported agreement involving Kairos Power and the Tennessee Valley Authority to supply power to data centres in the US Southeast.[New York Post]nypost.comSource details in endnotes.Published: August 20, 2025 Nuclear power is attractive for AI because it can provide firm low-carbon electricity rather than variable output. But nuclear projects also face long lead times, regulatory risk, cost uncertainty and local opposition, so they cannot be a near-term escape hatch for every data-centre cluster.

Renewables, batteries, geothermal, demand flexibility and transmission may often be faster. Yet clean-energy procurement can be misleading if it does not match when and where power is consumed. A data centre that buys enough renewable energy on an annual basis may still draw from a fossil-heavy grid during cloudy, windless or congested hours. The stronger standard is hourly, local and additional clean power: electricity that is generated close enough, at the right time, and added because of the buyer’s demand rather than merely reallocated on paper.

Cooling complicates the picture. High-density AI racks generate intense heat. Direct liquid cooling can reduce some cooling-energy burdens and support denser hardware, but adoption remains gradual; Uptime Institute’s 2025 cooling survey found that most operators still rely on traditional air cooling, even though high rack densities are pushing interest in direct liquid cooling.[intelligence.uptimeinstitute.com]intelligence.uptimeinstitute.comUI Field 181 Data center coolingUI Field 181 Data center coolingPublished: July 30, 2025 In water-stressed regions, evaporative cooling can trade lower electricity use for higher water consumption. The UK Government’s Sustainable ICT analysis has therefore urged careful siting, closed-loop cooling, rainwater use and avoidance of water-stressed areas.[sustainableict.blog.gov.uk]sustainableict.blog.gov.ukA I's thirst for waterA I's thirst for water

The environmental accounting should include both direct and indirect water use. A data centre may use little water onsite but still depend on electricity generation that consumes water elsewhere. Recent research in Joule estimated that AI systems alone could have a 2025 water footprint of hundreds of billions of litres, while carbon and water impacts vary heavily by energy mix, cooling design and location.[ScienceDirect]sciencedirect.comOpen source on sciencedirect.com. The practical implication is simple: “clean AI” is not a label that can be attached at the model level. It is an infrastructure outcome.

Chips are a physical supply chain, not just a budget line

Compute scarcity is often described as a shortage of GPUs, but GPUs are only the visible product at the end of a deeper supply chain. Advanced AI chips depend on leading-edge semiconductor fabrication, high-bandwidth memory, advanced packaging, substrates, lithography tools, chemicals, ultra-pure water, skilled engineers and reliable logistics. A bottleneck in any one layer can restrict the whole system.

The Semiconductor Industry Association’s 2025 report describes chips as essential building blocks for AI, quantum computing, communications and defence, and tracks major public and private efforts to rebuild semiconductor supply-chain capacity.[Semiconductor Industry Association]semiconductors.orgSource details in endnotes.Published: May 4, 2026 But expanding chip supply is not like adding more ordinary servers. Leading-edge fabs cost tens of billions of dollars, take years to build and depend on specialised equipment from a small number of suppliers. Advanced packaging capacity has also become strategically important because modern AI accelerators combine compute dies and high-bandwidth memory in tightly integrated packages.

This is why the AI hardware bottleneck keeps shifting. At one moment, the scarce input is finished accelerators. At another, it is high-bandwidth memory. Then it is CoWoS-style advanced packaging, substrates, power delivery or thermal management. Epoch AI estimated in 2026 that the four largest AI chip designers consumed roughly 90% of global advanced packaging and high-bandwidth memory supply in 2025, suggesting these were the binding constraints on AI chip production that year.[epochai.substack.com]epochai.substack.comAdvanced packaging and HBM — not logic diesAdvanced packaging and HBM — not logic diesPublished: March 12, 2026 While estimates of private supply chains should be treated cautiously, the direction is clear: AI scale is limited by industrial capacity that cannot be conjured instantly by software demand.

