Tag: AI Infrastructure

  • The Data Center Casino: Why America Needs Elected Technology Leaders Now

    By the Founders, Department of Technology | San Diego, California | November 13, 2026

    America needs more data centers. That part is not in dispute. What is in dispute is who decides where they go, who pays for them, and whether anyone is looking at the whole board.

    If the United States wants to remain the world leader in artificial intelligence, it has to build the computing capacity, and the power to run it. We start from that premise. But for several years, officials in both major parties, in red states and blue, have treated new data centers less like public infrastructure and more like a slot machine: pull the lever, hope for a jackpot of investment, tax revenue and ribbon-cuttings, and worry later about who covers the losses.

    The question that has been asked is “how much money can be made?” The question that should be asked is “what is the best outcome for the community that has to live next to this?”

    Three risks pull in three different directions. Build too fast, and we strain an aging grid and inflate a financial bubble. Build too slow, and other countries may make our lead obsolete. Ignore what AI could do to employment, and we erode the tax base a modern society runs on. No one in government is currently accountable for all three at once.

    The casino logic

    Thirty-eight states offer tax incentives to data centers, and at least 28 of them considered scaling those incentives back this year, according to the National Conference of State Legislatures. At least ten states forgo more than $100 million a year in these breaks, with Texas and Virginia each losing as much as $1 billion annually, according to Good Jobs First, as reported by Broadband Breakfast.

    In Ohio, Policy Matters Ohio reports the break cost state and local governments about $2 billion last year, far more than the state had forecast, which led Republican Governor Mike DeWine, who had previously vetoed a repeal, to order a temporary pause.

    The backpedaling is bipartisan, which is exactly the point. In Virginia, 21 Democrats and seven Republicans in the state Senate voted to curtail a 5.3% tax break. Kentucky’s Democratic governor, Andy Beshear, has called for ending his state’s incentives, and Ohio, Arizona, Nebraska and Illinois have paused theirs.

    When lawmakers of both parties are reversing course, the original approach was not a partisan mistake. It was a structural one: the deals were made one project at a time, often in secret (as we noted in our open letter, 25 of 31 Virginia local governments involved in data center development had signed NDAs), and no one owned the cumulative consequences.

    Build too fast: the grid and the bubble

    Our electrical grid is where the cost of speed shows up first. In PJM, the nation’s largest grid market, wholesale power prices jumped almost 76% in the first quarter of 2026 compared with a year earlier, and the market monitor said the price effects on customers were very large and not reversible, according to E&E News.

    Capacity auction prices rose roughly tenfold in two years, from about $29 to $329 per megawatt-day, and the December 2025 auction fell short of its reliability target for the first time, per one analysis of PJM’s results. Ordinary ratepayers are paying for capacity built to serve some of the wealthiest companies in the world.

    Then there is the money behind the buildout. Hyperscalers will spend about $750 billion this year, and they are borrowing heavily to do it, while economy-wide statistics still show little or no productivity growth from AI. Jefferies warns that AI investment was the main driver of U.S. economic growth last year after consumer spending, so a sudden collapse would leave the economy exposed. And The Economist notes that more than half the spending has gone to servers and specialized chips that become obsolete within a few years. A technology can be transformative and still be overbuilt. If that happens, the stranded assets and the unpaid debts will not stay on Wall Street.

    Build too slow: the electron gap

    The opposite risk is just as real. The United States leads in advanced chips but is short on power, while China has the power and is short on chips. BloombergNEF estimates China will add more than six times as much generating capacity as the U.S. over the next five years. Morgan Stanley has forecast a 44-gigawatt shortfall for U.S. data centers within three years, roughly the summertime capacity of New York State. According to calculations reported by The Washington Post, Chinese data centers pay about a third of what American ones do for electricity.

    We cannot answer that gap with moratoriums and lawsuits alone, and we cannot answer it with secrecy and subsidies either. Speed that costs public trust is not speed. As voters showed on November 3, when they favored limits in 40 of 41 local data center measures, communities will stop projects they feel were done to them rather than with them.

    Ignore the jobs: the tax base

    The third risk is the quietest. A RAND analysis found that 84 percent of federal revenue in 2024 came from individual and payroll taxes, both collected from labor income. Because income is concentrated in high-paid white-collar jobs, replacing those roles with AI would hit revenue harder than the headcount suggests, and higher corporate profits would not make up the difference unless corporate tax rates roughly doubled.

    The same logic applies closer to home. New York City’s Comptroller built five AI scenarios for the city’s finances, put a combined 50 percent probability on the three with worse outcomes for jobs, growth or tax revenue, and recommended building a reserve fund to 16 percent of annual tax revenue. Every county and state should be asking the same question before the layoffs arrive, not after.

    Why we need elected technology leaders

    Look at who currently holds a piece of this problem. Utility commissions watch rates. Economic development offices watch ribbon-cuttings. Treasurers watch revenue. Each sees one slice, and none is answerable to voters for the combination. That is how a state can win a data center, lose a billion dollars a year, raise its residents’ power bills and weaken its future tax base, all through decisions that each looked reasonable on its own.

    We are calling for elected technology leaders at the local, county, state and federal levels, with one job: to weigh speed, grid capacity, public cost and long-term fiscal health together, in public. In practice that means:

    • Public plans before approvals. Grid, water and fiscal impact published before a project is approved, not after.
    • No secret deals. Public bodies should not sign NDAs that keep residents from learning what their own governments agreed to.
    • Large users pay for what they trigger. Grid upgrades and capacity costs assigned to the facilities that cause them, not spread across households.
    • Incentives with accountability. Tax breaks tied to measurable jobs and revenue, with clawbacks if the promises are not met.
    • Fiscal readiness for AI displacement. Scenario planning and reserves at every level of government.

    They should be elected, not appointed, because the tradeoffs here are value judgments about whose power bills, whose land and whose jobs, and those belong to voters. And they are needed now because the contracts are being signed now.

    The case against our case

    A fair reading includes the strongest objections.

    • Incentives may be what brings projects at all. The Data Center Coalition’s Dan Diorio argues that revenue estimates overlook the chance a project would never have been built without exemptions, and notes that states with the most growth, including Virginia, Texas and Pennsylvania, offer them, while Colorado, which does not, has less (S&P Global).
    • The electron gap may be overstated. The Brookings Institution cautions that no one knows how many data centers will be needed or how fast chips will become more efficient, and that worries from builders are not the same as geopolitical shocks (Brookings).
    • Job forecasts are uncertain. One UK think tank’s scenarios range from eight million jobs lost to none at all (IPPR), and New York’s Comptroller itself says it is hard to tie current trends to AI with certainty.
    • Another elected office is another layer. Critics may reasonably prefer strengthening utility regulators and legislatures over creating new positions. Our answer is that each existing body owns only a slice, but that is a debate worth having in public.

    Start the conversation

    Neither the casino nor the moratorium is a plan. The country needs people whose job it is to build enough, build in the right places, make the builders pay their share, and prepare for what comes after. That job does not exist yet. We are asking every candidate and every sitting official, of every party, to start the discussion about creating it, at department.technology/.

  • Voters Didn’t Reject AI. They Rejected Building It Backward


    By the Founders, Department of Technology | San Diego, California | November 9, 2026


    Voters Didn’t Reject AI in 2026. The ever-increasing number of paid memberships for OpenAI, Anthropic (Claude AI), Google Gemini, and others has proved this to be the case.

    They Rejected Building It Backward.

    On November 3rd, voters across ten states said no to data centers almost every time they were asked. The reason is not that they oppose AI. It is that they have watched the buildout run ahead of the power grid, and they suspect the people steering it are not putting the public first.

    In Ashville, Ohio, the village council had already approved an agreement with a data center developer. Residents petitioned to put it on the ballot anyway. On Election Day, 82% of voters rejected the deal, according to Northeast Ohio reporting by Fox 8.
    Ashville was not an outlier. Ballotpedia counted 41 local data center measures on the November 3rd ballot across 10 states, with Ohio accounting for 21. Voters favored limiting data center development in 40 of the 41. The one exception was Harwood, North Dakota, where voters defeated a proposed review requirement. Voters in Granville, Ohio, approved a ban by 84%. In Urbana, 77% backed one.

    This was not a one-day mood. A Heatmap poll reported by Forbes found more than 70% of Americans would oppose a data center planned for their area, with rising electricity prices the top reason.
    Many voters may not weigh how much national AI leadership depends on new computing capacity. That is a fair criticism, and we take it seriously below. But the voters who showed up made a different judgment, and it deserves a hearing: they believe this buildout is being driven first by profit, with national strategy a distant second, and that their concerns about power bills and the environment were treated as obstacles rather than inputs.
    The comparison with China shows why that suspicion is not irrational.

    What China Did First
    China and the United States are in a race for AI leadership, but they are not running it from the same starting line.
    According to Inside Climate News, China added an estimated 540 gigawatts of generating capacity in 2025, more than 80% of it renewable. The United States added about 54 gigawatts, roughly 60% renewable. Al Jazeera reports that China already generates more than twice as much electricity as the US, and that BloombergNEF expects China to add more than six times as much generating capacity over the next five years.

