I have spent twenty-five years watching governments make infrastructure decisions. I have watched projects that should have been approved in eighteen months take a decade. I have watched contracts go to connected firms when superior firms existed. I have watched tax dollars disappear into procurement processes so complex and so opaque that no one person could trace where they went. I have watched lobbyists write the regulations that govern the industries their clients operate in. And I have watched genuinely good people, civil servants who entered public life because they believed in it, become slowly ground down by systems that were never designed to work efficiently.

I am not making a political argument. The waste I have observed crosses every ideological line. It is not a problem of the left or the right. It is a problem of the structure of government itself: humans making collective decisions about the allocation of enormous resources, with inadequate information, inadequate accountability, and enormous incentives for self-dealing. This is not a new observation. It is as old as civilization. What is new, what has never existed before in human history, is the possibility of a technological alternative.

The Thesis: From the County Up

Forget the federal government, at least for now. The federal government is a system so large, so entrenched, and so thoroughly captured by competing interests that meaningful transformation from the outside is not achievable in any near-term timeframe most of us will witness in our lifetimes. I hope I am categorically wrong. However, county, city, and state governments are different. They are closer to the people they serve. Their decisions are more directly felt. Their failures are more immediately visible. And they are small enough that technology can actually reach them.

The thesis I first wrote about in my LinkedIn article in 2024 and that I want to expand on here is this: county, city, and state governments will slowly transition into a blended platform of blockchain and AI features that will empower citizens to decide where tax dollars are allocated and which government services and infrastructure will take priority. This is not a fantasy. The building blocks are already being laid. Wyoming is piloting a state-issued stable token for contractor payments that reduced disbursement timelines from 45 days to seconds. California’s DMV has digitized 42 million car titles on blockchain infrastructure, creating an immutable and instantly accessible record of ownership. North Carolina’s legislature is actively exploring blockchain for property taxes, voting, and public records. These are not headline stories. They are the quiet early steps of a transition that, if it continues, will change what government means within a generation.

The Platform: What It Could Actually Look Like

Imagine an application, call it the CivicLedger, for the sake of argument, that every citizen can download onto their phone. It shows, in real time, every dollar of public revenue and every dollar of public expenditure in their city, county, and state. Not in the aggregate. Not in a PDF published quarterly that nobody reads. In real time. Every contract awarded. Every vendor paid. Every department’s budget. Every infrastructure project’s status. Every grant received and disbursed. Every salary on the public payroll above a defined threshold. All entries on a blockchain, so that no entry can be altered after the fact, only appended, with a time-stamped record of who made every change.

On this same platform, citizens can vote on major spending decisions. Not every decision, the model of requiring citizen approval for every routine procurement would be paralyzing, and voters do not want to spend their evenings deciding which office supplies to buy. But major decisions, infrastructure allocations above a defined threshold, new welfare program designs, emergency spending authorizations, the terms of public-private partnerships, the approval of new bond issuances, these are decisions that citizens have both the right and, when the information is accessible and the interface is simple, the capacity to make.

The ID verification infrastructure exists. Biometric identity systems, blockchain-anchored digital credentials, and government-issued digital IDs are already operational in Estonia, which has run its entire government on a digital platform for over twenty years, with ninety-nine percent of public services available online and voter participation in digital elections exceeding seventy percent of the eligible population. Estonia is a small country. But it is proof that this is not science fiction. This is an engineering problem that a real democracy has already solved on a smaller scale.

The AI Layer: Efficiency Without Bias — If Possible

Now add artificial intelligence. Not as the decision-maker, I will return to why that specific framing frightens me, but as the analyst. An AI system with read access to every public contract, every spending record, every regulatory filing, and every procurement decision across all government departments could identify patterns of waste, fraud, and inefficiency that no human audit team could detect at the same speed or scale. This system could identify unusual contract awards when a specific vendor repeatedly wins bids, even if their prices aren’t the lowest. It could identify regulatory approvals that consistently take three times longer for applicants without established political relationships. With its ability to calculate the per-capita cost of public services, it could also benchmark against comparable jurisdictions to identify underperforming areas. It could do all of this continuously, in real time, without fatigue, without bias toward any specific political outcome, and without the ability to be lobbied.

The efficiency gains from this layer alone, without removing a single human decision-maker, would be substantial. The federal government’s own Government Accountability Office has identified hundreds of billions of dollars in annual improper payments, duplicative programs, and procurement inefficiencies across the executive branch. At the state and local level, comparable waste is proportional. A system that simply surfaced this waste clearly, attributed it specifically, and made the attribution visible to citizens through the CivicLedger platform would create accountability pressure that the current system systematically cannot generate.

Could an AI-assisted government save enough to reduce or eliminate income tax? The honest answer is: yes, possibly, but the savings would need to be enormous, and the transformation would need to be complete. The Tax Policy Center estimates that the federal government spends approximately 15 percent of its budget on administrative overhead, procurement waste, and program duplication. At the state and local level, that percentage is higher in many jurisdictions. If a well-functioning AI-assisted government reduced that overhead by half, a conservative estimate given what operational AI has achieved in private sector supply chains and financial services, the savings would run into the hundreds of billions of dollars annually at the federal level alone. Whether those savings would translate into lower taxes or higher services is a political choice. But the savings would be real, and they would be demonstrable to every citizen who could see the numbers on their phone.

The Fear: Who Runs the AI?

And here is where I stop being optimistic. Because the history of powerful technology, from the printing press to the internet to social media, is a history of tools built with idealistic intentions that were captured by interests the inventors did not foresee. And artificial intelligence, as it currently exists, is not neutral.

We have already seen AI systems trained on data that reflects the biases of their creators, producing outputs that favor certain viewpoints, certain sources, and certain political framings over others. We have seen major AI platforms, including some of the most widely used in the world, respond differently to questions about different political groups, different historical events, and different religious traditions. This is not a conspiracy. It is a documented and acknowledged limitation of how large language models are trained: they learn from human-generated data, and human-generated data reflects human biases. When those biases are systematically skewed toward any particular political, cultural, or economic worldview, even unconsciously, the AI inherits and amplifies them.

A government run on a biased AI is not a more efficient version of the government we have. It is something potentially far worse: a system with all the efficiency advantages of artificial intelligence and all the partisan distortions of human politics, but without the visibility or accountability that human political systems at least nominally provide. At least when a politician makes a corrupt decision, there is a trail: campaign contributions, meeting records, phone calls, and emails that can eventually be subpoenaed. An AI system that systematically disadvantages certain communities or policy outcomes leaves no such trail, unless the system is specifically designed for transparency from the ground up.

