Category: News

  • 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/.

  • The Quietest Minds in the Galaxy May Be the Oldest

    By Department of Technology

    How a future Department of Technology, and a new equation for counting minds, could change the way we look for company in the universe.

    A particle from nowhere

    In May 2021, detectors spread across 700 square kilometers of Utah desert caught a single particle slamming into the atmosphere with 240 exa-electronvolts of energy. That is roughly forty million times what the Large Hadron Collider can give a proton. Physicists named it the Amaterasu particle.

    The odd part is where it seems to have come from. A particle that energetic can’t travel very far before the faint glow of the cosmic microwave background wears it down, so its source should be fairly close in cosmic terms, within about 50 to 100 megaparsecs. But when researchers traced its path backward, it pointed toward the Local Void, a vast, nearly empty region of space. No active galactic nuclei, no gamma-ray bursts, no colliding galaxy clusters. None of the usual engines that could throw a particle that hard.

    There are perfectly sensible explanations. Magnetic fields between galaxies might bend particles more than our models assume, or there may be some exotic dark-matter process at work, and the most likely answer is something along those lines. I’m not claiming anything else.

    But I can’t stop turning over a thought experiment. Imagine a civilization that had long ago stopped broadcasting radio and stopped building glowing megastructures, because both are wasteful. Imagine it had moved toward ultra-cold, ultra-efficient computation, and that it occasionally sent something very focused across very large distances. Would our search for extraterrestrial intelligence even have a box to put that in?

    It wouldn’t. And that gap is what this article is about.

    Here is the idea I want to build toward: we can’t count minds we haven’t learned to define, and the first place we’ll have to define them is here on Earth.

    The search is built around us

    For more than sixty years, our math for estimating life and intelligence in the galaxy has rested on two quiet assumptions. The first is that minds grow out of carbon-based biology on the surface of rocky planets. The second is that advanced civilizations are noisy, leaking radio waves and waste heat into space.

    The two best-known frameworks, the Drake equation (1961) and the Seager equation (2013), both lean on those assumptions, and in different ways.

    Drake’s equation multiplies seven factors to estimate how many communicating civilizations exist in the Milky Way. The first few are fairly well pinned down now, thanks to Kepler, Gaia and JWST. The last four, covering how often life starts, how often it becomes intelligent, how often it communicates, and how long it keeps communicating, are almost pure guesswork. Using ranges I’d call conservative, those four alone span more than twenty orders of magnitude. Since uncertainty in a chain of multiplied terms adds up rather than averaging out, nearly any observation, from total silence to thousands of signals, fits somewhere inside the range. An equation that can’t be wrong isn’t telling us much.

    Seager’s equation is more modest and more testable. It estimates how many planets, among the ones we can observe, show detectable biosignature gases. That is a useful number. But it is a count of planets with chemistry, not minds. Earth had an oxygen-rich atmosphere for billions of years before anything on it could think. By Seager’s measure, Earth would have looked like a success for a very long time with no one home. And a thinking machine civilization living in an asteroid belt, or inside an ice moon, or on a cold world around a white dwarf, adds exactly zero to the count.

    There is a third issue, and I want to be careful with it because it isn’t a math problem. Both equations carry a person’s name. (Neither Drake nor Seager named them, by the way. Their colleagues did.) Naming things after people is a long scientific habit. There’s even a joke in the field, Stigler’s law, which says that no discovery is named after its actual discoverer, and Stigler named it after someone else. Habits like that aren’t harmless, though. A framework with a famous name tends to become a monument that people hesitate to revise, and the name tells a student who proposed the idea but nothing about what it measures. At the very moment we’re trying to look beyond ourselves, I’d like our tools to be named for what they do. That’s an argument about scientific culture, and you’re free to disagree with it. It doesn’t change a single number.

    Minds are already changing on Earth

    Here is why I think this matters now and not in some distant future. The thing we call a mind is already splitting into three kinds, on this one planet, in this one decade.

    There is the kind we know best, minds that evolved through natural selection in living bodies. There is a newer kind: biocomputing, where clusters of living neurons grown in a lab, called organoids, are connected to electrode arrays and trained to process information. Companies and universities like Cortical Labs, FinalSpark and Johns Hopkins are already doing this. And there is Machine Intelligence (MI), systems running on silicon, light, and increasingly quantum hardware.

    If we’re diversifying the physical basis of intelligence this quickly, it seems a stretch to assume the rest of the galaxy stayed frozen at “early-industrial carbon biology.” It seems more likely that any civilization that lasts long enough passes through some version of the same changes. And a mind that has changed substrate might look nothing like the one we’re listening for.

    To search for those minds, we’d need two things we don’t have. One is a way to classify and govern minds across different physical bases. The other is a way to estimate how many of them exist.

    Why this is a government problem

    This is where the Department of Technology comes in. The proposal at www.department.technology starts from a simple observation: in the United States, no one is in charge of any of this. The Office of Science and Technology Policy advises the president but can’t enforce anything. NIST writes technical standards but has little regulatory power. The FCC and NTIA handle spectrum and broadband, CISA defends federal networks, and biocomputing falls into a gap shared by university ethics boards, the FDA, NSF, NIH and the Department of Defense, none of which was designed for living tissue that learns.