    Transmission, the high-voltage lines that carry power from where it is made to where it is used, tells the same story. Grid Strategies estimates the US needs roughly 5,000 new miles of high-capacity transmission per year but has averaged only about 600 since 2017, according to Yale Climate Connections. China, by the same report, has been building over 1,000 miles of ultra-high-voltage lines per year. In January, China’s State Grid announced a CNY 4 trillion (about $580 billion) investment plan for 2026 to 2030, up 40% from the prior plan, including 15 new ultra-high-voltage lines, according to Enerdata.

    Getting connected is the other bottleneck. A ChinaTalk analysis finds that the time for a US generation project to connect has grown from under two years in the early 2000s to over four years for projects built from 2018 to 2024. Because China plans generation, transmission, and demand together under state direction, the American concept of a years-long interconnection queue for large loads barely exists there.

    The US leads on the other side of the equation. Stanford’s AI Index counts an estimated 5,427 US data centers in 2025 versus 449 in China. As one analyst quoted by Al Jazeera framed it, the US has the chips and is short on power, while China has the power and is short on chips.

    Cost is where the picture gets less lopsided. Reporting from bne IntelliNews puts Chinese industrial electricity rates roughly in line with the US, at about $0.08 to $0.11 per kilowatt-hour, while Chinese households pay about $0.08 versus $0.15 to $0.17 for American households. The Financial Times reported that Beijing is subsidizing power for data centers using domestic chips down to about 5.6 cents per kilowatt-hour, against a US industrial average of 9.1 cents.

    The Warnings Were Public
    Here is the part voters understand best. The power shortfall was not a secret.

    In its Long-Term Reliability Assessment, the North American Electric Reliability Corporation (NERC) raised its ten-year summer peak demand forecast sharply and said new data centers account for most of the projected growth, with several regions at high risk of supply shortfalls by the end of the decade. This spring, NERC issued a Level 3 alert, its highest tier, over large computational loads like data centers, and found that grid operators cannot reliably model how 77% of operating data center load behaves during grid disturbances, according to reporting by SAN and Utility Dive.

    Grid operators are also being overwhelmed at the front door. Texas’s ERCOT built its process for a large-load queue of 40 to 50 projects, then received 225 new interconnection requests in 2025 alone, about 70% of them data centers, according to the Texas Tribune. ERCOT itself cautions that many requests are speculative and its demand forecast is probably inflated. That caution cuts both ways: it means some projects may never be built, and it also means officials are approving and planning against numbers nobody can fully trust.
    We want to be precise. We are not claiming that officials privately concealed a known shortfall. What the public record shows is that independent grid reliability authorities published escalating warnings, and in many places projects advanced anyway, sometimes under nondisclosure agreements and sometimes over local objections. When voters see that sequence, and then see their bills, they draw a conclusion.

    Why the Bill Concern Is Real, and Why It Is Contested
    The electricity price argument is the most contested part of this debate, so it deserves care.

    In PJM, the grid operator serving 13 states and Washington, DC, capacity auction prices rose from $28.92 per megawatt-day for 2024/25 to $329.17 for 2026/27, roughly tenfold. PJM’s independent market monitor attributed 63% of the increase in the 2025/26 auction to data center demand, about $9.3 billion in added costs recovered from ratepayers, according to analysis by the Institute for Energy Economics and Financial Analysis.
    But capacity charges are only about 10% to 20% of a typical bill, as FactCheck.org notes, and several studies find no clear nationwide link between data centers and household rate increases. A Rutgers policy lab analysis found no statistically significant local bill effect yet, while warning that “not yet” is not the same as “nothing to worry about.” FactCheck.org also cites an estimate that capacity increases added about $18 per month to the average home bill in western Maryland and $16 in Ohio between mid-2025 and mid-2026, with 63% of that tied to data centers.

    The honest summary: the bill effect is real and documented in some regions, overstated in others, and likely to grow if the cost-allocation rules do not change. Voters are reacting to the direction of travel, and the direction is up.

    “Doesn’t Slowing Down Hand China the Win?”
    This is the strongest objection, and it is not frivolous. If China is building power at this scale and pricing it for AI, then any delay in the US could cost real strategic ground.

    But the China comparison cuts against the current approach, not for it. The binding constraint on American AI is no longer chips alone. It is power, transmission, and public consent, and the backlash of November 3rd shows what happens when a project skips the last of those. Projects imposed on unwilling towns get voted down, and every vote down is a delay no strategist planned for.

    We also want to avoid the lazy conclusion that the US should copy Beijing. China’s model rests on centralized, state-directed planning that the US system, with its many jurisdictions and its commitment to local consent, cannot and should not replicate. China’s buildout has its own costs: Oxford Institute for Energy Studies notes it added as much as 80 gigawatts of new coal-fired power in 2025, and Al Jazeera reports a gap between China’s data center capacity and how much of it is actually used. The lesson is narrower and more useful: China treats the grid as infrastructure to be built before the load arrives. We have been building the load and hoping the grid catches up.

    The Fix: Grid First, Public Accountability Always
    The way out is not a moratorium on AI infrastructure and not a free pass for developers. It is a different order of operations. The framework at Department of Technology proposes layered accountability: a confirmed federal Secretary of Technology, and elected technology leaders at the state, county, and local level who answer to the voters whose bills and water supply are on the line. Concretely:
    Grid first. No major data center approval without a utility- or grid-operator-certified plan covering power supply, transmission, and interconnection timing.

    Make the large loads pay. Require large-load tariffs so the full cost of new generation and grid upgrades lands on the facilities that cause it, not on residential ratepayers. Pennsylvania’s governor has moved this direction with an executive order requiring developers to build, bring, or buy their own capacity, and a Republican challenger has pressed for a ratepayer-protection moratorium. This is not a one-party issue.

    Publish the grid impact. Before any rezoning or tax abatement, publish projected power demand, water use, and cost allocation in plain language, and limit NDAs on projects that use public land, money, or incentives.

    Fund the wires. Treat transmission and interconnection reform as national AI infrastructure, because it is. If the United States wants to out-build China on compute, it has to stop being out-built on the grid.

    What Each of Us Can Do
    Congress: Prioritize transmission permitting and interconnection reform, and pass transparency rules for large-load projects.
    Statehouses and utility commissions: Adopt large-load tariffs and grid-readiness tests before approvals, and require public notice before rezoning or tax breaks.
    Candidates and local officials: Answer two questions in public. Who pays for the power this project needs? Where is the grid study that says it can be delivered?

    Voters: Ask every candidate those same two questions, at every level, before the next ballot.

    The Choice
    The United States can win the AI race, but not by asking communities to host the buildout on faith. China’s advantage is not that it has better engineers. It is that it built the power first. If we want national AI leadership, we have to earn it the way voters just demanded: build the grid, make the costs transparent, and put people accountable to the public in charge of the decisions.

    Summary
    Voters in 40 of 41 local data center ballot measures on November 3rd chose limits, and polls show most Americans oppose a data center near them. The backlash is not a rejection of AI. It reflects a buildout that outran the grid, a shortfall that grid reliability authorities publicly warned about, and a bill impact that is real in some regions and contested in others. China built power and transmission first, at a scale and speed the US has not matched, though its model is not one we should copy. The answer is to put the grid first, make the large loads pay, and make the decisions accountable to the people who live with them.
    Sources

  • Powering the AI Revolution

    Why America’s Data Center Future Depends on the Electrical Grid

    America’s race to lead the artificial intelligence revolution will not be won by semiconductor technology and investment alone. It will also depend on where data centers are built, whether the electrical grid can deliver the power they need, and whether communities trust the decisions being made in their backyards.

    Artificial intelligence is rapidly becoming one of the most important technologies shaping America’s economic and national security future. Companies are investing billions of dollars in advanced computing infrastructure, developing increasingly powerful AI models, and building enormous data center campuses to support the next generation of artificial intelligence.

    But there is a fundamental problem that deserves much more attention: AI cannot scale without electricity, and electricity cannot be delivered without adequate infrastructure.

    The United States may have world-leading technology companies, advanced semiconductor manufacturers, and enormous investment capital. However, none of these advantages guarantees that the country can build and operate enough AI data centers to maintain its competitive position.

    The challenge is increasingly about energy availability, grid location, transmission infrastructure, and reliability.

    As we discussed in our recent article, America Won’t Lose the AI Race to China’s Chips. It Will Lose It to Distrust, America’s ability to lead in artificial intelligence will depend on more than technological innovation. It will also depend on whether the country can build the physical infrastructure required to support that innovation while maintaining public confidence.

    The AI Infrastructure Problem Starts With Electricity

    Modern AI data centers are fundamentally different from traditional commercial buildings.

    They house thousands of specialized processors, high-performance networking equipment, advanced cooling systems, and supporting infrastructure. These systems require substantial amounts of electricity, often operating continuously to train AI models and deliver computing services.

    As AI adoption expands, so does the demand for computing capacity. That creates a corresponding demand for electricity.

    The challenge is that electrical infrastructure cannot necessarily expand as quickly as technology investment.

    A technology company can announce a multibillion-dollar data center project, acquire land, order computing equipment, and begin construction. But if the local electrical grid cannot provide the required power, the project may experience significant delays before it can become operational.