The question of who runs the AI is the most important question of our time, and I do not have a satisfying answer. What I know is that concentrating control of a government-level AI system in the hands of any single founder, corporation, or political faction is exactly the wrong structure, regardless of how benign their stated intentions. The history of concentrated power does not provide reasons for optimism. And I have a specific and growing concern about whether any individual or small group of people, regardless of their intelligence or their good faith, can maintain control over AI systems that are rapidly approaching and in specific domains, already exceeding the intelligence of their creators. The idea that an AI system can be permanently constrained by the values and limitations of the humans who built it becomes less plausible as those systems become more capable.

The Pattern Already Underway: A Structural Warning

I want to be direct about something that is not comfortable to raise in a book about infrastructure finance, because infrastructure finance is a business that depends on government relationships, government contracts, and government goodwill. But intellectual honesty requires saying it plainly: the scenario I find most frightening about AI-operated government is not hypothetical. A pattern is already underway, visible in publicly documented contracts and policy decisions, and it deserves frank examination from anyone who cares about where the world’s largest economy is heading.

The pattern I am describing is drawn entirely from publicly available government contract records and the public securities filings of the companies involved, documents any member of the public can access through USAspending.gov and the SEC’s EDGAR database. The pattern works as follows. A private technology company, with deep roots in the intelligence community, develops software platforms that connect government databases, databases that were never designed to be linked, that were kept separate by design, because their separation was itself a form of civil liberties protection. The software is marketed not as a surveillance tool but as an efficiency tool. It promises to eliminate waste, streamline operations, reduce fraud, and modernize legacy systems. These promises are real; the efficiency gains are genuine. But so are the capabilities the software creates as a byproduct of those gains.

When you connect the immigration database to the tax database to the law enforcement database to the healthcare database to the social services database to the financial transaction records, you do not simply create a more efficient government. You create a unified profile of every citizen that no prior government in history has possessed. The granularity of that profile, immigration status, movement history, social network connections, financial behavior, physical descriptors, medical history, and employment records make it possible to identify, locate, and act on any individual in the country with a speed and precision that human administration could never achieve. That capability is real regardless of who currently holds political power, regardless of what the software’s stated purpose is, and regardless of how benign the intentions of the people who built it.

The public record on this is not hidden. Government contracts are public documents in the United States. What they collectively show when read together, rather than in isolation, is a systematic effort by at least one major technology firm to become the operating infrastructure layer for the entire US government. Contracts spanning intelligence agencies, the military, immigration enforcement, tax administration, health agencies, and law enforcement, consolidated wherever possible into single enterprise agreements that create long-term dependencies, have together produced a situation where one private company’s proprietary software platform mediates an enormous and growing share of consequential government decisions about individuals. That company’s own public filings describe its platforms as the ‘central operating systems’ for its government customers, an aspiration stated explicitly in its public market documentation as becoming the ‘default operating system across the US.’

THE INFRASTRUCTURE FINANCE LENS

For readers of this book, there is a specific way to understand this risk that the general public rarely considers. In infrastructure finance, we care deeply about structural lock-in, the moment when a physical or contractual dependency becomes so deeply embedded that it cannot be undone without catastrophic disruption. A pipeline that serves an entire region cannot be decommissioned without replacing the energy it delivers. A data platform that integrates thirty agency databases and upon which thousands of government workflows depend cannot be replaced in a budget cycle, or a political cycle, or perhaps several of them. When a private company achieves that level of structural dependency with a government, it has effectively become infrastructure, and infrastructure, as this book has argued from its first page, is extraordinarily difficult and expensive to change once it is built. The difference between a pipeline and a government data platform is that the pipeline cannot decide what it carries.

The connection to global trade and infrastructure investment is not metaphorical. The United States is still the world’s largest economy and the anchor of the global financial system. The dollar is the reserve currency of international trade. The frameworks described throughout this book, project finance, trade finance, ECA guarantees, and commodity markets, all ultimately flow through or depend upon the stability, rule of law, and predictability of the US system. If the administrative machinery of that system is increasingly mediated by proprietary AI platforms operated by private companies with their own interests, their own political relationships, and their own data business models, then the question of who controls those platforms becomes a question about who, in a very practical sense, controls the conditions under which global trade and infrastructure investment occur. That is not a civil liberties question in the abstract. It is a question about the foundation of everything this book describes.

I am not suggesting this outcome is inevitable. I am suggesting that it is the direction the current trajectory leads if it is not actively redirected. The same researchers, investigative journalists, and civil liberties organizations that have raised these concerns in recent years have done so not to obstruct technological progress but to argue for the conditions under which that progress can be trusted. I share those concerns. And I believe that the people who build infrastructure and finance technology are precisely the people who should be paying attention to them, because the infrastructure of governance is infrastructure too, and it is being financed and built right now, largely out of public view.

The Specific Danger: Efficiency as Cover

The most insidious aspect of this pattern is that it uses the language of efficiency and waste elimination to normalize capabilities that would be politically unacceptable if described directly. Who could be opposed to streamlining government operations? Who could be opposed to connecting databases to eliminate fraud? Who could be opposed to modernizing legacy systems that have not been updated in decades? Each of these phrases describes something that sounds not only reasonable but obviously desirable. Each of them also describes a capability that, framed differently, most citizens would find deeply alarming.

Streamlining certain enforcement operations means building a system that can identify, locate, and process individuals with a speed and scale that human administration could never achieve, before legal counsel is engaged, before due process runs its course, before errors in the underlying data can be caught and corrected. Connecting disparate databases to eliminate fraud means creating a unified profile of every citizen from data sources they never consented to combine, managed by a private company with its own commercial interests in the value of that data. Modernizing legacy systems means making a private company’s proprietary platform the irreplaceable operating layer for functions that a democratic government is supposed to perform transparently and accountably on behalf of its citizens.

This is the core danger. The tools of efficient government are not neutral. They can be used to build the citizen-controlled, transparent CivicLedger I described in the sections above. They can equally be used to build something that looks like efficiency on the surface but functions as comprehensive administrative control underneath. The difference between those two outcomes is not technical. It is about who owns the data, who controls the algorithms, who can audit the decisions, and what recourse citizens have when the system makes an error or when it makes a decision that is not an error but a feature.

What Proper Oversight Would Actually Require

If AI-assisted government is coming, and I believe it is, because the efficiency gains are too large for any administration to permanently resist, then the question is not whether to accept it but on what terms. Here are the minimum conditions I believe any honest person should insist on, regardless of which political party holds power when this conversation finally becomes unavoidable.