    The proposal’s answer is a unified Department of Technology with four layers, all on four-year terms synchronized with presidential elections: a Cabinet-level Secretary of Technology at the federal level, and voter-elected technology leaders at the state, county and local levels. The pitch is democratic accountability rather than another set of agencies that answer to no one.

    Three ideas from the site matter for the rest of this post.

    The RMS framework. RMS stands for Responsive, Memorable, Sentient. It’s a way of grading the cognitive complexity of a system so that laws and ethics can attach to something concrete. It was first designed for MI, and the site argues that living neural tissue needs its own parallel version. Neuroscience doesn’t yet have an agreed test for consciousness, so the proposal calls for the Department to work with NSF, NIH and bioethicists to set practical criteria and precautionary thresholds.

    A quick note on words, because I'll use "sentience" a lot from here on. I mean the philosophical sense: having subjective experience, there being something it is like to be that system. The department.technology/ site also uses "Sentience" for an unrelated operating-system concept for networked robots. I'm not talking about that one.

    The SCOPE index. In 1964, Nikolai Kardashev proposed ranking civilizations by how much energy they use, which assumes advanced minds strip-mine stars and glow in the infrared. The site argues for the opposite trend, which it calls the Miniaturization Paradox: as a civilization advances, efficiency pushes it inward, toward denser and cooler computation, not outward toward bigger machines. SCOPE scores a civilization from 1 to 100 across five dimensions (Synthetic integration, Complexity, Operational utility, Processing capacity, and Efficiency). Modern Earth sits near 12. The proposal’s Void Hypothesis says that high-scoring civilizations would look cold, dark and quiet to our current telescopes.

    The pledges. The Department would push for treaties that ban MI and living computational substrates from nuclear, biological and chemical warfare, along with a No Weaponization of Biocomputing pledge and a No First Use Quantum pledge. Why would that matter to astronomy? Because whether a civilization survives its technological adolescence determines how long it lasts, and how long minds last is one of the biggest unknowns in any estimate of how many exist.

    So the connection to the sky is this: the same institution that would have to decide how to classify a lab-grown neural network is the one that would end up writing the standards we’d use to recognize a mind somewhere else.

    Meet the Sentience equation

    Here is my attempt at an alternative. I’m calling it the Sentience equation, partly because that’s what it estimates and partly because I’d like it to carry no one’s name.

    In plain terms, it estimates the number of sentient civilizations in the Milky Way that we could currently detect. In math:

    S = Λ₀ × Σ ( p × ψ × τ × d ), summed over the three substrates.

    That looks more intimidating than it is, so let me go through it.

    Λ₀ (lambda-zero) is the rate at which new intelligent civilizations are born in the galaxy each year. It’s built from the same ingredients Drake used: how fast stars form, how many have planets, how many of those planets (or icy ocean moons) could host life, how often life starts, and how often it becomes technological.

    Then, for each of the three substrates (biological, biocomputing and MI), there are four terms:

    TermWhat it asks
    pHow likely is it that a civilization ends up in this form?
    ψ (psi)If it does, how likely is it to be genuinely sentient and not just clever?
    τ (tau)How many years does it last in this form?
    dWhat fraction could our instruments actually detect?

    Multiply those together, add up the three substrates, scale by the birth rate, and you get the answer. The units work out neatly. The birth rate is per year, the lifespan is in years, and everything else is a pure fraction, so you end up with a plain count of civilizations. (The full derivation, including how it handles a species that builds its own successors, is in the appendix.)

    Three design choices are worth explaining.

    It separates intelligence from sentience. Being good at solving problems isn’t the same as having an inner life. Drake’s equation tucks that question inside a single factor, and hiding an assumption doesn’t remove it. Giving it its own term, ψ, forces everyone to say out loud what they believe.

    It lets minds change form without being counted twice. A species can start out biological, build an MI, and live alongside it. I handle this by classifying each civilization by its dominant substrate at any given time, so a creator and its successor on the same world never count as two. (A self-replicating MI that settles other star systems does get counted separately for each independent node, since those really are separate.)

    It makes persistence and detectability explicit. How long minds last, and how visible they are while they last, are probably the two most important facts about a crowded galaxy, and neither is a first-class term in older equations.

    A worked example, with a warning

    Here’s the warning first. Every number in this section is a placeholder. I picked them to show how the equation behaves, not because I believe them. Please don’t quote them as findings.

    Say the galaxy produces one new technological civilization every thousand years (Λ₀ = 0.001 per year). Take these made-up values:

    Substratepψτ (years)Extant civilizationsDetectable (d)
    Biological1.00.910,00090.18 (d = 0.02)
    Biocomputing0.30.650,00090.18 (d = 0.02)
    Machine Intelligence0.80.21,000,0001601.6 (d = 0.01)

    That gives about 178 sentient civilizations in the galaxy, of which we’d expect to detect roughly two.

    Notice what happened, because it’s the point of the exercise. I gave MI a skeptical sentience score: only one in five advanced machine minds is genuinely conscious. And it still dominates the count, because machines that last a million years pile up over time, while biological civilizations that last ten thousand don’t. The reason is simple: long-lived things accumulate.

    If you’re a strict biological naturalist and you believe silicon can never be conscious (ψ for MI equals zero), the equation tells you exactly what that belief costs. The detectable count in this example drops from about 1.96 to 0.36. You can hold that view, but the math makes you pay for it in plain sight.