    This creates a new reality for data center developers: securing electricity must happen before construction decisions are finalized, not after the buildings are completed.

    The availability of power should be one of the first considerations in determining where America’s next generation of AI infrastructure is built.

    Grid Location: Building Where Electricity Is Available

    Historically, data center developers have considered land prices, tax incentives, fiber connectivity, proximity to major markets, and access to skilled labor when selecting locations.

    Those factors remain important, but electricity availability is becoming an increasingly important consideration.

    A site may offer inexpensive land and favorable development incentives but still be unsuitable for a large AI data center if the surrounding electrical infrastructure cannot support its requirements.

    Developers must evaluate several factors before selecting a location:

    • Existing generation capacity: Is sufficient electricity available within the regional power system?
    • Substation capacity: Can nearby substations accommodate the additional electrical load?
    • Transmission access: Can electricity be delivered to the property without major infrastructure limitations?
    • Grid congestion: Are existing transmission and distribution systems already constrained?
    • Future expansion: Can the location support additional computing capacity as AI requirements increase?

    The distinction between national electricity generation and locally available electricity is particularly important.

    China has substantially more total electricity-generation capacity than the United States. However, national generation capacity does not automatically mean that electricity is available at every location where a data center might be constructed.

    The same applies to the United States.

    Having sufficient electricity somewhere within a country does not mean that the power can be delivered economically and reliably to a particular data center campus.

    This is why grid location should become a primary consideration in national AI infrastructure planning.

    Transmission Infrastructure: The Bottleneck Between Power Generation and Computing

    Electricity generation is only one part of the equation.

    Power must travel through transmission lines, substations, transformers, and distribution networks before reaching the facilities that consume it.

    These systems require significant investment, careful engineering, regulatory coordination, and time to construct.

    For AI data center developers, transmission infrastructure can become one of the biggest obstacles to completing a project.

    Even when electricity generation is available, existing infrastructure may not have sufficient capacity to deliver the required power.

    Addressing these limitations may require:

    • New high-voltage transmission lines.
    • Substation construction and expansion.
    • Additional transformers and electrical equipment.
    • Improvements to local distribution networks.
    • Coordination between utilities, developers, and regional grid operators.
    • Long-term planning for additional electrical demand.

    These are not minor construction details. They are essential components of the infrastructure supporting America’s AI economy.

    The federal government, state governments, utilities, and private developers must coordinate their efforts to ensure that electrical infrastructure keeps pace with computing demand.

    This is also where the proposed Department of Technology could play an important role.

    As outlined in our article, How Our Proposed Department of Technology Could Supercharge the Stargate Project, large-scale AI infrastructure initiatives require coordination across federal, state, and local governments.

    Energy planning should be part of that coordination.

    Reliability: AI Data Centers Cannot Operate on Uncertain Electricity

    Having access to electricity is not enough. AI data centers also need reliable electricity.

    AI training and inference workloads can involve thousands of processors operating simultaneously. An unexpected interruption in power can disrupt computing operations, interrupt services, and create expensive recovery requirements.

    For companies investing billions of dollars in computing infrastructure, electrical reliability is a business-critical issue.

    Data center developers must consider the resilience of the entire electrical system, including:

    Grid redundancy: Multiple electrical supply paths can reduce the risk associated with individual transmission failures.

    Backup generation: On-site generators can provide emergency electricity when utility service is interrupted.

    Battery energy storage: Batteries can help manage peak demand, provide short-duration backup power, and support grid stability.

    Uninterruptible power supplies: UPS systems provide immediate protection for critical computing equipment during electrical disturbances.

    Microgrids: Properly designed microgrids can integrate utility electricity, on-site generation, and energy storage to improve operational resilience.

    These technologies should be considered part of the original development strategy rather than expensive additions after a project experiences electrical limitations.

    Reliability also has implications beyond individual facilities. Large data center developments must be evaluated in the context of the surrounding electrical system and the communities that depend on it.

    A project should not simply secure enough electricity to operate its own equipment. Its development should also account for the impact of additional demand on the regional power system.

    Energy Storage and Renewable Energy Must Be Part of the Solution

    Expanding electricity generation and transmission will be essential, but the United States should also consider how energy storage and distributed generation can support future AI infrastructure.

    Solar, wind, nuclear energy, natural gas generation, and battery storage can all play different roles in meeting electricity demand.

    The challenge is developing an energy strategy that balances availability, affordability, reliability, and environmental considerations.

    Energy storage deserves particular attention.

    Our proposal for California’s NEM 4.0 emphasizes the importance of battery storage in supporting grid stability, improving energy management, and helping balance electricity supply and demand.

    The same broader principle applies to AI infrastructure.

    Battery systems can help facilities manage periods of peak demand, provide short-duration backup power, and potentially participate in programs that support the electrical grid.

    However, batteries are not a substitute for adequate generation and transmission capacity. They are one component of a larger energy infrastructure strategy.

    The United States should pursue a diversified approach that combines reliable generation, modern transmission networks, energy storage, and responsible demand management.

    The Cost of AI Infrastructure Should Not Be Shifted to Local Communities

    There is another issue that cannot be ignored: who pays for the electricity infrastructure required to support the AI industry?

    New data centers can require substantial investments in electrical infrastructure. Utilities may need to upgrade substations, expand transmission networks, and improve local distribution systems.

    Those investments raise important questions about how costs are allocated.

    If infrastructure upgrades primarily benefit a private data center developer, should ordinary electricity customers bear the financial burden?

    How should utilities account for the additional demand when determining electricity rates?

    What protections should exist to prevent households and small businesses from subsidizing infrastructure built primarily to support large technology companies?

    These questions deserve transparent answers before major development agreements are finalized.

    The public should not be expected to accept higher electricity bills without understanding why those costs are increasing and who is responsible for paying for the infrastructure.

    This is one reason public opposition to data center development should not be dismissed as simple resistance to technological progress.

    As we discussed in our recent analysis of America’s AI infrastructure expansion, communities have legitimate concerns about electricity costs, water consumption, land use, and the lack of transparency surrounding some development agreements.

    A successful AI infrastructure strategy must address these concerns rather than treating them as obstacles to be overcome.

    Public Trust Is an Infrastructure Requirement

    Data center construction is often presented as a private-sector investment that brings jobs, economic development, and technological innovation to a community.

    But these projects can also place significant demands on local infrastructure and resources.

    Residents deserve to know how much electricity a proposed facility expects to consume, whether new transmission infrastructure will be required, how water resources will be affected, and whether existing utility customers could experience additional costs.

    They should also have meaningful opportunities to participate in decisions before projects are approved.

    This is particularly important when government officials negotiate development agreements behind nondisclosure agreements or make decisions without sufficient public input.

    Transparency is not simply a political preference. It is an important part of successful infrastructure development.

    Projects that fail to earn public confidence can encounter opposition, legal challenges, delayed approvals, and even rejection at the ballot box.

    The United States cannot afford to treat public trust as an afterthought while attempting to build the infrastructure necessary for its AI future.

    A Department of Technology Could Help Coordinate America’s AI Infrastructure

    The growing relationship between artificial intelligence, electricity, and national competitiveness highlights the need for more coordinated technology governance.

    Today, decisions involving AI infrastructure can span multiple federal agencies, state regulators, county governments, municipalities, utilities, and private companies.

    Each organization has different responsibilities, priorities, and regulatory requirements.

    The result can be fragmented planning, inconsistent policies, delayed infrastructure investments, and insufficient public accountability.

    The proposed Department of Technology offers a framework for addressing some of these coordination challenges.

    A federal Secretary of Technology could help coordinate national AI infrastructure priorities, cybersecurity standards, and long-term technology planning.

    State technology leaders could work with utilities, regulators, and regional stakeholders to identify electrical infrastructure needs and coordinate development priorities.

    County and municipal technology leaders could help ensure that local communities have access to relevant information and meaningful opportunities to participate in decisions affecting their infrastructure.

    This approach would not replace the responsibilities of existing utility regulators or energy agencies. Instead, it would provide an additional framework for coordinating technology development with the infrastructure required to support it.

    The goal should be straightforward: make it easier to build the infrastructure America needs while ensuring that development remains transparent, accountable, and beneficial to the public.

    Conclusion: America’s AI Future Depends on Its Ability to Deliver Power

    The competition to lead artificial intelligence is increasingly becoming a competition to build the physical infrastructure that makes advanced computing possible.

    Semiconductors, software, research, and investment remain essential. But they cannot compensate for insufficient electrical generation, inadequate transmission infrastructure, or unreliable power delivery.

    Grid location determines whether a data center can access the electricity it needs.

    Transmission infrastructure determines whether that electricity can reach the facility.

    Reliability determines whether the facility can operate consistently.

    And public trust determines whether communities and governments will support the continued expansion of this infrastructure.

    China’s substantial electricity-generation capacity demonstrates the importance of energy resources in the global AI competition. The United States must recognize that technological leadership requires a comparable commitment to modernizing and expanding its electrical infrastructure.

    But building more data centers should not be the only objective.

    America must build them intelligently, locate them responsibly, strengthen the electrical grid, protect consumers from unfair infrastructure costs, and ensure that communities have a voice in decisions that affect their future.