First, no private company should own the operating system of a democratic government. The software that integrates government data, makes government decisions, and profiles government citizens should be open-source, auditable by genuinely independent third parties, and owned by the public. A proprietary platform that defines how government data is connected, categorized, and made searchable is not a trade secret; it is a map of how the government sees its citizens, and citizens have a right to see how that map is drawn.

Second, the databases that can be integrated must be legally defined and publicly disclosed before integration occurs. Every combination of government databases that a platform connects should require specific legislative authorization, not procurement approval, not administrative discretion, but a vote of elected representatives, and should be subject to audit by an authority that is financially independent of the agencies being audited.

Third, algorithmic decisions that affect individuals must be explainable in plain language and contestable through a process that a normal person can access. If a system determines that a person is a priority for enforcement action, a fraud suspect, a security risk, or ineligible for a benefit, that determination must be attributable to specific, documented data inputs and subject to challenge on those specific grounds. Opaque determinations that deprive individuals of rights or liberty are not consistent with due process under any defensible reading of constitutional law, regardless of whether the opaque actor is a human bureaucrat or a machine.

Fourth, the crowd approval mechanism I described earlier should apply explicitly to government AI deployments and data integration decisions. Any contract that gives a private company access to cross-agency citizen data should require supermajority legislative approval plus a defined public comment period. The current practice of expanding data access through procurement renewals that attract no public attention is precisely the opacity that the CivicLedger is designed to eliminate.

Fifth, AI systems used in government should be required to demonstrate neutrality through adversarial testing conducted by parties with no financial relationship to the system’s developers, not the company’s own ethics board, not the agencies whose data the system manages, but genuinely independent researchers with full access to training data, model parameters, and decision outputs, and the legal authority to publish their findings without modification.

None of these conditions is currently in place in any comprehensive form. Most of them are not being seriously debated at the level of policy that would need to adopt them. The contracts that matter most are already signed. The platforms are already embedded. The structural dependencies are already forming. That is the honest state of where we are, and why this chapter belongs in a book about financing infrastructure, because the infrastructure of governance is the foundation on which every other infrastructure decision in this book ultimately rests.

The Safeguard: Crowd Approval as a Constitutional Principle

Which brings me to the safeguard I think is most important, and which is also the one most directly connected to the themes of this book. Every major decision made by an AI-assisted government should require crowd approval, explicit, verifiable, blockchain-recorded consent from a qualified majority of affected citizens, before it is executed.

This is not a new idea. It is representative democracy, extended to its logical conclusion by the technology that makes genuine direct participation possible at scale for the first time in human history. The Athenians practiced direct democracy in the fifth century BC because their city was small enough that all citizens could gather in one place. The scale of modern nation-states made representative democracy the only feasible substitute. But representative democracy is a second-best solution, a mechanism for aggregating citizen preferences through intermediaries because direct participation was physically impossible. When physical impossibility is removed by technology, the justification for the intermediary weakens significantly.

What decisions should require crowd approval? At a minimum: any declaration of war or military engagement. Any infrastructure expenditure above a defined threshold, perhaps $500 million at the federal level, $50 million at the state level, and $5 million at the county level. Any new welfare program or significant modification to an existing one. Any tax rate change above a defined percentage. Any emergency spending authorization beyond a defined duration. Any public-private partnership in which a private entity receives exclusive access to public resources.

The technology to implement this already exists. Estonia does it. Utah’s DAO legislation provides the legal framework for blockchain-recorded collective decision-making. Wyoming’s stable token infrastructure demonstrates that government transactions can be executed on blockchain rails. The CivicLedger platform I described earlier is not technically difficult to build; it is politically difficult, because the people who would have to authorize it are precisely the people whose power it would diminish.

The Timeline: Starting at the Bottom

This is why the transformation, when it comes, will start from the bottom of the governance hierarchy rather than the top. No sitting Congress will vote to implement a system that replaces most of its members with an algorithm and citizen voting. No incumbent administration will deploy an AI transparency platform that makes its own waste visible in real time. But a city council in a mid-sized American city, facing a budget crisis and a population that does not trust it, might. A county government trying to compete for new business against a neighboring county with lower taxes and better services might. A state legislature that has watched local governments successfully pilot civic technology and is under pressure to adopt it might.

The diffusion of this technology will follow the same pattern as every major governance innovation in American history: it begins in the states and localities, the laboratories of democracy, where the stakes are manageable, and the experiments are recoverable. It proves itself through demonstrated results, lower costs, better services, higher citizen satisfaction, and measurable reduction in fraud and waste. And then, slowly, it propagates upward through the governance hierarchy as the political cost of resisting it exceeds the political cost of adopting it.

I do not know whether this will happen in ten years or fifty. I do not know whether the AI systems that enable it will be genuinely neutral or subtly distorted. I do not know whether the humans who build the platform will protect its integrity against capture or succumb to the temptation to use its power for their own ends. These are not technical questions. They are human questions, and humans have never had a particularly reliable track record of choosing wisely when power is available.

What I do know is this: the tools described in this book, the infrastructure finance frameworks, the trade finance platforms, the community equity models, the inventor financing pathways, are all tools for putting more economic power in the hands of more people. Every crowdfunding campaign that lets a community own a piece of its local infrastructure is a small experiment in the same idea. Every equity crowdfunding round that lets a garage inventor bring a technology to market without surrendering control to a single investor is a small experiment in the same idea. The CivicLedger is a larger version of the same experiment. The question is whether, as the experiments scale, the principles that animated them, transparency, distributed ownership, citizen participation, and accountability scale with them.

The Hope

Here is what I allow myself to believe, on the days when I am most optimistic. The generation that is growing up with smartphones in their hands and AI in their daily lives will not accept the opacity of nineteenth-century governance structures applied to twenty-first-century problems. They will not wait in line to vote on a machine that was designed in the 1960s. They will not trust institutions that cannot show their work. And they will build or demand that others build the systems that reflect their expectations.

The infrastructure revolution that could follow a genuinely efficient, genuinely transparent, genuinely citizen-accountable government is almost impossible to imagine in full. High-speed rail networks approved by citizen vote and funded by redirected waste savings, built at half the cost of current projects because the procurement process is clean. Energy grids modernized not over the forty-year timelines that current regulatory processes require, but on the timelines that the physics of climate change actually demands. Community-owned renewable infrastructure, financed through the same crowdfunding tools described in Chapter 17, is built in neighborhoods that currently export their energy dollars to distant utilities. A country that actually looks like the country its founders imagined, not because of the wisdom of its leaders, but because its citizens have, for the first time, the tools to hold those leaders genuinely accountable.