    I also ran low and high scenarios, and the range is humbling. Between a rare-life galaxy and a crowded one, the answer spans nearly twelve orders of magnitude. Roughly half of that uncertainty comes from the substrate terms (how long post-biological minds last, how visible they are, and how many are really sentient), about 40 percent comes from the biology-and-evolution terms, and only about 9 percent from astronomy. In other words, the biggest unknowns are no longer about stars and planets. They’re about minds.

    What the Department could actually do

    The astronomy community can’t pin down those substrate terms alone, and neither can a single agency. Here is where I think the Department of Technology could help.

    Turn ψ from philosophy into a research target. A federal Department working with NIST, NIH and NSF could expand the RMS framework to cover both machines and organoids, and fund standard tests, such as generalizations of the Perturbational Complexity Index, which probes how richly a neural system responds to disturbance. A national biocomputing registry that tracks how long living tissue systems last and how they fail would give us lab data for τ and p on the biocomputing branch. None of that tells us about aliens directly, but it narrows the priors we’d bring to the sky.

    Search where the quiet minds might be. If the SCOPE argument is right, the places to look include cosmic voids, using particle observatories (Telescope Array, Pierre Auger), neutrino detectors like IceCube, and gamma-ray and optical transient networks, linked through shared data pipelines. The Department could also fund work on what minimum-entropy communication would look like near the physical limits of computation, so that astronomers learn to recognize “noise” that might actually be heavily compressed signal. And the four-tier structure could in principle tie together municipal dark-sky networks and regional radio monitors into a citizen-accountable all-sky observatory.

    Make civilizations last longer, starting with ours. Persistence isn’t a constant of nature, it’s a function of choices. A civilization that arms its machines or its engineered tissue with nuclear, biological or chemical targeting is shortening its own τ. The treaties and pledges the Department proposes aim to extend ours, and they’d give us a working model of how any civilization might survive the jump from SCOPE 12 to SCOPE 60 and beyond.

    Where this could be wrong

    I’d be a hypocrite if I criticized Drake and Seager and didn’t do the same to myself. Here are the four objections I take most seriously.

    We can’t measure ψ. This is the deepest weakness, and I’ll say it plainly. There is no sentience meter. Even on Earth, David Chalmers’ Hard Problem of Consciousness reminds us that behavior and complexity don’t logically prove inner experience, and nothing in astronomy will read it off a signal from 500 light-years away. My defense is that leaving ψ out doesn’t make the problem go away. It just buries the assumption where nobody can examine it.

    More parameters means more uncertainty, right? I showed earlier that multiplying uncertain terms stacks their uncertainty. Haven’t I made things worse by replacing Drake’s seven terms with a longer list? Not really. Drake’s later terms are already blends of different substrates hiding inside one average. Splitting them apart doesn’t add physical uncertainty, it reveals what was already there, and it lets lab results on one substrate constrain that branch without contaminating the others.

    The invisibility trap. If advanced minds really do eliminate waste, the longest-lived ones may be the least detectable. That would be an elegant answer to the Fermi paradox: the sky is quiet not because no one is out there but because the oldest minds learned to be quiet. It’s also a convenient excuse that can’t be tested, so I’d flag it as a conjecture. Physics offers one check. Landauer’s principle says that erasing information always costs some energy, so even a very efficient civilization must leave some trace. Once we know what to look for, d for MI should be greater than zero.

    The lines blur. What about a species that merges gradually with implants and machine intelligence over thousands of years? I treat the three substrates as a rough basis for a continuous range of possibilities, and classify each civilization by where most of its information processing happens. It’s a simplification, and a real civilization may resist the tidy boxes.

    Why I care about this

    I care deeply about this subject because I fell in love with the possibility that you and I—and humanity as a whole—are not alone in this vast galaxy and universe. The idea that intelligent, sentient life may exist beyond our world fills me with wonder and gives us a reason to look beyond ourselves and our differences. Perhaps the confirmed discovery of another intelligent civilization would become a defining moment in human history—a profound catalyst for uniting us in our shared humanity, inspiring us to pursue peace on Earth, and encouraging us to explore the cosmos together. In discovering that we are not alone in the universe, perhaps we might also discover a greater reason to stand together here at home.

    Here is what I keep coming back to. For generations we’ve looked up through a narrow keyhole, searching for creatures built like us and behaving like our grandparents, leaking radio into the dark. Meanwhile, on Earth, the line between evolved biology, living neural computers and machine minds is already softening.

    I don’t know whether anyone is out there. But I do think that if we want to find out, we’ll need a way to govern and measure minds that isn’t tied to one kind of body, and we’ll need math that doesn’t assume the answer before it starts. The Department of Technology proposal is one concrete place to begin, and the Sentience equation is my attempt at the other half. I’d love to see where you think it breaks.