Energy efficiency is now changing chip design priorities. Reuters reported in May 2026 that TSMC, the world’s largest contract chipmaker, sees energy use forcing a rethink of AI chip design, with customers seeking performance gains without proportionate power increases. TSMC pointed to advanced packaging, 3D stacking and photonics as part of the route beyond simply shrinking transistors.[Reuters]reuters.comAI data centers are forcing dirty 'peaker' power plants back into serviceAbout 60% of the oil, gas, and coal plants scheduled for shutdown in PJM territory in 2025 have now had their retirements delayed or canc…Published: December 23, 2025 This is important for AI bloom because the relevant quantity is not raw model size. It is useful intelligence per joule, per pound of capital, per tonne of materials and per unit of social disruption.

There is also a distribution issue. If compute remains scarce, expensive and geopolitically concentrated, the benefits of advanced AI may be captured by a small number of companies and states. Scarce compute can make safety testing harder for independent researchers, limit public-interest AI, raise barriers for universities and poorer countries, and give infrastructure owners unusual leverage over the future. Compute governance is therefore not just an economic question. It shapes who can build, audit, regulate and benefit from advanced systems.

Materials make “digital” abundance visibly physical

Data centres and AI chips require large material flows. Copper carries electricity through grids, substations, buildings and server racks. Rare earth elements are used in motors, magnets and parts of the wider clean-energy system. Lithium, nickel, graphite and cobalt matter for batteries and electrification. Ultra-pure silicon wafers, specialty gases, photoresists and advanced chemicals matter for chipmaking. Concrete, steel, transformers and cooling equipment matter for the buildings and grid upgrades around compute.

The IEA’s Global Critical Minerals Outlook 2025 projects strong growth in demand for energy-transition minerals. In its stated-policies scenario, lithium demand grows fivefold by 2040, graphite and nickel demand double, cobalt and rare earth demand rise by 50–60%, and copper demand grows by around 30%.[IEA]iea.orgOpen source on iea.org. AI is not the only driver; electric vehicles, grids, renewables and batteries are larger parts of the minerals story. But AI adds a fast-growing, high-value claimant on the same supply chains needed for decarbonisation.

Copper is a useful example because it links AI, grids and clean energy. Data centres need copper inside the facility, but the bigger demand may come from transmission lines, transformers and generation needed to serve them. If AI data-centre growth accelerates grid expansion, it can indirectly increase copper demand even when the data-centre building itself is only one slice of the total. The constraint is not simply whether there is enough copper in the Earth. It is whether mining, refining, recycling and permitting can expand without unacceptable environmental and social damage.

Mining and processing are also politically concentrated. Some minerals are extracted in one set of countries and refined in another, creating strategic vulnerabilities and ethical risks. Supply concentration can make AI infrastructure vulnerable to export controls, conflict, forced labour concerns, environmental harm or price spikes. A bloom future built on hidden extractive damage would be morally unstable: it would reduce scarcity for some by intensifying burdens elsewhere.

The semiconductor supply chain has its own material fragility. A 2025 Capgemini report on semiconductors in the AI era argued that limited availability of inputs such as silicon wafers, inert gases and rare earth elements affects a large share of semiconductor supply chains, and recommended material substitution, diversification and supply-chain resilience.[Capgemini]capgemini.comThe semiconductor industry in the AI eraThe semiconductor industry in the AI eraPublished: April 16, 2025 The exact figures in consultancy reports should not be treated as physical law, but the strategic point is sound: AI capability rests on a stack of materials whose supply is neither automatic nor evenly governed.

Efficiency helps, but rebound is real

One tempting answer is that AI will simply become more efficient. That is partly true. Chips improve. Models are compressed. Inference systems batch requests, cache results and route easy tasks to smaller models. Software improvements can deliver dramatic reductions in energy per useful output, as Google’s production measurements suggest for text prompts.[arXiv]arxiv.orgarXiv Measuring the environmental impact of delivering AI at Google ScalearXiv Measuring the environmental impact of delivering AI at Google ScalePublished: August 21, 2025

But efficiency does not automatically reduce total resource use. When a technology becomes cheaper and more capable, people often use much more of it. This rebound effect is especially plausible for AI. Cheaper inference could enable always-on agents, synthetic video, personalised tutoring, automated software generation, robot control, scientific search and enterprise monitoring. Many of those uses may be valuable. They may also multiply demand faster than efficiency reduces energy per task.