I am writing this at a moment when that future feels both closer and more threatened than at any point in my adult life. Closer because the technology exists. More threatened because the people who control the most powerful AI systems are also, in many cases, the people who would benefit most from those systems remaining unaccountable. The distance between those two observations is the most important frontier in human governance, and it will be crossed, one way or another, within the working lifetime of most people reading this book.

Where Does Everyone Go?

This is the question I find myself sitting with most quietly, in the hours after the analysis is done and the contracts are reviewed, and the financial models are closed. If AI runs governments, manages infrastructure, processes trade finance, underwrites projects, monitors construction, and optimizes operations, not partially, but comprehensively, then what happens to the developers? The traders? The bankers? The laborers who build the facilities, weld the pipelines, string the powerlines, and drive the heavy equipment? These are not rhetorical questions. They are the practical human consequence of the technological trajectory this entire book has been describing.

The honest answer is: they are displaced. Not all at once, and not without transition, these things never happen the way they are modeled in the anxious think-tank papers that proliferate whenever a new wave of automation arrives. But they are displaced. The infrastructure developer who today spends three years assembling a capital stack across six lender relationships in four countries will eventually find that an agentic AI system can do the same work in weeks, with fewer errors, better documentation, and no deal fatigue. The commodity trader who today relies on relationship networks, market intuition, and the ability to execute under pressure will find those edges competed away by AI systems that never sleep, never miss a price signal, and never let emotion affect a position. The independent engineer who today certifies construction progress by physically visiting project sites will find satellite imagery, digital twins, and computer vision doing the same work continuously and more comprehensively than any human can.

And below these knowledge workers sit the physical laborers, the people who actually build the world this book is about. The construction crews who pour the concrete and install the equipment. The truck drivers who move the equipment and materials. The refinery operators who run the processes. The port workers who load and unload the containers. Robotics and automation are already replacing these workers at the margins of every industry, and the replacement is accelerating. A $10 million per megawatt data center that requires months of intensive labor to build today will, within a generation, be assembled largely by robotic systems working around the clock with human supervision rather than human hands. The port that currently employs thousands of longshoremen is already measurably less labor-intensive than the port of twenty years ago, and the trajectory continues.

I want to be careful here not to simply repeat the comfortable reassurance that has been wrong in every previous version of this conversation throughout economic history: that new technology creates more jobs than it destroys, that workers displaced from one industry find their way to another, that the system equilibrates. That narrative has been broadly true for most of the industrial era; the agricultural workers who were displaced by mechanized farming did eventually find work in factories, and the factory workers displaced by automation did eventually find work in services. But the comfort of that historical pattern depends on the new opportunities arising faster than the old ones disappear, and on the new opportunities being accessible to the people being displaced rather than only to an elite with specific educational credentials and social capital. Neither of those conditions is guaranteed in an AI-driven transition, and there are serious reasons to doubt both.

Universal Basic Income: The Question We Can No Longer Defer

Which brings us to universal basic income, the idea that was once the province of academic economists and fringe political movements and has become, within the span of a decade, a mainstream policy proposal being piloted in countries from Finland to Kenya to Canada to the United States. UBI is simple in concept and enormously complex in execution: every adult citizen receives a regular, unconditional cash payment from the government, sufficient to cover basic needs, regardless of employment status, regardless of other income, regardless of behavior. No means test. No work requirement. No bureaucratic gatekeeping that consumes a third of the program’s budget before any money reaches anyone.

The question I find myself asking and that I think deserves to be asked more directly than it typically is in polite economic discussion is not whether UBI is affordable. If AI and automation eliminate the need for the majority of routine human labor, and if the productivity gains from that elimination are even a fraction as large as current projections suggest, then the economic output available to fund UBI becomes extraordinary. The question is not the money. The money, in a world of AI-managed government and AI-optimized production, will be there in a way it has never been before. The question is whether the political and social structures exist to distribute it equitably and whether the humans who receive it will know what to do with the freedom it provides.

Because that is the more interesting question. Not the economics of UBI but the anthropology of it. What do humans do when survival is guaranteed? What do they create when they are not exhausted from labor that machines can do better? What do they discover about themselves and each other when they have the time and the security to ask?

The da Vincis We Never Found

Here is the thought that I cannot let go of, that has stayed with me through the writing of this entire book, and that feels, more than anything else in it, like the seed worth planting.

Leonardo da Vinci was born in 1452 in a small Tuscan village, the illegitimate son of a notary and a peasant woman. He had access to education because his father recognized his talent and arranged for him to apprentice with a Florentine painter. That single act of recognition, one person noticing what was inside another person and creating a path for it to emerge, produced the most remarkable polymath in recorded history: painter, sculptor, architect, engineer, musician, mathematician, geologist, botanist, writer, cartographer, and inventor of machines that would not be built for four hundred years.

Nikola Tesla was born in 1856 in a Serbian village, the son of an Orthodox priest. He had access to education because his father, despite initially wanting him to join the clergy, recognized his exceptional mathematical ability and supported his studies. That single act of recognition produced the inventor of alternating current, the induction motor, the radio, the remote control, and dozens of other technologies that made the electrified world possible.

Now ask yourself the question I cannot stop asking: how many da Vincis and Tesla’s were born in that same century, in the same villages, in the same decades, carrying the same extraordinary potential, who spent their entire lives doing something else? Farming land that barely fed their families. Working in mines. Carrying loads. Tending animals. Dying of diseases that were already understood by the educated class but not accessible to them. Not because they lacked the capacity. Not because the gift was not there. But because no one recognized it, because no path existed for it, because survival consumed every hour they had.

We will never know their names. We will never know what they would have built, what they would have painted, what they would have discovered, what problems they would have solved that we are still carrying today. They are the invisible loss at the center of every economic system that has ever existed, the cost, measured not in dollars but in unrealized human potential, of organizing a society around the premise that most people’s primary purpose is to perform labor that someone else has determined is needed.

What changes if that premise is removed?

If AI and automation absorb the routine labor, the driving, the carrying, the sorting, the welding, the data entry, the compliance processing, the warehouse picking, the crop harvesting, the construction supervision and if the productivity gains from that absorption fund a universal income sufficient to cover shelter, food, healthcare, and education for every person on earth, then for the first time in human history, the answer to the question of what a person can become is not constrained by what their labor market will pay them to do. A child born in rural Mozambique with da Vinci’s visual intelligence and Tesla’s mathematical intuition would not spend her life farming because that is the only option available within walking distance of her village. She would have time. She would have access to knowledge, to tools, to a global community of others who share her interests. She would have the security to experiment, to fail, to try again.