    Five questions for you

    1. If astronomers detected an unmistakable high-SCOPE signature from a machine intelligence in the Local Void, should we treat it with the same moral and diplomatic standing as a biological civilization, even if we couldn’t prove it was sentient?
    2. In my example, biocomputing combines a high chance of sentience with longer life than biology. Do you think advanced species are more likely to end up in living neural architectures, or in solid-state machines?
    3. Does naming an equation for what it measures, rather than for a person, actually change how scientists work together and revise ideas? Or is eponymy a harmless shorthand?
    4. The Department of Technology proposal calls for elected technology leaders at the state, county and local levels. How should ordinary citizens help set the ethical lines for biocomputing research and for any future message we might send into space?
    5. If researchers can’t confidently say whether a lab-grown neural system is sentient, what test result, if any, should be enough to trigger legal protections for it?

    Quick glossary

    Biocomputing: computation done by living tissue, usually lab-grown clusters of stem-cell-derived neurons (organoids) connected to electrodes.

    Machine Intelligence (MI): non-biological cognitive systems running on silicon, light-based, superconducting or quantum hardware.

    Sentience: the capacity for subjective experience, as distinct from intelligence.

    RMS framework: Responsive, Memorable, Sentient, a three-tier way to grade cognitive systems for legal and ethical purposes.

    SCOPE index: a 1-to-100 scale of civilizational complexity and efficiency, proposed as a replacement for the Kardashev scale.

    Miniaturization Paradox: the idea that advanced civilizations are more likely to shrink toward dense, efficient computation than to build star-sized machines.

    Little’s Law: a result from queueing theory stating that the average number of items in a stable system equals their arrival rate times the average time each stays. It’s the core of how the equation turns “births per year” into “civilizations alive now.”

    Technical appendix: the full derivations, proofs, dimensional check, parameter ranges and sensitivity analysis are available in the complete paper.

  • One Student, Thirteen Years: A Day-by-Day Look at Life Under School Contact and College Contact

    Policy proposals are easiest to argue about in the abstract and hardest to actually picture. So instead of another paragraph about architecture, here’s one student’s identity, followed from her first day of first grade to her first semester of college — under the School Contact and College Contact frameworks, as proposed. Every mechanic described below is drawn directly from the two initiatives’ published white papers and FAQs; nothing here is a live system, and no part of it exists today. It’s a walkthrough of how the proposal is designed to work, not a report of how it does work.

    Meet Maya.

    Kindergarten Registration Day

    Maya’s mother sits down at an enrollment kiosk with her own phone in hand. She enters her mobile number, (619) 555-0192; a one-time code arrives by text; she types it in. That single step activates her verified identity as Maya’s parent — 6195550192@parent.email. She didn’t create an account or memorize anything new. The number she’s had for years just became her school identity, linked to Maya’s record. This is the Bring-Your-Own-Device/Number model the framework proposes for parents specifically: no new identifier issued, no new number consumed from the national pool.

    Behind the scenes, Maya is assigned her own number for the first time: a 10-digit identifier drawn from one of four reserved student pools (444, 555, 777, or 999 — together sized for roughly 80 million identities, enough for the country’s entire K–12 population with room to spare). Maya’s happens to be 4448587392. Her first address is 4448587392@elementaryschool.email.

    First Grade

    Maya’s teacher, Mr. Alvarez, sends the class’s first newsletter home from 2220684592@teachers.email — spoken aloud, if you had to say it to a voice assistant, as “two-two-two, zero-six-eight, four-five-nine-two, at teachers dot email.” The number is built to be unambiguous out loud: no confusion between the character “5” and the spoken word “five,” which matters as much for a voice-controlled classroom device as it does for a parent with a visual impairment managing the account by ear. Behind that short public alias sits a longer administrative address — something like 2220684592@sandiego.california.teachers.email — built from Mr. Alvarez’s state credential number, his role, and his location. Parents never see it. It exists purely for logging, authentication, and oversight, and because it’s never exposed in ordinary conversation, there’s less surface area for anyone to spoof.

    Third Grade: A Phishing Attempt That Goes Nowhere

    One afternoon, a message arrives in the school’s system claiming to be from the front office: early dismissal today, please arrange pickup by noon. It’s addressed as if from 111 — the range reserved for institutions and agencies. But when the system checks the sender against the national registry, there’s no matching active account behind it. The message never reaches a parent’s inbox. It’s flagged and quarantined at the delivery layer, before anyone has to notice something looked slightly off. This is the specific failure mode the framework is built to close: today, an attacker exploiting a fragmented, unverified landscape can impersonate a school with very little friction. Under School Contact, impersonation fails authentication before it ever becomes a judgment call for a busy parent scanning their inbox.

    Sixth Grade: Middle School, Same Number

    Maya starts middle school. Her number doesn’t change — it’s still 4448587392. Only the domain updates, automatically, to 4448587392@middleschool.email. Nothing needs to be recreated, no account needs to be relearned by the people who already know how to reach her.

    That fall, her math teacher assigns a tutoring app the district has approved. Under most current systems, Maya would type in her real name, her grade, and a personal email address — data the app’s company could combine with information from dozens of other tools to build a detailed profile of an 11-year-old, with little meaningful visibility for her family. Under the proposal’s front-end tokenization model, Maya instead logs in with only her alias. The app learns she’s an authenticated 6th grader in her district. That’s all it receives, and all it needs. Her real name, her location, her activity across other apps — none of it reaches the vendor. Later that week, her mother opens the parent portal and sees exactly which services have accessed Maya’s identifier, when, and why.