The useful distinction is between “wasteful demand” and “transformative demand”. Wasteful demand includes low-value spam, manipulative advertising, disposable synthetic media, pointless overuse of large models for small tasks, and duplicated training runs conducted mainly for competitive signalling. Transformative demand includes medical research, climate modelling, grid optimisation, education, accessibility, safety testing, public-interest science and automation of dangerous work. Both consume compute. A serious AI abundance strategy would try to price, regulate and design infrastructure so the second category gets priority over the first.

Several mechanisms can help:

Research on AI inference as relocatable electricity demand has explored how some workloads can be shifted across geography when latency budgets allow, but also notes that migration costs, data locality, legal constraints and capacity limits can sharply reduce the benefit.[arXiv]arxiv.orgarXiv Measuring the environmental impact of delivering AI at Google ScalearXiv Measuring the environmental impact of delivering AI at Google ScalePublished: August 21, 2025 In other words, flexibility is valuable, but not magic. A hospital AI assistant, a robot in a factory and a live translation system cannot always wait for windy weather in another region.

AI can also attack the bottlenecks

The strongest version of the AI bloom argument is not that AI gets a free pass on energy and materials. It is that AI may help solve the very constraints it intensifies. This is the key tension: AI creates new demand for clean power, chips and minerals, but it can also improve energy systems, materials discovery and industrial design.

In electricity systems, AI can improve forecasting, maintenance, dispatch, grid planning and renewable integration. The IEA argues that AI can optimise complex energy systems and, in a widespread-adoption case, could yield up to USD 110 billion in annual savings by 2035 from power-plant operations and maintenance through avoided fuel use and lower costs. It also highlights AI’s role in integrating more renewable electricity into grids.[IEA]iea.orgglobal critical minerals outlook 2025global critical minerals outlook 2025Published: May 2025 These gains are not automatic; grid operators are rightly cautious about opaque algorithms in critical infrastructure. But better forecasting, anomaly detection and control could let existing networks carry more clean power safely.

AI is also accelerating materials science. Google DeepMind’s GNoME work reported predictions of 2.2 million new crystal structures, including about 380,000 stable candidates, with possible relevance to batteries, photovoltaics, superconductors and computing.[Google DeepMind]deepmind.googleGoogle Deep Mind Millions of new materials discovered with deep learningGoogle Deep Mind Millions of new materials discovered with deep learningPublished: November 29, 2023 A 2025 Energy-GNoME project applied AI-driven screening to energy applications and highlighted tens of thousands of candidate materials for thermoelectrics, photovoltaics and batteries.[ScienceDirect]sciencedirect.comOpen source on sciencedirect.com. Most predicted materials will not become commercial products; synthesis, stability, cost, toxicity and manufacturability remain hard filters. Still, the search space is so large that better computational triage can matter.

Fusion research provides another example of AI as a control tool rather than just a consumer of energy. DeepMind and the Swiss Plasma Center used deep reinforcement learning to control plasma shapes in a tokamak, a result published in Nature and presented as a way to handle high-dimensional, high-frequency plasma-control problems.[Nature]nature.comOpen source on nature.com. Fusion is not a near-term answer to data-centre demand, and it should not be invoked as a vague future rescue. But it illustrates a broader mechanism: advanced AI may improve the design and operation of complex clean-energy technologies that humans currently struggle to control or optimise.

The same pattern applies to batteries, catalysts, heat pumps, power electronics, carbon capture, cement, steel and recycling. AI can help search designs, simulate processes, automate laboratories and optimise factories. The catch is that scientific acceleration still has to pass through the slow world of experiments, standards, permitting, supply chains, finance and deployment. AI can compress parts of discovery; it cannot abolish every bottleneck between a promising material and a gigafactory.

The political economy of compute will shape who blooms

Clean energy and chips are not only technical constraints. They are sources of power. Whoever controls cheap compute, reliable electricity and advanced manufacturing can shape markets, research agendas and geopolitical leverage. This matters because an AI bloom is supposed to expand human flourishing broadly, not merely give a few actors extraordinary productive capacity.