We have never run this experiment. Not once in the history of civilization has a majority of the human population had the time, the security, and the access to develop their full potential. We have always reserved that for a small class whose labor was not required for survival, and the output of that small class, even so constrained, is the entirety of what we call civilization: every painting, every symphony, every theorem, every invention, every poem, every discovery. The cathedral builders and the shipwrights and the farmers who made that class possible are mostly unnamed. Their potential went largely unexpressed, not because it was not there but because survival left no room for it.

AI is the first technology in history with the genuine potential to change that equation. Not by making everyone’s labor unnecessary, at least not immediately, and not without enormous transition costs that must be honestly managed. But by progressively freeing larger and larger portions of the human population from the requirement to spend their most productive hours performing tasks that a machine can do better. Every hour reclaimed from necessity is an hour available for whatever it is that person was actually built to do. And in a world of eight billion people, the statistical likelihood that we have been missing thousands of da Vincis and Tesla’s is not small. It is near certainty.

Would some of those potential da Vincis have spent their reclaimed hours watching screens and doing nothing? Of course. Human nature does not transform overnight, and freedom is harder to use well than compulsion. But history suggests that when people have genuine time and genuine security, a remarkable number of them create not because they were told to, not because the market rewarded it, but because making things, understanding things, and connecting with other people are what humans, left to their own devices, actually want to do. The question of how to structure a society that converts the productivity gains of AI into the conditions for widespread human flourishing is the most important design challenge of the next century. It is harder than any engineering problem in this book. And it is, ultimately, what every financing framework, every technology deployment, and every infrastructure investment described in these pages is in service of, whether we say so explicitly or not.

The Thread That Runs Through

This book began with infrastructure, the pipelines, ports, and power grids that move everything the world makes and uses. It ends with a question about what humans do when infrastructure takes care of itself. Those two things are not as far apart as they appear. Every tool described in these pages, the project finance structure that lets a small developer access institutional capital, the equity crowdfunding platform that lets a garage inventor raise a pilot plant without surrendering to a single gatekeeper, the community ownership model that lets a neighborhood own the infrastructure it lives beside, the trade finance innovation that lets a small commodity producer access markets previously reserved for banks, every one of these tools is, at its core, a mechanism for distributing economic power more widely. For giving more people the resources to participate in building, owning, and deciding.

That impulse, more participation, more ownership, more transparency, more decision-making power in the hands of more people, is the same impulse that animates the CivicLedger platform, the crowd approval mechanism, and the argument for universal basic income. It is the same impulse that drove the JOBS Act, which let ordinary Americans invest in early-stage companies for the first time. It is the same impulse that drove the community development finance system, which directed patient capital to places that institutional investors ignored. It is the same impulse that drives every developer who structures a project so that a neighborhood can own equity in the asset being built beside them.

The world we are financing is not just a world of better infrastructure and more efficient trade. If we get this right, the AI governance, the oversight structures, the distribution of gains, the preservation of human agency in the face of automation, it is a world in which the person who would have been a great artist is not spending their life driving freight. In which the person who would have solved a problem that has cost humanity decades of suffering is not spending their life in a call center. In which the person who carries the next breakthrough in clean energy, or materials science, or medicine inside them has the time and the security, and the access to let it out.

I am writing this at a moment when all of that feels both closer and more fragile than at any point in my life. The tools exist. The financing frameworks exist. The technology exists. What does not yet exist, in sufficient quantity, is the wisdom to use them in ways that enlarge human freedom rather than constrain it. That wisdom is not a technical problem. It is a human problem, and it will be solved by people who understand both systems they are building and the people those systems are meant to serve.

That is why this book exists. Not just to explain how infrastructure gets financed. But to suggest, quietly, that the people who understand finance are not separate from the question of what kind of world gets built. They are among its most important architects. The capital goes where the financing frameworks point it. The technologies that get commercialized are the ones that find a path through the capital stack. The communities that own their assets are the ones where someone understood both the crowdfunding regulations and the CDFI system well enough to combine them. The inventors who change the world are the ones who found a platform and a legal structure that let them raise enough money to prove the idea without surrendering the vision.

None of that is inevitable. All of it requires people who understand the tools, who care about the outcomes, and who are willing to use one in service of the other.

The rest, as always, is up to us; maybe.

David Goodnight | Founder, Comnet International | Author, Financing the World We Trade In

• AI data centers consumed energy equivalent to all of Pakistan.

• Nuclear, geothermal, fuel cells, rare earth magnets, solar, and grid optimization are all being reshaped by AI tools.

• AI found more geothermal sites in three years than the industry found in thirty.

• Data center waste heat is raising neighborhood temperatures by up to 4 degrees Fahrenheit.

• A startup just raised $140 million to build floating AI data centers powered entirely by ocean waves, operational by August 2026.

There is a central irony at the heart of the current AI-energy buildout: the same technology driving unprecedented electricity demand is now being deployed across laboratories, drilling rigs, and transmission control rooms to help solve the supply problem it created.

The scale of AI’s energy consumption is worth stating plainly. US data centers drew 183 terawatt-hours of electricity in 2024, more than 4 percent of total national consumption. The International Energy Agency projects that figure reaches 426 terawatt-hours by 2030, a 133 percent increase in six years, equivalent to adding the entire current electricity consumption of Germany to America’s grid in less than a decade.

America’s investor-owned utilities have responded by committing 1.4 trillion dollars in capital spending through 2030, the largest coordinated utility investment in American history. Goldman Sachs projects a 160 percent increase in data center power demand by decade’s end. Increasingly, the grid is being planned around a single emerging customer class: AI infrastructure.

Solar, wind, nuclear fission, and natural gas are all being deployed at maximum pace. They are necessary yet not sufficient. One of the deeper problems is timing. Building new transmission lines takes five to ten years. Permitting a gas plant requires three to five. Grid interconnection queues in key US markets now stretch seven to ten years. AI data center demand is growing on a trajectory that none of these timelines can match. OpenAI can train a frontier model in months. Building the transmission infrastructure to power the next generation of models can take a decade. No policy intervention has yet closed that gap.

What published research now confirms, across six distinct domains, is that artificial intelligence is simultaneously attacking the supply side of this equation. Not in theory. In peer-reviewed journals, in funded laboratory programs, in commercial deployments, and in signed deals that have cleared institutional capital committees.