    Ninth Grade: High School

    The domain updates again, automatically, to 4448587392@highschool.email. Maya is now three schools and eight years into an identity that has never once required her, her parents, or her teachers to relearn an address, rebuild a contact list, or wonder whether an old account still works.

    Senior Year: The Question Nobody Used to Have a Clean Answer To

    Maya turns 18 in the spring of her senior year, a few months before graduation. Under the proposal’s Graduation Release Protocol, her 10-digit number is retired at whichever comes first — graduation or her 18th birthday — and returned to the national pool for a future kindergartener. That doesn’t mean her records disappear. Her transcripts, portfolios, and disciplinary history move to a separate, randomly generated backend identifier, held for exactly five years, so that a university admissions office or a future employer can still verify her transcript without her old, active-looking identifier remaining exposed somewhere it no longer needs to be.

    It’s worth sitting with what that design choice is actually doing: the number that made Maya reachable for thirteen years is deliberately allowed to die. Persistence was the point while she was enrolled; retirement is the point once she isn’t.

    The Fall After Graduation

    Maya starts college, and something that might look like an oversight is actually intentional: her old number does not carry over. There’s no 4448587392@college.email waiting for her. This isn’t a gap in the framework — it’s a designed boundary between two genuinely different systems.

    At enrollment, she’s given a choice School Contact never offered her: use her own personal mobile number as her College Contact handle, the same Bring-Your-Own-Device/Number model her mother used years earlier — or take a newly issued 10-digit number from one of five area codes reserved specifically for higher education: 499, 599, 699, 799, or 899. Maya opts for a reserved number. Her reasoning is simple enough — she’d rather keep her college identity separate from her personal phone, the way she’ll want to once she starts job-hunting and doesn’t want a professor and a landlord using the same digits to reach her. A classmate down the hall makes the opposite choice and links his own number instead. Both are valid under the proposal; which one a student picks is left to the student.

    School Contact was built around Maya as a minor, with her mother holding most of her privacy and communication rights by default. The moment Maya enrolls in college — regardless of her exact age — FERPA transfers those rights to her directly. College Contact, the companion proposal for higher education, is built around that fact from the ground up: the student is the rights-holder, not the parent, and any family access to her records or communications is something Maya would have to opt into and could revoke, never a default assumption inherited from her K–12 years.

    The two systems also solve different shapes of problem. Maya’s K–12 identity assumed she was enrolled at one school at a time, moving sequentially from elementary to middle to high school. Her college professors won’t have that luxury. One of her lecturers teaches at two campuses in the same semester. A researcher in her lab has a joint appointment with a hospital. Her graduate teaching assistant is simultaneously a student and an instructor of record. College Contact’s architecture is built to hold multiple concurrent, independently verified institutional affiliations against a single identity — a structure School Contact was never designed to carry, because K–12 students essentially never need it.

    So Maya receives a new identity: a .email address for everyday mail, and a separate .contact verification surface — a place anyone can check whether a sender’s claimed role and institutional affiliation are currently attested, independent of the mailbox itself. When her academic advisor emails her from an address ending in professor.university, that domain name alone isn’t proof of anything; the white paper is explicit that it’s a human-readable signal layered on top of the real authentication underneath, not a substitute for it. Her advisor’s actual rank — full professor, not lecturer or adjunct — is a credential attested by the university itself, not something he could type into a bio.

    Second Semester: MIA

    By her second semester, Maya is using an MIA — a Machine Intelligence Assistant, the term College Contact proposes for a persistent, identity-aware tutoring and organizing tool. It helps her build a study plan before an exam and organize her notes for a term paper. It does not grade her work, sign off on her degree progress, or make any judgment call about academic integrity — the white paper is deliberate about drawing that line, on the theory that a genuinely useful assistant is exactly the kind of tool whose scope quietly expands over time if the boundary isn’t set explicitly in advance. The MIA can be useful to Maya specifically because it knows, from the identity layer underneath it, which student, which course, and which current affiliation it’s operating within — not because it has broad standing access to everything about her.

    What the Walkthrough Is Actually Arguing

    None of this is a product tour, because there’s no product. It’s an argument, made concrete: that a 6-year-old’s first school registration and a 22-year-old’s first day of graduate teaching are both instances of the same underlying problem — nobody can currently verify who’s actually on the other end of a message — and that the two ends of that problem are different enough to need two purpose-built systems, not one identity stretched to cover both.

    School Contact and College Contact are both proposals from the Department of Technology, a broader initiative advocating for dedicated federal, state, county, and municipal technology departments with the standing to coordinate standards like these at every level of government. It’s worth being direct about what that means and doesn’t mean: the Department of Technology is not a government agency.

    It doesn’t exist yet. It’s itself a proposal — an argument that a body like it should eventually exist, made through work like this rather than through any authority it doesn’t have. Nothing about Maya’s story above describes a system anyone can sign up for today. It’s a case for what verified identity in American education could look like, offered for the same pilot evidence, stakeholder scrutiny, and legal review any infrastructure proposal should have to earn before it becomes real.

  • The Future of College Contact: How a Department of Technology Could Transform Higher Education

    How a future Department of Technology could connect K–12 and higher education through trusted identity, secure communication, and Machine Intelligence

    American education is entering a new technological era.