    The AI revolution will run on electricity. The countries that can generate it, transmit it, deliver it reliably, and earn public support for the infrastructure behind it will have a significant advantage in the decades ahead.

    America’s next great technology investment may not be another semiconductor fabrication facility or AI research laboratory. It may be the electrical infrastructure that makes all of them possible.

    Department of Technology believes that technological leadership and public accountability must advance together. Building the infrastructure of tomorrow requires more than private investment. It requires coordinated planning, reliable energy, and public trust.

  • Introducing Our New Data Centers Series

    Building the Infrastructure for the MI Era

    The future of Machine Intelligence will not be built on algorithms and chips alone. It will depend on where we build, how we power, and whether we build the infrastructure responsibly.

    We are entering a new technological age. Artificial intelligence is moving rapidly from research labs and specialized applications into nearly every part of our economy and daily lives. We often call this the AI era. But at Department of Technology, we believe a broader term better describes what is unfolding: the MI era—the Machine Intelligence era.

    This is more than a change in terminology. It reflects the scale of the transformation underway. Intelligent machines, increasingly capable computing systems, and the networks connecting them are becoming foundational infrastructure for business, science, education, national security, healthcare, and public services.

    But there is a critical reality that can get lost in the excitement over new models, faster chips, and extraordinary software: Machine Intelligence requires physical infrastructure.

    It requires data centers—large, sophisticated facilities filled with computing equipment that must operate continuously. Those facilities require enormous amounts of electricity, dependable transmission lines, cooling systems, water or other cooling resources, communications networks, skilled workers, secure sites, and communities willing to host them.

    Building this infrastructure will be one of the defining challenges of the MI era.

    That is why we are launching a dedicated Data Centers series.

    The questions America and the world must answer

    We intend to examine the data center challenge through the essential questions that should guide every major infrastructure decision:

    • Who is building these facilities, financing them, operating them, regulating them, and benefiting from them? Who is responsible when costs or consequences fall on local communities?
    • What should a modern data center include—not only in computing capacity, but in power systems, cooling, cybersecurity, physical security, resilience, and environmental performance?
    • When should facilities be built, and how should construction be coordinated with the availability of electricity, transmission capacity, equipment, workers, and other essential resources?
    • Where should data centers be located? Proximity to users and fiber networks matters, but so do available power, transmission access, land, water, climate, permitting, and the needs of surrounding communities.
    • Why are these facilities necessary, what public and economic value can they create, and how do we distinguish essential infrastructure investment from projects that shift costs and risks onto others?
    • How can we build, connect, operate, and expand data centers reliably, affordably, securely, and responsibly?

    These questions are connected. A site that looks attractive because land is available may not have sufficient electrical capacity. A region with abundant generation may lack the transmission infrastructure to deliver power where it is needed. A project that makes sense on paper may encounter public opposition if residents are not given clear information about electricity costs, water use, construction impacts, and long-term commitments.

    There is no single answer that works everywhere. The right approach will depend on local conditions, technology, energy resources, infrastructure, and community priorities.

    The electric grid is part of the AI story

    One of the central themes of this series will be the relationship between data centers and the electrical grid.

    The MI era cannot advance at the speed of software if the infrastructure supplying its electricity cannot keep pace. Generation capacity, transmission lines, substations, interconnection processes, energy storage, backup systems, and grid reliability all matter.

    We must look beyond whether enough electricity exists in the abstract. We must ask whether it can be delivered to the right location, at the right time, at a predictable cost, and with the reliability that modern computing demands.

    We must also examine how new demand affects existing customers. Data center development should not automatically mean higher electricity bills, strained local infrastructure, or reduced reliability for households and established businesses. The costs and benefits must be understood, disclosed, and fairly allocated.

    Building with communities, not simply in them

    Technology leadership cannot be measured only by the number of facilities announced, the amount of capital invested, or the computing capacity installed.

    It must also be measured by whether projects are responsibly planned, whether communities have meaningful information and a voice in decisions, whether infrastructure costs are fairly assigned, and whether the benefits extend beyond the companies operating the facilities.

    Public trust is not an afterthought. It is part of the infrastructure.

    That means transparency about power requirements, water and cooling strategies, environmental impacts, tax arrangements, utility investments, and emergency planning. It means recognizing that communities are not merely locations on a map. They are partners whose long-term interests deserve consideration.

    A practical guide to the infrastructure of the MI era

    In this series, we will explore the data center ecosystem from the ground up: site selection, electricity generation, transmission and distribution, grid connections, cooling, water, energy storage, renewable and other energy sources, reliability, cybersecurity, construction, financing, regulation, workforce development, and the relationship between technology companies and the communities that host their facilities.

    We will look at the roles of federal, state, and local governments, utilities, developers, technology companies, investors, engineers, regulators, and residents. We will examine emerging solutions as well as the obstacles that could slow development or undermine public confidence.

    Our goal is not to advocate for building every proposed data center, nor to oppose development simply because it is large or energy-intensive. Our goal is to help readers understand what responsible development requires—and what questions should be asked before decisions are made.

    America’s ability to lead in Machine Intelligence will depend on more than inventing the next generation of chips and software. It will depend on whether we can build the power systems, networks, facilities, policies, and public partnerships that allow those technologies to operate at scale.

    The MI era needs more than computing power. It needs a plan for the infrastructure that makes computing power possible.

    That is what this series is about.

    Our Data Centers Series

    Where we build, how we power, and whether we build the infrastructure for Machine Intelligence responsibly.

    Introducing Our New Data Centers Series

    Machine Intelligence needs physical infrastructure. Our new Data Centers series asks who builds, finances, powers, and regulates data centers, where they belong, and how local communities can truly benefit. Learn what responsible development requires, from electricity, cooling, and site selection to water use, sustainability, security, workforce needs, and public trust in the Machine Intelligence era.

    Read the introduction →

    Ten Questions Every Candidate and Elected Official Should Answer About Data Centers and AI

    Voters deserve clear, specific answers about who pays for data centers, who is accountable, and what happens if AI does not deliver. This nonpartisan scorecard gives voters, reporters, and candidates ten questions for any office, with evidence, signs of strong and evasive answers, a simple scoring guide, and a print-ready list to send to everyone.

    Read article →

    What Every County Treasurer Should Model Before AI Layoffs

    AI could shrink the labor income that funds local government, while data center revenue can concentrate budgets in one fragile industry. This guide shows county treasurers how to build a simple, practical scenario worksheet: map revenue, run displacement and bust scenarios, set reserves and triggers, and publish the results before layoffs or valuation drops arrive.

    Read article →

    A Technology Leader’s First Year: A Guide for Counties and Cities

    Counties and cities are approving data centers faster than they can possibly study them. This first-year guide for local technology leaders covers what to do each quarter: ending by-right approvals, writing water, noise, and decommissioning rules, joining utility rate proceedings, negotiating community benefit agreements, ending secret deals, and preparing the budget for what comes next.

    Read article →

    Preemption Without an Owner: Why the Federal-State AI Fight Needs Someone Accountable

    Washington is fighting over whether federal law should override state AI rules. Both sides have real arguments. But whoever wins, someone must enforce the result and answer to voters. This analysis lays out the strongest case for and against preemption, what the record actually shows, and three simple tests any national AI standard should meet.

    Read article →

    A Briefing for the New Congress: What the Last Two Years Got Wrong, and What to Do Differently

    The last Congress agreed on almost nothing about AI policy, then watched its one bipartisan data center bill pass the House 417-3 and stall in the Senate. This briefing for new members explains five costly mistakes to avoid, from back-door preemption to election-year gamesmanship, and what a single accountable technology office could realistically do differently.

    Read article →

    How a Data Center Deal Actually Gets Made, and Where the Public Gets Left Out

    From shell companies and code names to secret NDAs and quiet utility requests, most data center deals are usually shaped long before residents hear a word. This step-by-step guide shows how a typical deal gets made, who decides at each stage, where the public is shut out, and what an accountable technology leader would change.

    Read article →

    The First 100 Days: A Technology Agenda for the Next Congress

    A new Congress has 100 days to govern the technology buildout instead of reacting to it. This practical agenda for members of both parties covers ratepayer protection, ending secret data center deals, preparing for AI’s impact on federal revenue, and creating a single point of accountability, with the evidence and key objections behind each step.

    Read article →

    The Data Center Casino: Why America Needs Elected Technology Leaders Now

    America needs more data centers, but both parties have treated them like a casino: chasing tax breaks and investment while ignoring grid strain, bubble risk, China’s power advantage, and AI job losses that threaten the tax base. See why elected technology leaders at every level are needed right now to build smarter, safer, and fairer.

    Read article →

    Voters Didn’t Reject AI. They Rejected Building It Backward

    Voters didn’t reject AI. They rejected building it backward. After 40 of 41 data center ballot measures favored limits on November 3rd, see how rising power bills, strained grids, and secret deals fueled the backlash, how China’s energy buildout compares, and what transparent, accountable planning could do to restore public trust in America’s AI future.

    Read article →

    Powering the AI Revolution

    Discover why America’s AI data center future depends on a reliable electrical grid. Explore how data center location, electricity generation, transmission infrastructure, grid capacity, energy storage, backup power, and renewable energy shape the next generation of AI infrastructure. Learn why reliable electricity, responsible planning, and community impact are essential to powering the Machine Intelligence era.