1. Nuclear Fusion: Compressing Decades into Years

Researchers at Ames National Laboratory have built an AI tool called DuctGPT, published in Acta Materialia, that compresses the search for fusion reactor materials from months to hours. The core problem is brutal: reactor walls must withstand plasma temperatures in the hundreds of millions of degrees (hotter than the sun’s core) without degrading. Finding metal alloys that survive those conditions once required years of trial-and-error laboratory work. DuctGPT turns it into a search query: specify the required properties, and the system returns ranked candidates. What took a research team years now takes an afternoon.

AI is reshaping the physics of plasma control as well. One of fusion’s persistent engineering problems is plasma instability: sudden disruptions that can damage reactor walls and halt operation. AI systems are now being trained to detect the early signatures of these events and intervene before they occur. Google DeepMind is applying reinforcement learning to plasma stabilization in collaboration with Commonwealth Fusion Systems. NVIDIA and General Atomics are building a digital twin of the DIII-D National Fusion Facility in San Diego, allowing engineers to stress-test reactor conditions virtually before any physical exposure. The UK government committed 45 million pounds to build a dedicated AI supercomputer at the Culham fusion campus, beginning operations in June 2026.

Private capital is not waiting for government timelines. Global private investment in fusion now totals more than 9.7 billion dollars across approximately 50 projects. Helion Energy’s Polaris prototype achieved 150 million degrees Celsius in February 2026, ten times the sun’s core temperature, operating with deuterium-tritium fuel for the first time in a privately developed machine. Helion holds a power purchase agreement with Microsoft targeting grid delivery by 2028.

2. Geothermal: More Discoveries in Three Years Than in Thirty

Geothermal energy is firm baseload power. It operates 24 hours a day regardless of weather and exists nearly everywhere beneath the surface. Its problem has always been finding viable sites efficiently. Historically that required expensive brute-force drilling, most of which came up dry.

Zanskar, a geothermal exploration company, says its AI models have accelerated geothermal discovery rates dramatically, identifying more conventional geothermal sites in three years than the industry had found in the prior three decades. The approach has been validated with successful drilling at Pumpernickel and Big Blind in Nevada, both sites previously believed to be barren. A systematic review of 183 peer-reviewed papers published in ScienceDirect in November 2025 confirms AI is now being applied across the entire geothermal lifecycle, from exploration to drilling optimization to plant operations.

Fervo Energy, backed by Google and Breakthrough Energy Ventures, uses fiber optic sensing and AI-enhanced monitoring to improve drilling precision. The company raised 462 million dollars in December 2025 and is preparing a 1.33 billion dollar IPO at a valuation of up to 6.5 billion dollars in 2026. Geothermal is the baseload answer that solar and wind cannot provide, and AI found it hiding in ground the industry had already written off.

3. Solid Oxide Fuel Cells: AI Perfecting a Technology Nobody Noticed

Solid oxide fuel cells have been in commercial use since the early 2000s. They generate electricity through a chemical reaction rather than burning fuel, which makes them significantly more efficient than gas turbines or diesel generators, converting 50 to 60 percent of fuel to usable power, compared with 30 to 40 percent for conventional alternatives. They use no water in normal operation, can be installed in 90 days without waiting years for grid interconnection approval, and can run on hydrogen or biogas using the same hardware.

AI did not invent this technology. It made it precise for a new purpose. Bloom Energy’s sixth-generation platform was redesigned specifically for AI data center workloads using computational modeling against operating data from 1.5 gigawatts of deployed systems worldwide. The redesign produced cleaner power output, faster response to shifting load demands, and integrated cooling, three problems that conventional backup generators cannot solve simultaneously. The results are confirmed in Bloom’s 2025 SEC filings.

The capital markets noticed. Between October 2025 and January 2026, fuel cell companies closed 7.65 billion dollars in binding data center power agreements in 90 days. Brookfield Asset Management committed up to a 5-billion-dollar deployment of Bloom fuel cells at AI factories globally. American Electric Power followed with a 2.65 billion dollar, 20-year agreement. Goldman Sachs, Morgan Stanley, and Evercore ISI have all published research validating fuel cells as a bankable primary power source. Oracle’s Project Jupiter in New Mexico, where 2.45 gigawatts of Bloom fuel cells replace planned gas turbines and diesel generators, is the largest announced fuel cell deployment for an AI data center anywhere in the world.

Low-water generation technologies are also becoming strategically important as hyperscale campuses increasingly face cooling-water constraints and local permitting resistance.

4. Rare Earth Magnets: AI Reading 67,000 Compounds at Once

Rare earth magnets are inside every EV motor, wind turbine generator, defense platform actuator, and data center cooling system. China processes approximately 90 percent of global rare earth elements. In April 2025, Beijing imposed export licensing requirements on seven heavy rare earth elements, causing weeks-long delays for drone manufacturers and demonstrating the supply chain leverage in stark terms.

Researchers at the University of New Hampshire trained machine learning models to autonomously extract data from thousands of scientific papers and built the Northeast Materials Database, a catalogue of 67,573 magnetic compounds. The AI identified 25 previously unrecognized materials that retain magnetism at high temperatures. Seven of the screened candidates were subsequently confirmed in existing scientific literature, validating the model’s accuracy. The study was published in Nature Communications in October 2025, funded by the US Department of Energy’s Office of Basic Energy Sciences. A parallel 2026 study in the Journal of Magnetism and Magnetic Materials used AI screening to identify three additional rare-earth-free candidates.

5. Perovskite Solar and Green Hydrogen: Closing the Efficiency Gap

Researchers at the Karlsruhe Institute of Technology used AI to work backward from the solar cell performance they wanted, identifying new materials that could deliver it, a reversal of the traditional laboratory process. Published in Science in late 2024, the AI-discovered materials pushed one cell’s efficiency to 26.2 percent, roughly two points above the baseline. In 2025, next-generation perovskite-silicon tandem cells reached 34.5 percent efficiency in laboratory conditions, a threshold that conventional silicon alone cannot cross. The first commercial-scale perovskite production facilities were announced in 2025.

On the hydrogen side, Japan’s National Institute for Materials Science used AI to discover high-performance materials for splitting water into hydrogen without the rare platinum-family metals that have historically made the process expensive. A March 2026 paper in Angewandte Chemie describes an AI system that predicts catalyst performance before any physical experiment is run, then refines its own hypotheses based on lab results. This is not AI assisting science. It is AI conducting it.

6. Grid Optimization and the Transmission Bottleneck

All of the generation technologies above require time to scale. In the gap, AI is doing something less visible but immediately valuable: reducing the friction inside the grid that already exists. But before optimization comes a more fundamental problem that energy professionals know, and the broader conversation has largely missed: the physical infrastructure to move power from source to load is itself the binding constraint.