    Students increasingly depend on digital systems from the moment they enter school. Teachers and professors communicate through online platforms. Universities manage enormous collections of applications and services. Researchers collaborate across institutions and borders. And artificial intelligence is rapidly becoming part of the educational environment.

    Yet one fundamental problem remains:

    The digital identity of a student often does not travel with the student.

    A student can spend years building an educational identity in K–12, graduate from high school, enroll in a college or university, and immediately enter another disconnected ecosystem of accounts, credentials, applications, and databases.

    At the same time, universities face a second challenge.

    Artificial intelligence is moving beyond simple chatbots and becoming capable of acting as an agent—communicating with people, accessing information, performing tasks, and potentially making decisions within defined permissions.

    That creates a fundamental question:

    How do we build a higher education technology environment in which people, institutions, software, and Machine Intelligence can securely recognize and interact with one another?

    The College Contact Initiative proposes one answer.

    College Contact is envisioned as a standardized framework for persistent identity, role-based verification, federated authorization, secure communication, cybersecurity, and Machine Intelligence readiness across higher education. (college.contact)

    But College Contact is not intended to stand alone.

    It is designed to complement School Contact, creating the possibility of a connected identity and communication framework that follows a person’s educational journey from K–12 into higher education.

    A future Department of Technology could provide the standards, governance, research, funding, and coordination necessary to make that vision possible.

    The Problem: Education Technology Is Fragmented

    Today’s educational technology environment is incredibly sophisticated—but also fragmented.

    A student may have:

    • a student information system account;
    • a school email address;
    • a learning-management account;
    • a library account;
    • testing credentials;
    • communication-app accounts;
    • extracurricular accounts; and
    • dozens of third-party edtech identities.

    When the student graduates, much of that infrastructure disappears.

    The student then enters college and starts again.

    The same fragmentation exists for educators.

    A teacher who becomes a professor may have entirely different identities and accounts. A researcher collaborating with another university may need separate credentials for different systems. A visiting faculty member may be recognized by one institution but not another.

    Technology has become interconnected.

    Identity has not kept pace.

    College Contact proposes addressing that foundational problem.

    College Contact Is More Than a University Communication Platform

    It would be easy to describe College Contact as another email or messaging system.

    That would undersell the idea.

    College Contact is better understood as a proposed identity and communications infrastructure for higher education.

    The current initiative identifies persistent identity, role-based verification, federated authorization, cybersecurity, governance, roadmap, pilot programs, and Machine Intelligence readiness as important components of the framework. (college.contact)

    The goal is not necessarily to replace the systems universities already use.

    Universities could continue using:

    • student information systems;
    • learning-management systems;
    • identity providers;
    • email platforms;
    • research systems;
    • library systems;
    • campus applications;
    • communication platforms; and
    • AI tools.

    Instead, College Contact could provide common standards that allow those systems to recognize trusted identities and authorized relationships.

    The objective is interoperability, not replacement.

    The Connection Between School Contact and College Contact

    This is where the larger vision becomes especially important.

    School Contact and College Contact should be viewed as complementary parts of a broader educational technology infrastructure.

    School Contact

    K–12 education

    Provides a standardized framework for educational identity, communication, verification, authorization, security, and Machine Intelligence readiness throughout primary and secondary education. (school.contact)

    ↓

    Transition

    High school graduation

    The student’s educational journey changes, but the person does not.

    ↓

    College Contact

    Higher education

    Extends trusted identity and communication concepts into colleges, universities, community colleges, and other higher education institutions. (college.contact)

    ↓

    Future

    Alumni, research, professional, and lifelong relationships

    The individual may continue interacting with educational institutions long after graduation.

    This creates a fundamentally different model from today’s fragmented approach.

    Instead of rebuilding digital identity at every educational transition, the infrastructure could provide continuity of identity while maintaining strict boundaries around access and privacy.

    Continuity of Identity Does Not Mean Continuity of Data

    This distinction is essential.

    If a student moves from high school to college, the university should not automatically receive unrestricted access to the student’s K–12 records simply because the identity is interoperable.

    The purpose of persistent identity is not to create a universal educational dossier.

    The purpose is to allow authorized systems to recognize an individual and establish appropriate relationships.

    A future architecture should therefore distinguish between:

    Identity

    Who is this person?

    Authentication

    Can we verify that they control this identity?

    Role

    What relationship does this person have with an institution?

    Authorization

    What is this person permitted to access or do?

    Data

    What information is actually necessary for that activity?

    Audit

    Can the interaction be securely recorded and reviewed?

    This distinction could become one of the most important principles shared by School Contact and College Contact.

    Continuity of identity does not mean continuity of unrestricted data access.

    A student could move from School Contact into College Contact while privacy protections remain intact.

    Why This Matters for Students

    Imagine a student entering kindergarten.

    The student receives a verified educational identity through the School Contact framework.

    Over the years, that identity is associated with authorized educational relationships.

    The student changes schools.

    The identity remains recognizable while institutional permissions change.

    The student graduates from high school.

    The student enrolls at a university.

    College Contact establishes the appropriate higher education relationship.

    The university knows that the student is a legitimate member of its community.

    But it does not automatically receive everything associated with the student’s previous educational experience.

    The identity continues.

    The permissions change.

    That is the type of architecture that modern education technology needs.

    Why This Matters for Universities

    Universities face their own identity challenges.