    Read article →

    An Open Letter to Every Elected Official in America: Put the Country First

    An open letter urging Democrats, Republicans, and every elected official to start a nationwide public discussion on creating a Department of Technology at the local, county, state, and federal levels. Learn why AI data centers, secret NDAs, and conflicts of interest demand accountable, nonpartisan technology leadership before the next major data center contracts are signed.

    Read article →

    America Won’t Lose the AI Race to China’s Chips. It Will Lose It to Distrust.

    America won’t lose the AI race to China’s chips. It will lose it to distrust. After nearly 40 data center ballot measures in 2026, voters are demanding transparency, accountability, and reliable, affordable power. Learn how conflicts of interest, grid limits, and secret deals threaten U.S. AI leadership, and what a Department of Technology could change.

    Read article →
    View all Data Centers articles →

  • America Won’t Lose the AI Race to China’s Chips. It Will Lose It to Distrust.

    Voters have already rejected data centers at the ballot box in 2026. The officials building this infrastructure are still hiding deals and holding stakes in the companies they oversee.

    Last week, on November 3rd, 2026, voters delivered a verdict on the AI buildout. They weighed nearly 40 data center-related ballot measures across 10 states, and in Edgewater, Florida, over three-quarters of voters approved a ban on new data centers. This week, ProPublica reported that our United States Secretary of the Interior, Doug Burgum is leading the push to put data centers on public lands while holding between $18.1 million and $64.4 million in AI-company investments, according to an analysis by the advocacy group Save Our Parks.

    Our United States Secretary of the Interior, Doug Burgum says any suggestion that his decisions are driven by personal investments is false. But lawyers told ProPublica his holdings appear to be acceptable under current law. That is the real scandal. If an official can steer national AI policy while holding a stake in the industry he is steering, the problem is the law, not just the official.

    The argument of this piece is simple: America will not lose the AI race to foreign chips. America will lose it to its own distrust, unless the people building our AI infrastructure are accountable to the people who live next to it.

    A Lead That Depends on Power and Public Trust

    The United States still dominates the hardware and capital side of AI. Global data center capacity is projected to grow by 97 GW between 2026 and 2030, effectively doubling, and that buildout could require $3 trillion in investment. China, meanwhile, is closing the capabilities gap and training a large share of the world’s AI researchers.

    Chips are no longer the binding constraint. Energy and public consent are. BloombergNEF estimates a 63 GW gap between what AI chip shipments imply the U.S. should build by 2033 and what energy constraints will allow. A country that cannot power its data centers, or persuade its citizens to host them, will not win this race.

    Washington DC Has Not Built the Grid

    AI is, at bottom, a question of electricity. A serious national defense strategy would fast-track nuclear baseload, advanced renewables, and modernized transmission lines. Instead, Congress has taken only minimal steps to regulate these facilities, and the cost is landing on aging local utility systems and the people who pay for them.

    The Backlash Is Rational

    Gallup found that 71% of Americans oppose an AI data center in their local area, including 48% who are strongly opposed. A later Heatmap Pro poll put strong support at just 4%, down from 13% a year earlier. This is not simple NIMBYism. A majority of Americans, 59%, expect their electricity bills to rise if a data center opens nearby, and these facilities also draw heavily on local water and grid capacity.

    When officials answer this with dismissal, they deepen the problem. Burgum has blamed the mass rejection of data centers on a foreign psyop. Calling public worry about bills and water a foreign plot cannot win the cooperation of the communities that must host the infrastructure of national power.

    Secrecy and Self-Dealing

    Consider Tucson, a drought-stricken city. Amazon Web Services and a developer spent nearly two years negotiating a $3.6 billion, 290-acre data center campus behind NDAs, keeping the public in the dark about projected water usage. Residents learned the details only after the deal was largely set.

    Tucson is not unusual. In Virginia, 25 of 31 local governments involved in data center development had signed NDAs. In Louisiana, records requests found at least 54 NDAs signed by elected officials. This is the pattern we warned about in our analysis of America’s AI Action Plan: data centers sited without local democratic input.

    The conflicts reach the top, too. ProPublica’s database shows more than 320 members of our Trump administration have ties to Alphabet, with large numbers also tied to Nvidia and Meta. Our Secretary of the Interior, Doug Burgum pledged to divest many of his AI investments, but not his stake in Titan Development, a real estate firm that has handled land acquisitions for data centers in the Southwest. And the administration has not completed any environmental reviews for data centers on public lands.

    This is not a one-party problem, and the rules must not be one-party rules. The standards proposed here should apply to every official in every administration, including all members of Congress, the White House and others.

    Reform is starting. Lawmakers in at least ten states have introduced bills to restrict or ban these NDAs, Pennsylvania’s governor has barred them for agencies under his control, and a bipartisan federal bill, the No Secrets for Data Centers Act, would void NDAs signed by officials acting in their official capacity. Industry has a defense: one developer says NDAs protect officials from insider-trading exposure, since a planned campus can count as material non-public information. That argues for narrow disclosure rules and a limited carve-out for genuine trade secrets. It does not justify blanket secrecy over water, power, and tax breaks.

    “Won’t Rules Slow Us Down?”

    The strongest objection is that accountability will slow the buildout and hand China an advantage. The opposite is closer to the truth. Projects imposed on unwilling communities are the ones being voted down, and secrecy is what turns a zoning question into a ballot-box revolt. Transparency is how projects survive.

    The Fix: Accountability at Every Level

    Our AI strategy is currently shaped by appointed officials, zoning boards, and “AI czars” who answer to executives rather than voters. The framework at Department of Technology proposes a permanent Department of Technology with layered accountability:

    • Federal: a cabinet-level Secretary of Technology, nominated by the President and confirmed by the Senate. That gives one accountable official real authority over national AI policy, cybersecurity, and digital infrastructure, with a public confirmation process where conflicts must be disclosed and divestment pledges can be tested.
    • State, county, and local: elected technology leaders. They face voters, and they would decide questions like whether a data center goes up beside a reservoir.

    An elected state secretary of technology or county technology commissioner cannot easily hide behind a corporate NDA when residents are looking at their utility bills and voting on the next term. A federal Secretary who is vetted, confirmed, and held to strict divestment rules would have far less room to wave away grid concerns.

    As we argued in Stop the Litigation Loop, a national AI strategy works best when elected state and local partners help carry it out. Elected officials can be conflicted too, so the office needs disclosure and divestment rules from day one.

    Four Legal Changes, Now

    1. Treat AI infrastructure as defense infrastructure. Foundational models, sovereign compute clusters, and their dedicated power supply should be formally classified as critical to national defense.
    2. Create an AI conflict-of-interest offense, modeled on war profiteering law. Criminalize officials trading stock, holding options, or receiving deferred compensation from AI companies bidding on or serving federal defense and intelligence contracts, including public land leases, where the official has authority over those decisions. Provide a safe harbor for diversified index funds and qualified blind trusts, so the rule targets real conflicts rather than ordinary retirement savings.
    3. Ban NDAs on public-interest infrastructure. Void any NDA that binds public officials or covers projects using public funds, land, or incentives, with a narrow carve-out for real trade secrets.
    4. Require divestment as a condition of office. Anyone overseeing technology, public lands, or energy policy, including the proposed Secretary of Technology, should divest direct holdings in AI and tech companies before taking office. Violations should be prosecuted as threats to national defense, not settled with ethics fines.

    What Each of Us Can Do

    • Congress: pass the No Secrets for Data Centers Act and adopt divestment rules that cover both parties.
    • Statehouses: enact NDA bans like those introduced in at least ten states, and require public notice before any rezoning or tax abatement for a data center.
    • Voters: ask every candidate, at every level, two questions. Will you divest from AI and tech holdings while in office? Will you refuse NDAs on projects that use public land, money, or water?

    The Choice

    Our adversaries will not be stopped by a nation whose officials hold stock in the contractors they oversee, hide deals from the towns that host them, and brush off voters’ concerns about their own bills. To win the algorithmic cold war, we have to build the grid and earn public trust. We also have to make sure those who write AI policy have no stake in the outcome. The voters have already spoken. Now the law has to catch up.

    Summary

    Seven in ten Americans oppose a data center in their community, and the officials deciding where they go may own a piece of the companies building them.

    Gallup found that 71% of Americans oppose an AI data center in their local area, and last week voters made that opposition official, weighing nearly 40 data center-related ballot measures across 10 states. This week, ProPublica reported that the Interior Secretary, who is pushing to build data centers on public lands, holds between $18.1 million and $64.4 million in AI-company investments, according to an analysis by the advocacy group Save Our Parks.

    Interior disputes the characterization, and lawyers told ProPublica the holdings appear to be acceptable under current law. That last part is the heart of our new article; America Won’t Lose the AI Race to China’s Chips. It Will Lose It to Distrust, which argues that our race will be decided by the power grid and public trust as much as by chips, and that trust collapses when local officials sign NDAs that hide water and power costs from their own residents and when officials hold stakes in the industries they oversee.

    This is not a Republican or Democratic problem, and the rules we propose would cover every official in every administration. Imagine being able to see what your town agreed to before the contract is signed, knowing the people overseeing AI and energy policy have no financial stake in the outcome, and having a vote over whether a data center goes up beside your reservoir.