Power transformer lead times in the United States reached 128 weeks in 2025, up from roughly 24 weeks in 2021, according to Wood Mackenzie. Generator step-up transformers average 144 weeks, with some orders extending to four years. Transformer prices have risen 77 percent since 2019. Sightline Climate tracked 12 gigawatts of 2026 US data center capacity announced across 140 projects; only 5 gigawatts was actually under construction. High-voltage substations, switchgear, and HVDC equipment face lead times of three to five years. Electrical equipment represents less than 10 percent of total data center cost and 100 percent of the bottleneck.

AI is being applied directly to this constraint. PJM Interconnection approved an 11.8 billion dollar transmission expansion plan in February 2026 and deployed AI-enabled tools to compress interconnection review cycles that currently stretch seven to ten years. High-voltage long-distance transmission, the infrastructure that moves power from where it is generated to where AI needs it, is a market projected to grow from 15.6 billion dollars in 2025 to over 22 billion dollars by 2030, with AI now being applied to accelerate both project design and real-time grid control. AI-driven planning tools are compressing timelines on transmission infrastructure that was previously constrained by the pace of human engineering teams. The DOE committed 1.9 billion dollars to transmission modernization in 2025. The constraint is structural, documented, and not yet solved.

A peer-reviewed scenario analysis published in Nature Communications Sustainability in May 2026 projects that AI-enabled grid optimization could generate net electricity savings of up to 130 percent of AI’s own consumption under high-efficiency scenarios, meaning AI could theoretically save more power than it uses, a finding the researchers themselves flag as requiring careful interpretation. Separately, researchers at Tufts University demonstrated in April 2026 that a new AI architecture can reduce the energy required to run AI models by up to 100 times without sacrificing accuracy.

AI Is Reindustrializing the Physical World

The AI boom is not just a software story. It is the largest physical infrastructure buildout since the interstate highway system. Every frontier model runs on a foundation of power transformers, substations, transmission lines, cooling systems, and the copper and rare earth metals that connect them. Morgan Stanley Research projects a power shortfall of roughly 49 gigawatts against projected US data center demand by 2028, a gap no algorithm can close. It requires steel, concrete, and industrial timelines. The United States has not faced an infrastructure expansion challenge of this scale since rural electrification. The AI buildout increasingly resembles the railroad era more than the software era.

The national security implications run deeper than most coverage acknowledges. AI dominance requires energy dominance. Energy dominance increasingly requires grid modernization. Grid modernization requires transformer manufacturing. Transformer manufacturing depends on mineral supply chains. And mineral processing, for the rare earths, copper, and specialty alloys that run through every element of this chain, is heavily concentrated in China. Beijing’s April 2025 export licensing restrictions on seven heavy rare earth elements were not an isolated trade action. They were a demonstration of leverage over the entire physical stack that AI depends on. The AI race is quietly becoming a grid race, a transformer race, and a minerals-processing race. The software industry is discovering, with some urgency, that it is now in the infrastructure business.

Geography is being rewritten in the process. AI data centers increasingly cluster around cheap power, abundant land, cooling access, and transmission proximity. Electricity-rich regions such as Nevada, Texas, Wyoming, parts of Appalachia, and the Nordic countries are becoming the next digital capitals. The next generation of technology hubs may be determined less by venture capital density than by megawatts available at the substation. AI is forcing the software industry into the capital intensity of the utility industry. The geography of computing is being pulled back toward the physical world.

Some of that geography is now moving offshore. Oregon-based Panthalassa, backed by Peter Thiel in a $140 million round announced in May 2026, is building self-propelled floating platforms called Ocean-3 that generate electricity from wave motion and run AI computing onboard. No land. No fuel. No grid connection. Results are transmitted to shore via low-Earth-orbit satellite. The ocean provides both the power and the cooling. The first units are targeted for deployment in the northern Pacific by August 2026. Panthalassa estimates 2,500 terawatt-hours of wave energy sit untapped along US coastlines alone. If even a fraction of that becomes recoverable, the geography of AI infrastructure expands from a land-constrained problem to a planetary-scale resource question.

What This Means for Infrastructure Finance

The research direction across all six domains is increasingly validated. The less-examined question is what happens next: when these technologies work at scale, who finances them, and how?

Many of these projects may ultimately follow the same financing logic that has underpinned every prior generation of first-of-a-kind energy infrastructure. The power purchase agreements already in place at Helion, Commonwealth Fusion Systems, and Fervo Energy are not just commercial milestones. They are the foundation of a project finance capital stack. A developer with signed offtake from Microsoft, Google, or a major utility has the same fundamental financing asset as an LNG terminal developer with a signed capacity agreement.

Brookfield’s involvement signals growing institutional confidence that fuel cells may become financeable infrastructure at hyperscale. When a firm of that scale underwrites a global AI factory portfolio on fuel cells, a new asset class has crossed from interesting to bankable.

Across all six domains, the pace of AI-driven development is outrunning the institutions built to finance and regulate it.

Many of these claims may not survive contact with execution. Fusion timelines have disappointed before and may disappoint again. Geothermal scaling at the speed Zanskar implies has not yet been demonstrated at national scale. Transformer bottlenecks may worsen before AI-assisted engineering compresses them. Permitting reform, the single most powerful lever available, has stalled repeatedly across administrations. And AI’s own energy efficiency gains, while real, have not historically outpaced demand growth; there is no strong reason to assume they will now. A peer-reviewed ASU study published this month found data center waste heat is already raising air temperatures in downwind Phoenix neighborhoods by up to 4 degrees Fahrenheit, in a city that recorded a 97-degree overnight low in 2023. The infrastructure problem is not only about watts. It is about heat, water, and the communities being built around these facilities.

But the deeper risk may not be execution failure at all. If AI makes power grids more efficient, accelerates materials discovery, improves data-center operations, and reduces the cost of computation, the result may not be lower electricity demand. It may be the opposite. In economics, Jevons Paradox describes the phenomenon where efficiency gains make a resource cheaper to use, which increases total consumption rather than reduces it. The National Academies has noted this risk explicitly in the context of data-center efficiency and electricity demand. AI may become the most consequential modern test of that principle. If intelligence becomes cheaper, more industries will use more of it. The question is not simply whether AI can reduce the energy intensity of computation. The question is whether civilization will consume every efficiency gain by asking machines to think more often, in more places, for more purposes.

For two decades, the digital economy appeared detached from the physical world. That detachment is ending. The next technology supercycle will not be constrained by code. It will be constrained by electricity, steel, copper, transformers, and the speed at which civilization can build.

The deeper paradox is that AI may not reduce humanity’s demand for energy at all. It may accelerate it.