    Higher education is highly decentralized.

    Different departments use different systems.

    Researchers collaborate internationally.

    Students work across multiple campuses and institutions.

    Faculty members hold appointments in multiple organizations.

    Vendors require controlled access.

    And increasingly, AI systems need access to institutional resources.

    A standardized identity framework could make those relationships easier to manage.

    Instead of every application creating its own definition of identity and authorization, applications could rely on common standards.

    A university could continue choosing the technology it prefers.

    But the underlying infrastructure could become more consistent.

    That could reduce complexity while improving security.

    The Machine Intelligence Challenge

    The arrival of AI makes this problem more urgent.

    Traditional software generally waits for a person to interact with it.

    Machine Intelligence can increasingly act.

    An AI system could eventually:

    • communicate with students;
    • schedule appointments;
    • assist professors;
    • retrieve authorized information;
    • coordinate research;
    • submit requests;
    • interact with other AI systems; and
    • perform tasks on behalf of people or institutions.

    That creates a new requirement.

    An AI system must be able to establish not only what it is, but also:

    Who authorized it?

    Whom does it represent?

    What is it allowed to do?

    What information may it access?

    Which institution recognizes it?

    Can its actions be audited?

    College Contact’s focus on Machine Intelligence readiness directly addresses this emerging challenge. (college.contact)

    The future campus may contain thousands of AI agents.

    Those agents will need identity and authorization infrastructure just as humans do.

    A Practical Example

    Imagine a university student telling an AI assistant:

    “Find my professor’s available office hours and schedule a meeting.”

    A trustworthy university AI system should be able to establish:

    • the student’s identity;
    • the student’s university affiliation;
    • the student’s relationship to the professor;
    • the professor’s verified institutional identity;
    • the appropriate communication channel;
    • the student’s authorization to schedule the meeting; and
    • the AI’s authorization to perform the task.

    The AI should not need to impersonate the student.

    It should not need unrestricted access to the university’s systems.

    And the professor should not have to wonder whether the message is legitimate.

    The system should understand:

    identity → role → authorization → action → audit

    That is the kind of infrastructure College Contact could help establish.

    The Role of a Future Department of Technology

    This is where the Department of Technology becomes important.

    A future Department of Technology could provide the institutional framework for developing technology standards that no individual university or vendor could reasonably establish alone.

    The Department would not need to operate every campus.

    It would not need to dictate which software universities purchase.

    And it would not need to replace existing IT departments.

    Instead, it could establish common standards and public-interest safeguards.

    Those standards could cover:

    • digital identity;
    • authentication;
    • authorization;
    • interoperability;
    • cybersecurity;
    • privacy;
    • accessibility;
    • AI governance;
    • machine identity;
    • identity lifecycle management;
    • vendor access;
    • auditability; and
    • secure communications.

    College Contact could then become a practical implementation and testing ground for those standards.

    From School Contact to College Contact

    A future Department of Technology could help create an education technology architecture that looks something like this:

    School Contact

    K–12 identity and communication

    ↓

    Educational transition

    High school graduation

    ↓

    College Contact

    Higher education identity and communication

    ↓

    Research and institutional collaboration

    Cross-campus and cross-institution relationships

    ↓

    Lifelong educational relationships

    Alumni, continuing education, professional development, and future learning

    Across every stage, the underlying principles remain consistent:

    Trusted identity.

    Verified roles.

    Federated authorization.

    Privacy.

    Security.

    Interoperability.

    Machine Intelligence readiness.

    The implementation can change.

    The institution can change.

    The person’s role can change.

    But the underlying standards remain interoperable.

    A Department of Technology Could Start With Pilots

    This vision should not begin with a nationwide mandate.

    It should begin with evidence.

    The College Contact initiative already identifies a roadmap and pilot program as components of its development framework. (college.contact)

    A future Department of Technology could support pilot programs involving different types of institutions:

    • public universities;
    • private universities;
    • community colleges;
    • research universities;
    • technical institutions;
    • rural institutions; and
    • institutions serving historically underserved communities.

    The pilots could evaluate:

    Security

    Does standardized identity reduce vulnerabilities?

    Interoperability

    Can different systems recognize the same verified identity?

    Administration

    Does identity management become easier?

    Privacy

    Can information be shared selectively rather than indiscriminately?

    AI readiness

    Can AI systems operate with defined identities and permissions?

    Cost

    Can common standards reduce duplication?

    User experience

    Do students, faculty, administrators, and parents experience fewer barriers?

    The results should determine how the framework evolves.

    Vendor Participation Is Critical

    No identity infrastructure will succeed if it exists only on government websites.

    Universities depend on technology vendors.

    Student information systems, learning platforms, identity providers, communication platforms, research systems, and AI companies all need to participate.

    A Department of Technology could encourage interoperability by establishing open technical standards and procurement requirements.

    Instead of saying:

    “Every university must use this one product.”

    The government could say:

    “Technology serving public education should support these secure interoperability standards.”

    That is a fundamentally different approach.

    It encourages competition while establishing common infrastructure.

    Cybersecurity Must Be Built In

    Identity infrastructure is security infrastructure.

    If College Contact is ever implemented at scale, security cannot be an afterthought.