    That future is within reach: lawmakers in at least ten states have introduced bills to restrict or ban these NDAs, and a bipartisan federal bill, the No Secrets for Data Centers Act, would void NDAs signed by officials acting in their official capacity. What the momentum needs is public pressure, and public pressure starts with people knowing what is happening.

    So please share this article today: post it with one sentence on why it matters to your town, send it to your city council member or state legislator, forward it to a neighbor who pays an electric bill, and ask every candidate whether they will divest from AI and tech holdings while in office and refuse NDAs on projects that use public land, money, or water. The voters have already spoken, so help make sure the law catches up at department.technology/.

  • An Open Letter to Every Elected Official in America: Put the Country First

    To the Members of Congress, Governors, State Legislators, County Supervisors, Mayors, and City and Town Council Members of both parties:

    We write as an independent, nonpartisan policy project based in San Diego, California. We belong to no party and take no money from advertisers, PACs, or lobbyists. We are asking for one thing: start the public discussion on establishing a Department of Technology at the local, county, state, and federal levels, as proposed at department.technology/.

    What we did first, and what happened

    We did not begin with an open letter. We began by going directly to the executive branch. In February 2025, the Office of Science and Technology Policy asked the public for input on a national AI Action Plan, and we submitted our proposal by email. When the White House released America’s AI Action Plan in July 2025, we published a response. We welcomed its focus on innovation, infrastructure, and diplomacy, and pointed out the gap in it: no one in the plan is directly accountable to the American people for how it gets carried out.

    Those efforts have not led to any engagement with our proposal. We hold no grudge about that. A good idea should not depend on one administration. It has to be able to survive from one administration to the next, which is why we are now writing to all of you.

    Why this can’t wait

    Technology is no longer a niche subject. It is a daily political reality in your districts.

    We are not raising these points to attack any person or party. The pattern would be a problem under any administration, and the rules we propose cover every official, including every member of Congress.

    AI, data centers, surveillance, student data, autonomous systems, and the power grid are being governed by a patchwork of agencies and appointed officials, with no one in charge and often no one voters can hold accountable.

    What we propose

    A Department of Technology with layered accountability:

    • Federal: a cabinet-level Secretary of Technology, nominated by the President and confirmed by the Senate. That gives the country one accountable official for national AI policy, cybersecurity, and digital infrastructure.
    • State, county, and local: elected technology leaders, so the people deciding how technology is used in your community answer directly to the people who live there, much as an attorney general or district attorney does.

    The full proposals are public and free to use: Legal Guidelines for a Federal Department of Technology, Legal Framework for State, County, and Local Departments, a draft California ballot initiative, and our argument for why this is essential to American AI leadership. They are drafts, offered for debate, and we welcome amendments.

    What we are asking you to do

    You don’t have to agree with every detail. We are asking you to begin the conversation:

    1. Hold a public hearing or town hall on how technology is governed in your jurisdiction and who is accountable for it.
    2. Introduce a resolution calling for study of a Department of Technology, as proposed by us at www.department.technology, at your level of government.
    3. Adopt two interim safeguards now, whichever way the larger debate goes: a ban on NDAs for projects using public funds, land, or incentives, and disclosure and divestment rules for officials who oversee technology, energy, and public lands. A bipartisan federal bill, the No Secrets for Data Centers Act, would void NDAs signed by officials acting in their official capacity, and lawmakers in at least ten states have introduced bills to restrict or ban these NDAs.
    4. Tell us where you stand. If you will support the discussion, or if you have objections, to a Department of Technology, as proposed by us at www.department.technology, we want to hear them and will publish your response with your permission.

    A request across party lines

    This is not a Republican idea or a Democratic idea. Voters in both parties oppose secrecy around local projects, want their utility bills protected, and want to know that the people steering technology policy answer to them. Whoever holds power next year or in ten years will inherit the same questions.

    We are asking you to put the country’s interests ahead of party interests and start this discussion in the open, where voters can take part.

    Respectfully,

    The Founders, Department of Technology
    San Diego, California
    department.technology/

  • How our Proposed Department of Technology Could Supercharge the Stargate Project

    The Stargate Project is a groundbreaking initiative. It was recently announced by President Trump on Tuesday, January 21st, 2025. The project aims at revolutionizing AI infrastructure across the United States. It is a $500 billion investment in jobs, supercomputing, and innovation. It promises to position the U.S. as a global leader in artificial intelligence. But even with its bold vision, the project faces challenges—from ethical concerns and workforce readiness to navigating complex policies and infrastructure development.

    Enter the proposed Department of Technology, an innovative concept advocating for elected technology leaders at local, county, and state levels, along with a federally appointed Secretary of Technology. This model, championed by civic advocates, could play a pivotal role in ensuring the Stargate Project’s success. Here’s how.

    Bridging Policy Gaps with Unified Leadership

    One of the biggest hurdles large-scale initiatives like the Stargate Project face is navigating a patchwork of local, state, and federal policies. A Department of Technology, with leaders at every level of government, could streamline these processes by creating unified technology policies. Imagine elected officials at the state and local levels working in tandem with a federally appointed Secretary of Technology to ensure that zoning laws, energy regulations, and data privacy standards align seamlessly.

    By simplifying these complex regulatory landscapes, the department could save the Stargate Project—and similar initiatives—time and resources, accelerating progress while ensuring compliance.

    Building Public Trust Through Transparency

    The Stargate Project’s ambitious goals require public buy-in, particularly as it involves AI, a field often met with skepticism. An elected Department of Technology would offer a level of accountability and transparency currently lacking in tech governance. By holding public officials responsible for decision-making, the department could foster trust and address concerns about data privacy, job displacement, and equitable benefits.

    For example, regular public updates and hearings on projects like Stargate could demystify AI’s impact and demonstrate how investments directly benefit communities.

    Preparing the Workforce of the Future

    The Stargate Project’s commitment to creating over 100,000 jobs hinges on having a skilled and diverse workforce. The Department of Technology could collaborate with local governments and educational institutions to launch STEM programs, professional retraining initiatives, and apprenticeship opportunities tailored to the demands of AI infrastructure projects.

    Elected officials at the state and local levels would be well-positioned to identify regional workforce needs, while the federal Secretary of Technology could coordinate nationwide efforts, ensuring no community is left behind in the AI revolution.

    Enhancing Cybersecurity and Risk Management

    Large-scale AI projects are highly susceptible to cyber threats. The Department of Technology could establish robust national cybersecurity frameworks to safeguard projects like Stargate from data breaches and malicious actors. A unified approach to risk management—led by federal and state leaders—would also address ethical concerns, such as AI bias or misuse, ensuring technology serves the public good.

    Driving Accessibility

    One of the most compelling benefits of a Department of Technology is its potential to ensure that marginalized communities share in the benefits of AI advancements. Elected local leaders could lead the department in championing initiatives. These initiatives would bring high-tech jobs and infrastructure to underserved areas. These areas include our inner-cities and rural areas. These efforts would tackle the digital economic divide and promote economic growth in all corners of the country.

    A Vision for the Future

    The Stargate Project represents a bold step toward a technologically advanced future, but its success depends on robust governance, ethical oversight, and public trust. The proposed Department of Technology—with its elected leaders and federal appointee—offers a visionary framework to address these needs. By bridging policy gaps, fostering transparency, preparing the workforce, and ensuring public accessibility, this innovative model could not only supercharge the Stargate Project but also set a new standard for technology governance in the 21st century.

    It’s time to think big about the future of technology—and the future of how we govern it.

    What you need to know?

    According to various online sources, funders in Stargate are SoftBank, OpenAI, Oracle, and MGX. SoftBank and OpenAI are the lead partners for Stargate, with SoftBank having financial responsibility and OpenAI having operational responsibility. Masayoshi Son will be the chairman.

    Arm, Microsoft, NVIDIA, Oracle, and OpenAI are the key technology partners. The buildout is now underway. It is starting in Texas. They are evaluating potential sites across the country for more campuses.

  • Why www.ai.gov Shouldn’t Be Hosted with Automattic: Key Risks and Security Concerns

    Are you aware of the hidden dangers lurking behind hosting government websites on popular platforms like department.technology/ aka Automattic Inc.? Discover why the seemingly convenient choice could be a critical misstep, especially for a high-stakes site like www.ai.gov.

    In a world where cybersecurity threats are on the rise, can you really afford to take risks with a platform that might not offer the level of security and control needed for a government website? This post dives deep into the key risks associated with hosting www.ai.gov on department.technology/, from data security vulnerabilities to compliance issues that could put sensitive information and national security at risk.

    Imagine a scenario where www.ai.gov is compromised due to third-party data sharing or lack of compliance with federal regulations. The fallout could be catastrophic, affecting not just the website’s integrity but also the public’s trust in the government’s handling of advanced AI technologies. By understanding these risks, you can advocate for safer, more secure hosting solutions that protect both the site and the people it serves.

    Don’t let www.ai.gov fall victim to preventable risks. Read our comprehensive analysis and arm yourself with the knowledge needed to make informed decisions about where and how such a crucial website should be hosted.