Infrastructure is fate.

Sources: Acta Materialia, DuctGPT, Ames National Laboratory (2026); Nature Communications, University of New Hampshire (October 2025); MIT Technology Review, Zanskar geothermal (December 2025); ScienceDirect geothermal lifecycle review, 183 papers (November 2025); Carbon Credits, Fervo Energy IPO (2026); Science / KIT, perovskite inverse design (January 2025); Applied Physics Reviews, AI green hydrogen (September 2025); Angewandte Chemie, self-improving catalyst discovery (March 2026); Oracle / BorderPlex / Bloom Energy press release (April 27, 2026); Bloom Energy SEC filings FY2025–26; Helion Energy press release (February 13, 2026); Fusion Industry Association Global Report 2025; Wood Mackenzie, transformer lead time survey (Q2 2025); Sightline Climate, data center capacity tracking (2026); PJM Interconnection transmission expansion plan (February 2026); Markets and Markets, HVDC transmission market report (2025); DOE transmission modernization funding (2025); Power Magazine, AI grid delivery (April 2026); Nature Communications Sustainability, AI grid savings (May 2026); ScienceDaily / Tufts University, AI efficiency (April 2026); IEA Energy and AI (April 2025); Brookings Institution (April 2026); PowerLines Utility Capital Expenditure Analysis (April 2026).

David Goodnight is the founder of The Goodnight Group and Comnet International, and author of Financing the World We Trade In (2026). He has arranged more than $3 billion in project financings across twenty countries. A portion of book sales are contributed to the David Goodnight Scholarship Fund.

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David Goodnight of Austin, Texas, is the Founder of Comnet International and The Goodnight Group. David highlights the challenges within the energy transition, including cloud storage, AI computing, and data center emissions.

While conventional energy is continuously under attack, everyone with a smartphone is responsible for a significant and growing volume of Greenhouse Gas (GHG) emissions via data centers servicing social feeds and cloud storage. Data centers are a largely unpublished participant of GHG emissions and an accelerating public relations challenge for major tech companies and all businesses developing or incorporating AI into daily operations.

Cloud data and intensive computing are stored in gigantic structures filled with thousands of hard drive-bearing racks drawing a tremendous amount of energy, typically fossil sourced, and clean water to keep the equipment cool. Data centers are responsible for 2% of overall U.S. Greenhouse Gas emissions, when laptops and smartphones are included, the volume now exceeds 2% of global emissions. For comparison, the global fuel oil market is 3.6%, this is only the fuel that is responsible for marine transport. The total global electricity consumption from data centers is expected to climb as high as 1,000,000 GW hours by 2026.

Every data center is exploding with demand and equally correlated emissions. One companies’ emissions surged nearly 50% compared to 2019 according to its 2024 environmental report. A significant setback in its goal to achieve net-zero emissions by 2030. Anyone other than an uncontacted tribesman who attacks data centers is simply a hypocrite, knowingly or unknowingly. There is so much chatter and extreme policymaking surrounding the energy transition that society has lost touch with the definition of the word transition; it will take time, innovation, and considerable investment to reach net-zero that does not include crafty greenwashing. Dozens of major corporations and government bodies are making capital-intensive efforts on a daily basis to reduce their GHG footprint by supporting advancements in technology and critical calibration of existing solutions.

The AI market is expected to reach almost $2 trillion by 2030 which equates to modular data center growth of $25 billion to $81 billion. Yes, technology is scary, in fact, more people are afraid of robots than they are of their own death, and current technology has barely scratched the surface of the data and power demand of AI operated robots in our everyday lives.  When telephones were introduced in the late 1800s, the New York Times was quick to attack. One contributor went so far as to say that society would become “nothing but transparent heaps of jelly to each other.” Another insisted that it would make society lazy and anti-social. Humorous predictions that are far truer in today's world of social media and the growing preference for emoji driven text messages over all other forms of communication.

Factoring in the current anti-social growth among society, the same people attacking agriculture, aviation, or gas fired power stations that coincidentally charge their EV and smartphone may need a reality check as putting down your phone or saving that video to your private hard drive will reduce an individual’s personal emissions footprint. Thankfully, our world is brimming with brilliant minds and well-intentioned corporations that will eventually allow us to use the best of AI and watch as many social streams as we choose without Greenhouse guilt.

Presently, Comnet International is advising two separate patent holding technology companies. The first is a new method to generate hydrogen that is vastly more efficient and far more stable. The second is a cooling technology that will reduce the need for water and power for all data centers globally by a significant volume. Comnet anticipates that each complimentary technology will have small scale commercial plants online in Q2 of 2025. Pilot facilities have been operating flawlessly for over 1 year. Comnet recognizes that the ultimate goal is to escape this period of “transition” and pivot to a time of net-zero permanence.

An exciting idea that was recently explored and EU-funded is the ASCEND project, the exploration of space data centers powered by solar energy. ASCEND aims to deploy 13 space data centers into low orbit, with a capacity of 10 MW, by 2036. Each data center would measure nearly 70,000 square feet. Unfortunately, a 1 MW space data center could require 600,000 pounds of rocket fuel per year to keep it in a low orbit.

Several corporations are exploring subsea data centers that could have the same impact without the rocket fuel demand. It would be an outstanding achievement if subsea facilities were powered utilizing wave technology installed by WaveFarm or one of the other dozen companies attempting to harness the largest untapped renewable energy source on the planet, that being the continuous motion of the ocean.

Geothermal took a massive leap this quarter. Fervo entered a world-record energy contract with one the largest utility providers in California to support Fervo’s geothermal technology. Fervo chose to embrace techniques from the oil and gas industry to break up rocks, drive water through them horizontally (fracking), and collect the resultant steam to drive turbines at the surface. Historically, geothermal has been generated by drilling vertical bores that deliver water into the hot rocks lying beneath the Earth's surface, a near identical protocol to conventional oil. Geothermal has been restricted to very specific geographies for the past century, but the Fervo program will undoubtably accelerate the potential for geothermal power in several new regions, such as Texas and Argentina, where fracking is embraced in the shale basins.

Comnet plans to engage with corporations and governments to assist in servicing the global demand for data processing. Collectively, Comnet is certain that several advancements and new methodologies will keep our skies and oceans uninhabited by our need for computing.

David Goodnight of Austin, Texas, is the Founder and Managing Partner of The Goodnight Group and Comnet International. The Goodnight Group is an investor-developer and Comnet International is a project finance, technology, and trade advisor. Both companies have separate interests in piloting the world forward with new ideas and financing solutions.

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