If compute is scarce, the owners of large clusters can decide who gets access, on what terms and under what surveillance. If grid upgrades are paid through public bills while profits accrue privately, communities may see AI infrastructure as extraction rather than progress. If data centres receive tax breaks but provide few local jobs, consume water and raise electricity prices, political backlash is predictable. Uptime Institute’s 2025 global survey describes an industry facing rising costs, worsening power constraints and challenges in meeting AI demand, while also dealing with supply-chain delays and public opposition.[Uptime Institute]intelligence.uptimeinstitute.comUI Field 181 Data center coolingUI Field 181 Data center coolingPublished: July 30, 2025

The local bargain matters. A data centre can be more legitimate if it funds additional clean power, pays for required grid upgrades, avoids water-stressed regions, shares waste heat where practical, supports local tax bases, discloses environmental impacts and provides community benefits without greenwashing. It is less legitimate if it externalises grid costs, relies on fossil backup beyond emergencies, hides water use, or uses political influence to bypass planning scrutiny.

For poorer countries, the risk is different. AI abundance could widen global inequality if compute, chips and clean power remain concentrated in rich countries and a handful of firms. Many emerging economies already face higher costs of capital for clean-energy projects, weaker grids and less access to advanced hardware. If they must import AI services while exporting raw materials or hosting polluting infrastructure, the bloom story becomes another version of unequal development.

A fairer path would treat compute access as part of development infrastructure. That could mean public-interest compute clouds, regional clean-energy data-centre partnerships, open scientific models, shared safety-evaluation resources, and financing that helps low- and middle-income countries build both digital and electrical capacity. The point is not charity. A world with broader access to AI-enabled science, education, health and energy innovation is more resilient and more legitimate.

What would count as success?

The physical bottleneck problem is not solved by one breakthrough. It requires a portfolio of tests that are harder to game than broad claims about “green AI” or “abundant intelligence”.

A credible AI abundance pathway would show progress on at least five fronts. First, data-centre growth would be matched by additional clean electricity and grid capacity, not merely annual certificate purchases. Second, AI developers would report comparable energy, water, carbon and hardware-lifecycle metrics, including model development and idle capacity rather than only final training runs or selected prompts. Third, chip supply would become more efficient and more geographically resilient without creating new environmental sacrifice zones. Fourth, compute would become accessible enough for universities, public agencies, safety researchers and poorer countries to use advanced AI for public goods. Fifth, AI itself would measurably speed clean-energy deployment, materials discovery, grid operation and industrial decarbonisation.

There are also warning signs. One is a rush towards onsite fossil generation because grids cannot connect clean power quickly enough. Another is local opposition driven by water stress, noise, diesel backup, land use or electricity prices. A third is compute concentration so extreme that only a few companies can train, test or deploy the most capable systems. A fourth is rebound: efficiency improvements swallowed by low-value uses while high-value public applications remain underfunded.

The deeper lesson is that post-scarcity is the wrong phrase if it implies an end to physical limits. A better phrase is scarcity transformation. AI may reduce some of the scarcities that most limit human life — expert knowledge, scientific search, medical discovery, design capacity, dangerous labour — while increasing pressure on others: power, chips, minerals, water, land, grid connections and institutional trust. Whether that trade becomes a bloom depends on choices made now.

The real constraint is coordination

Clean energy, compute and materials sit at the boundary between technological optimism and practical civilisation-building. Advanced AI could help design better batteries, manage renewable-heavy grids, discover new materials, optimise factories and accelerate clean-energy research. It could also intensify fossil demand, deepen chip geopolitics, concentrate power and move environmental burdens onto communities with the least bargaining power.

The answer is not to freeze AI development until the physical world is perfect. Nor is it to assume that intelligence will automatically dissolve every constraint. The serious path is to build AI infrastructure as if it were part of the public energy and industrial system: planned with grids, water, communities, safety, access and climate goals in view.

An AI bloom worthy of the name would make intelligence more abundant while also making the material foundations of civilisation cleaner, fairer and more resilient. That is a much harder project than scaling models. It is also the project that determines whether AI abundance becomes broad human flourishing or merely a new competition for scarce power.

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Endnotes

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Link:https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai

2. Source: iea.org
Link:https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary

3. Source: iea.org
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4. Source: energy.gov
Title: doe releases new report evaluating increase electricity demand data centers
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5. Source: reuters.com
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Link:https://arxiv.org/abs/2508.15734
Published: August 21, 2025

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Title: energy supply for ai
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Additional References

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65. Source: eta-publications.lbl.gov
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66. Source: aixenergy.io
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67. Source: avidsolutionsinc.com
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68. Source: b2match.com
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