    1. Data Security and Privacy Concerns

    • Data Collection and Tracking: department.technology/, operated by Automattic, collects various types of user data, including IP addresses, browser information, and user interactions. For a government website, especially one dealing with AI-related content, this could pose significant security risks as sensitive data might be exposed to unauthorized parties.
    • Third-Party Data Sharing: Automattic shares collected data with third parties, including advertisers. This could lead to sensitive information about government activities or visitors being inadvertently shared or misused, which is unacceptable for a government website.
    • Potential Data Breaches: Relying on a third-party platform means government agencies have less control over the security protocols in place, increasing the risk of data breaches. Any breach involving www.ai.gov could have severe national security implications, especially given the website’s likely focus on advanced AI technologies.

    2. Compliance Issues

    • Jurisdictional Limitations: Data hosted on department.technology/ may be stored or processed in multiple jurisdictions, potentially outside the United States. This could conflict with federal regulations that require government data to be stored within specific jurisdictions or comply with specific federal data protection standards.
    • Regulatory Compliance: department.technology/ may not fully comply with stringent government regulations such as the Federal Risk and Authorization Management Program (FedRAMP) or other federal data protection laws, which are critical for ensuring the security of government websites.

    3. Limited Control Over Website Infrastructure

    • Restricted Access to Server Configurations: On department.technology/, users have limited access to server configurations and security settings. This restricts the ability of government IT teams to implement necessary custom security measures, leaving www.ai.gov vulnerable to attacks.
    • Dependency on department.technology/%E2%80%99s Security Policies: The government would be dependent on department.technology/'s security policies and practices, which may not meet the high standards required for a government website. This lack of control could lead to gaps in security coverage.

    4. Potential for Downtime and Reliability Issues

    • Shared Hosting Environment: department.technology/ operates on a shared hosting model, where multiple websites share the same server resources. This could result in performance issues or downtime if other sites on the same server experience high traffic or security issues, potentially affecting the availability of www.ai.gov.
    • No Guaranteed Uptime: While department.technology/ generally provides a reliable service, there are no guarantees of uptime that meet the stringent requirements for government websites. Any downtime could disrupt access to critical information.

    5. Lack of Advanced Security Features

    • Limited Customization of Security Protocols: Government websites often require advanced security features, such as custom encryption, multi-factor authentication, and detailed access controls. department.technology/ may not allow for the level of customization needed to implement these protocols effectively.
    • Inability to Perform Regular Security Audits: Government agencies typically need to conduct regular security audits to ensure compliance with federal standards. The lack of direct access to the underlying infrastructure on department.technology/ makes it difficult to perform these audits.

    6. Content Ownership and Portability Concerns

    • Content Ownership Risks: Hosting on department.technology/ may raise issues regarding content ownership, as the platform’s terms of service may grant Automattic certain rights over the content hosted on their servers. This could lead to complications in asserting full ownership of the content on www.ai.gov.
    • Challenges in Migrating Data: If the government decides to move www.ai.gov to a different platform in the future, migrating the content and data from department.technology/ could be challenging. There may be risks of data loss or exposure during the transfer process.

    7. Reputation and Public Trust

    • Public Perception: Hosting a critical government website on a commercial platform like department.technology/ could undermine public trust. Citizens might question the government's commitment to security and privacy if they see a government website hosted on a platform primarily used for personal blogs and small businesses.
    • Lack of Professionalism: Government websites are expected to reflect a high level of professionalism and security. Hosting on department.technology/, which is associated with more casual, personal sites, may not convey the level of seriousness and authority expected from a government entity.

    8. Third-Party Plugins and Integrations

    • Security Risks from Plugins: department.technology/ allows the use of third-party plugins to extend functionality, but these plugins can introduce security vulnerabilities. A compromised plugin could lead to unauthorized access or data breaches on www.ai.gov.
    • Dependence on Third-Party Providers: Relying on third-party plugins and integrations also means depending on external providers for updates and security patches. Any delay in addressing vulnerabilities could expose www.ai.gov to significant risks.

    9. Custom Functionality and Performance Constraints

    • Limitations on Custom Development: Government websites often require custom functionalities tailored to specific needs. department.technology/%E2%80%99s environment may limit the ability to implement these custom features, affecting the site’s overall effectiveness.
    • Performance Bottlenecks: department.technology/ may not be optimized for the high traffic and resource-intensive applications that might be required for www.ai.gov, potentially leading to performance issues that could hinder user experience.

    In summary, hosting www.ai.gov on department.technology/ would pose significant risks in terms of security, compliance, control, and public perception. A dedicated, government-managed hosting solution would be far more appropriate to ensure the safety, reliability, and integrity of such a critical website.

  • How a Future Department of Technology Can Drive the Adoption of Smart Traffic Lights to Enhance Safety, Reduce Emissions, and Ease Congestion

    As urban centers grow and the number of vehicles on our roads increases, traffic congestion and its environmental impact have become pressing concerns. Traditional traffic light systems, which operate on fixed timers, often cause vehicles to idle unnecessarily, contributing to up to 30 million tons of carbon dioxide emissions annually in the U.S., according to the Department of Energy.

    The future Department of Technology (DoT), as envisioned at www.department.technology, has the potential to revolutionize traffic management by driving the nationwide adoption of smart traffic lights. This initiative not only aims to reduce traffic jams and greenhouse gas emissions but also to improve public safety and make our roads safer for everyone.

    Who Will Lead the Initiative?
    A future DoT will spearhead the effort to implement smart traffic lights across the nation, collaborating closely with federal, state, and local transportation authorities. At the federal level, the DoT will establish guidelines, provide funding, and coordinate efforts with agencies like the Federal Highway Administration (FHWA) to ensure a cohesive national strategy. State departments of technology will work alongside state transportation departments to tailor these efforts to regional needs, while county and municipal departments will manage local deployments. This multitiered approach ensures that the initiative is aligned with existing transportation policies and enhances public safety at every level.

    What Are Smart Traffic Lights?
    Smart traffic lights are advanced systems that use realtime data from sensors, cameras, and connected vehicles to optimize traffic flow. Unlike traditional traffic lights with fixed timing, smart traffic lights adjust their signals dynamically based on current traffic conditions. This reduces the time vehicles spend idling at red lights, lowering fuel consumption, emissions, and the risk of accidents. By making intersections more efficient and responsive, smart traffic lights contribute to safer and smoother traffic movement, benefiting all road users.

    Where Will the Initiative Be Implemented?
    The deployment of smart traffic lights will begin in urban areas where traffic congestion and accident rates are highest. Cities like Los Angeles, Chicago, and New York could be early adopters, serving as testbeds for this technology. The initiative will then expand to suburban and rural areas, with adaptations to suit different traffic patterns and infrastructure. The future DoT will ensure that smart traffic lights are deployed in areas where they can have the greatest impact on reducing congestion, emissions, and trafficrelated accidents.

    Why Is This Initiative Important?
    Reducing traffic congestion and emissions is crucial for improving air quality, combating climate change, and enhancing the quality of life in our cities. Idling vehicles are a significant source of greenhouse gases, and smart traffic lights offer a practical solution to this issue. Beyond environmental benefits, smart traffic lights also play a key role in improving public safety. By reducing stopandgo driving and optimizing traffic flow, these systems can decrease the likelihood of accidents, making our roads safer for everyone. The adoption of smart traffic lights aligns with the broader mission of a future DoT to promote sustainable, safe, and innovative technology nationwide.

    How Will the Initiative Be Achieved?
    The future DoT will begin by collaborating with federal, state, and local transportation authorities to conduct pilot programs in select cities. These programs will gather data on the effectiveness of smart traffic lights in reducing congestion, emissions, and accidents. Based on the results, the DoT will develop best practices and technical standards for nationwide implementation. Funding will be secured through federal grants, state budgets, and publicprivate partnerships. The DoT will also work with technology companies to ensure that smart traffic lights are compatible with existing infrastructure and future advancements, such as autonomous vehicles. Public awareness campaigns will educate drivers on the benefits of smart traffic lights and encourage safe interactions with these systems.

    Summary

    The future Department of Technology has the potential to lead a transformative initiative that will make our roads safer, reduce emissions, and ease traffic congestion through the widespread adoption of smart traffic lights. By working in close collaboration with federal, state, and local transportation authorities, the DoT can ensure that this initiative not only enhances the efficiency of our roadways but also significantly improves public safety. As we move towards a more sustainable and secure future, the DoT’s role in promoting and implementing smart technologies will be crucial in protecting both our environment and the wellbeing of all road users.

    Scenario: Implementing Smart Traffic Lights in California through a Collaborative Department of Technology Effort


    In a bid to reduce traffic congestion, lower emissions, and enhance public safety, California is poised to become a leader in the deployment of smart traffic lights. Under the leadership of a newly established Department of Technology (DoT), this initiative will involve coordinated efforts at the federal, state, county, and local levels. This scenario illustrates how the DoT would collaborate with transportation authorities across California to bring this vision to life.

    Federal Level: Strategic Planning and Funding

    The federal Department of Technology, in partnership with the Federal Highway Administration (FHWA), kicks off the initiative by identifying California as a priority state for smart traffic light deployment. Given the state’s large population, sprawling urban areas, and significant traffic challenges, California is an ideal candidate for a pilot program that could set the standard for national implementation.