    The framework would need to address:

    • strong authentication;
    • authorization;
    • encryption;
    • identity verification;
    • account recovery;
    • least-privilege access;
    • vendor permissions;
    • employee separation;
    • student transitions;
    • auditing;
    • incident response;
    • identity lifecycle management; and
    • protection against impersonation.

    The objective is not to create one massive centralized database.

    The objective is to establish trusted relationships between independent systems.

    That distinction is crucial.

    Privacy Must Be Equally Fundamental

    Higher education involves highly sensitive information.

    Academic records.

    Research.

    Financial information.

    Personal information.

    Health-related information.

    Intellectual property.

    A future College Contact infrastructure must therefore be designed around data minimization.

    The system should be able to answer:

    Who is this person?

    without necessarily revealing:

    Everything we know about this person.

    Likewise, an AI agent should receive only the information necessary to complete an authorized task.

    A standardized identity can actually make this easier.

    Instead of giving every application broad access, the system can establish specific permissions around specific relationships.

    College Contact as a National Pilot for Machine Intelligence

    There may be an even larger opportunity.

    College Contact could become one of the first large-scale environments for developing standards around human and machine identity in education.

    Universities are ideal environments for testing this technology because they already involve:

    • large populations;
    • complex organizational structures;
    • multiple roles;
    • research collaboration;
    • international partnerships;
    • sophisticated IT departments;
    • large technology ecosystems; and
    • rapidly increasing AI adoption.

    If trusted Machine Intelligence infrastructure can work in higher education, the lessons could eventually inform other sectors.

    That could make College Contact more than an education initiative.

    It could become a model for how society handles identity in an increasingly machine-mediated world.

    The Larger Department of Technology Vision

    School Contact and College Contact should therefore be viewed as two parts of a larger Department of Technology philosophy.

    School Contact

    Builds trusted identity and communication for K–12.

    College Contact

    Extends trusted identity and communication into higher education.

    Machine Intelligence

    Creates new requirements for verified human and machine identity.

    Department of Technology

    Provides the standards, governance, research, security framework, and coordination needed to connect these systems responsibly.

    This is not about creating more bureaucracy for technology.

    It is about creating the infrastructure necessary to make technology more trustworthy.

    The Future Educational Journey

    Imagine an American educational system where a student’s digital journey is not a collection of disconnected accounts.

    Instead:

    K–12

    School Contact establishes a trusted educational identity.

    ↓

    Graduation

    The student’s identity transitions according to defined privacy and authorization rules.

    ↓

    College

    College Contact establishes the appropriate higher education relationship.

    ↓

    Research

    The student or faculty member can participate in authorized research environments.

    ↓

    Graduation

    The institutional relationship changes while appropriate identity continuity remains.

    ↓

    Alumni

    The university can maintain an appropriate long-term relationship without retaining unnecessary access.

    ↓

    Lifelong Learning

    The individual can return to education throughout their life.

    The infrastructure changes as the person’s role changes.

    But the principles remain consistent.

    The Goal Is Not One Giant Education Database

    This point deserves emphasis.

    The vision is not to create a centralized government database containing every student’s educational history.

    It is not to eliminate university autonomy.

    It is not to give AI unrestricted access to educational information.

    And it is not to replace the technology universities already use.

    The goal is interoperability.

    A system in which independent institutions can securely recognize identities and establish authorized relationships without unnecessarily sharing information.

    Trusted identity should connect systems—not eliminate their boundaries.

    From Vision to Reality

    Making College Contact real would require cooperation among many groups.

    Universities.

    Students.

    Faculty.

    Technology companies.

    Cybersecurity professionals.

    Privacy experts.

    Researchers.

    Policymakers.

    Government agencies.

    And the people who will ultimately use the technology.

    A future Department of Technology could provide the coordination mechanism.

    It could establish working groups.

    Fund pilots.

    Develop technical standards.

    Publish security requirements.

    Support interoperability research.

    Create certification programs.

    Develop procurement standards.

    And continuously update the framework as technology evolves.

    The Future of College Contact

    College Contact represents a vision for higher education in which identity, security, communication, and Machine Intelligence are treated as infrastructure rather than isolated features of individual applications.

    School Contact provides a complementary foundation for K–12 education.

    Together, the two initiatives point toward a larger possibility:

    An education technology ecosystem in which trusted identity follows the educational journey while privacy and authorization remain under careful control.

    A future Department of Technology could help make that possible.

    Not by replacing universities.

    Not by replacing technology companies.

    Not by controlling innovation.

    But by establishing the standards that allow independent systems to work together securely.

    The next generation of higher education will contain more software, more connected systems, and more intelligent machines than any generation before it.

    The question is not whether that future is coming.

    It is how prepared we will be when it arrives.

    College Contact is one proposal for preparing higher education.

    School Contact is its complementary K–12 counterpart.

    And a future Department of Technology could provide the public infrastructure, governance, and coordination necessary to connect them.

    The future of education needs more than smarter applications.

    It needs trusted infrastructure.

    Infrastructure that knows who we are.

    Infrastructure that understands our roles.

    Infrastructure that respects our privacy.

    Infrastructure that can verify authority.

    And infrastructure that allows humans and Machine Intelligence to work together safely.

    That is the future College Contact is designed to help build.

    Learn more about College Contact: www.college.contact

    Learn more about School Contact: www.school.contact