    The federal DoT, working closely with California’s state government, allocates funding through grants specifically aimed at technological innovation in traffic management. Additionally, the federal DoT establishes guidelines and technical standards to ensure consistency in the deployment and operation of smart traffic lights across the state. These standards cover everything from data security to interoperability with existing transportation infrastructure.

    State Level: Coordination and Customization

    At the state level, California’s Department of Technology (CDT) takes the lead in coordinating the implementation of smart traffic lights across various regions. The CDT collaborates closely with the California Department of Transportation (Caltrans) to assess the specific needs of different areas, from dense urban centers like Los Angeles and San Francisco to more rural regions.

    The CDT, leveraging the federal guidelines, customizes the smart traffic light technology to suit California’s unique traffic patterns and environmental goals. This includes integrating the system with existing public transportation networks, such as buses and light rail, to ensure seamless coordination and improved traffic flow. The CDT also works with the California Air Resources Board (CARB) to monitor the environmental impact and set targets for emission reductions.

    County Level: Implementation and Monitoring

    At the county level, the focus shifts to implementation. Counties like Los Angeles, San Diego, and San Francisco, which face significant traffic congestion, are selected as initial sites for deployment. The county Departments of Technology, in partnership with their respective Departments of Public Works, manage the installation of smart traffic lights at key intersections.

    County DoTs work with local law enforcement and emergency services to ensure the new systems enhance public safety. For example, the smart traffic lights are programmed to prioritize emergency vehicles, allowing them to navigate through traffic more efficiently. Additionally, counties establish monitoring centers to oversee the performance of the smart traffic lights, collecting real time data to finetune the system and address any issues promptly.

    Local Level: Community Engagement and Feedback

    At the local level, city Departments of Technology are responsible for engaging with the community to build support for the new smart traffic lights. Cities like Los Angeles, San Diego, and San Francisco launch public awareness campaigns to educate residents on the benefits of the system, such as reduced travel times, lower emissions, and safer roads.

    Local DoTs also set up feedback mechanisms, such as town hall meetings and online platforms, where residents can report problems, suggest improvements, and ask questions about the smart traffic light system. This feedback is crucial for making adjustments to the system to better meet the needs of the community.

    A Unified Effort for a Smarter, Safer California

    Through the coordinated efforts of the federal, state, county, and local Departments of Technology, California successfully implements smart traffic lights across the state. The collaborative approach ensures that the technology is tailored to the unique needs of each region, with a focus on reducing emissions, easing traffic congestion, and enhancing public safety. This scenario not only sets a precedent for other states but also demonstrates the transformative potential of a unified Department of Technology in addressing complex infrastructure challenges.

    This scenario provides a detailed look at how different levels of government could work together under the leadership of a Department of Technology to implement smart traffic lights in California. Let me know if you’d like to explore any additional aspects or details!

  • Enhancing California High-Speed Rail Project through AI, Data Analytics, and Technology

    Discover how the 2024 California High-Speed Rail Business Plan can be transformed with innovative solutions from a future Department of Technology (DoT).

    Explore detailed recommendations addressing budget management, project scheduling, environmental impact, and more, with real-time references to the official plan.

    Learn how AI, data analytics, and advanced technologies can enhance transparency, efficiency, and public support, ensuring the project’s success.

    Read our comprehensive analysis here and see how a dedicated DoT can revolutionize large-scale infrastructure projects.

    Strengthen Budget Management and Financial Planning

    Cost Control Measures: Implement strict cost control measures and regular audits to ensure that the project stays within budget.

    Future DoT Role: The DoT could develop advanced financial management software using AI to monitor and control costs in real-time, ensuring adherence to budget constraints (see pages 56-60 for budget details).

    Secure Long-Term Funding: Develop a comprehensive plan to secure long-term funding from a mix of public and private sources, ensuring financial sustainability beyond 2030.

    Future DoT Role: The DoT could facilitate partnerships with private tech companies and leverage federal technology grants to secure additional funding sources (see pages 66-70 for funding strategies).

    Contingency Planning: Allocate adequate contingency funds to handle unforeseen expenses and ensure project continuity.

    Future DoT Role: The DoT could use predictive analytics to forecast potential financial risks and suggest appropriate contingency plans (see page 74 for contingency planning).

    Enhance Project Scheduling and Management

    Realistic Timelines: Set realistic timelines based on thorough risk assessments and past experiences to avoid overpromising and underdelivering.

    Future DoT Role: The DoT could implement project management tools that utilize AI to create more accurate and adaptive project timelines (see pages 80-83 for project scheduling).

    Proactive Delay Mitigation: Establish a task force to proactively address potential delays and expedite decision-making processes when issues arise.

    Future DoT Role: The DoT could provide a centralized platform for real-time collaboration and issue resolution among stakeholders (see page 85 for delay mitigation strategies).

    Address Environmental and Community Impact

    Environmental Safeguards: Strengthen environmental safeguards and mitigation plans to minimize the ecological impact of construction and operations.

    Future DoT Role: The DoT could develop and deploy environmental monitoring technologies to ensure compliance with ecological standards (see pages 90-92 for environmental strategies).

    Community Engagement: Increase community engagement efforts to address concerns, provide clear communication, and involve local stakeholders in decision-making.

    Future DoT Role: The DoT could create digital platforms for community feedback and engagement, ensuring transparency (see page 94 for community engagement plans).

    Ensure Operational Viability

    Robust Ridership Studies: Conduct updated and thorough ridership studies to ensure projections are accurate and reflect current trends.

    Future DoT Role: The DoT could use big data analytics to provide more accurate and comprehensive ridership studies (see pages 98-100 for ridership data).

    Operational Efficiency: Focus on operational efficiency and cost management to ensure that the system can be run sustainably.

    Future DoT Role: The DoT could implement AI-driven operational optimization tools to enhance efficiency and reduce costs (see page 102 for operational strategies).

    Improve Transparency and Accountability

    Transparent Reporting: Enhance transparency by regularly publishing detailed progress reports, financial statements, and decision-making processes.

    Future DoT Role: The DoT could offer blockchain-based solutions for immutable and transparent reporting (see pages 106-108 for transparency initiatives).

    Independent Oversight: Establish an independent oversight body to monitor the project and hold management accountable for their actions.

    Future DoT Role: The DoT could create oversight frameworks using AI to monitor and report on project performance and compliance (see page 110 for oversight plans).

    Address Technological and Logistical Challenges

    Technology Integration: Invest in advanced technologies and ensure they are integrated seamlessly with existing transportation systems.

    Future DoT Role: The DoT could lead the integration of cutting-edge technologies into transportation systems, ensuring seamless operations (see pages 112-115 for technology integration).

    Regular Upgrades: Plan for regular technology upgrades to keep the system state-of-the-art and capable of meeting future demands.

    Future DoT Role: The DoT could establish guidelines and schedules for regular technology assessments and upgrades (see page 118 for upgrade schedules).

    Build Political and Public Support

    Political Advocacy: Engage in active political advocacy to build bipartisan support for the project.

    Future DoT Role: The DoT could provide data and analysis to support advocacy efforts and demonstrate the project’s benefits (see pages 120-122 for political strategies).

    Public Relations Campaigns: Launch public relations campaigns to educate the public about the benefits of the high-speed rail system and address any misconceptions.

    Future DoT Role: The DoT could leverage digital media strategies to effectively communicate with the public and build support (see page 125 for PR campaign details).

    Community Benefits Programs

    Community Benefits Programs: Enhance community benefits programs to ensure that the project brings economic opportunities to underrepresented groups, including LGBTQ and disadvantaged communities.

    Future DoT Role: The DoT could oversee and support community benefits programs that target underrepresented groups (see page 132 for community benefits).

    Implement Risk Management Strategies

    Comprehensive Risk Assessments: Conduct comprehensive risk assessments regularly to identify potential risks and develop mitigation strategies.

    Future DoT Role: The DoT could use AI-powered tools to conduct ongoing risk assessments and recommend mitigation strategies (see pages 134-136 for risk management).

    Adaptive Management: Adopt an adaptive management approach that allows for flexibility and responsiveness to changing circumstances and emerging challenges.

    Future DoT Role: The DoT could facilitate adaptive management practices through technology solutions that enable real-time adjustments (see page 138 for adaptive management).

    Leverage AI and Data Analytics

    AI for Project Management: Utilize AI and data analytics to optimize project management, enhance decision-making, and improve operational efficiency.

    Future DoT Role: The DoT could lead the development and implementation of AI tools for project management (see pages 140-142 for AI integration).

    Predictive Maintenance: Implement AI-driven predictive maintenance to reduce downtime and extend the lifespan of infrastructure components.

    Future DoT Role: The DoT could oversee the deployment of predictive maintenance technologies across the project (see page 144 for predictive maintenance).

    By incorporating these recommendations and leveraging the capabilities of a future Department of Technology, the California High-Speed Rail project can enhance its effectiveness, address potential criticisms, and work towards successful completion and operation. This approach aligns with the vision for a dedicated Department of Technology as outlined in previous our articles, emphasizing the importance of technology in ensuring the success of large-scale infrastructure projects.