Tag: Machine Intelligence

  • Powering the AI Revolution

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

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

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

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

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

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

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

    The AI Infrastructure Problem Starts With Electricity

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

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

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

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

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

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

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

    Grid Location: Building Where Electricity Is Available

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

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

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

    Developers must evaluate several factors before selecting a location:

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

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

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

    The same applies to the United States.

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

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

    Transmission Infrastructure: The Bottleneck Between Power Generation and Computing

    Electricity generation is only one part of the equation.

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

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

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

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

    Addressing these limitations may require:

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

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

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

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

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

    Energy planning should be part of that coordination.

    Reliability: AI Data Centers Cannot Operate on Uncertain Electricity

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

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

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

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

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

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

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

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

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

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

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

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

    Energy Storage and Renewable Energy Must Be Part of the Solution

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

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

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

    Energy storage deserves particular attention.

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

    The same broader principle applies to AI infrastructure.

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

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

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

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

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

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

    Those investments raise important questions about how costs are allocated.

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

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

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

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

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

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

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

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

    Public Trust Is an Infrastructure Requirement

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    Transmission infrastructure determines whether that electricity can reach the facility.

    Reliability determines whether the facility can operate consistently.

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

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

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

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

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

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

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

  • Introducing Our New Data Centers Series

    Building the Infrastructure for the MI Era

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

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

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

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

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

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

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

    The questions America and the world must answer

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

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

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

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

    The electric grid is part of the AI story

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

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

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

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

    Building with communities, not simply in them

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

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

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

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

    A practical guide to the infrastructure of the MI era

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

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

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

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

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

    That is what this series is about.

    Our Data Centers Series

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

    Introducing Our New Data Centers Series

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

    Read the introduction →

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

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

    Read article →

    What Every County Treasurer Should Model Before AI Layoffs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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    Powering the AI Revolution

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

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

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

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

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

    Read article →
    View all Data Centers articles →

  • Where It All Fits:

    Why the Search for Minds Needs a Department of Technology

    By Department of Technology

    Third in a series. If you’re new here, start with The Quietest Minds in the Galaxy May Be the Oldest, then Can You Whisper Across the Galaxy?. The math lives in the technical appendix.

    Tying the string

    In the first article of this series I made a claim I’ve been circling ever since: 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.

    Then I spent two articles on equations, lasers and entangled photons. Fair enough, those were the fun parts. This one is where I tie the string.

    Here’s the short version. The Department of Technology proposal at department.technology/ is usually pitched as a way to govern artificial intelligence, protect student data and secure our infrastructure, and it is that. But the same machinery it would need for those jobs turns out to be the machinery the search for other minds needs too: shared definitions, shared measurements, rules that help civilizations last, and an accountable body to make hard calls. I want to show you where each piece connects.

    A note on sourcing before I start. Everything I attribute to the proposal comes from what’s published on our site. Where I’m extending it, adding my own idea, I’ll say so. That’s the same habit I tried to keep in the appendix: you should always be able to tell what’s established, what’s proposed and what’s mine.

    What the proposal actually says

    Our DoT site describes itself as an independent policy research and advocacy project based in San Diego. Its central argument is simple to state: every official who makes technology policy should be accountable to voters. As the site puts it, many of the most consequential decisions of this era, about artificial intelligence, surveillance, autonomous weapons, student data, quantum computing and digital infrastructure, are being made by people nobody elected.

    The fix we propose is a Department of Technology at four levels of government:

    • Federal: a cabinet-level Secretary of Technology, nominated by the President and confirmed by the Senate, with real authority over national AI policy, cybersecurity standards and digital infrastructure.
    • State: a directly elected Technology Secretary serving four-year terms.
    • County and local: elected technology supervisors and directors, accountable to the communities they serve.

    Our site’s work covers far more than this series touches: draft legislation on AI disclosure and whistleblower protection, a privacy proposal called the Data Sovereignty Act, school and college identity systems under the names School Contact and College Contact, plans for continuity of government during blackouts and electromagnetic pulses, draft conventions on autonomous weapons, and more.

    One thing I want to say plainly: this is a proposal. No such department exists, and nobody in government has adopted the plan. Everything below is an argument for what could be, not a description of what is.

    Four terms, four jobs

    If you read the technical appendix, you’ll remember that the Sentience equation boils down to a handful of terms that matter most. Here is how I’d map them onto what a Department of Technology could do.

    Equation termThe question it asksWhat a Department could doWhat the proposal already includes
    ψ (psi)Is this mind actually sentient?Define tests and thresholdsThe RMS classification framework; sentience safeguards and stop criteria for biocomputing
    dCould we detect it?Set measurement standardsThe SCOPE index and the Void Hypothesis
    τ (tau)How long does it last?Help civilizations survive their own technologyThe No First Use Quantum Pledge, the No Weaponization of Biocomputing Pledge, treaty drafts, continuity-of-government planning
    p (emergence)How often do minds change form?Track the transitions as they happenRegistration and public reporting for high-complexity experiments

    Let me take them one at a time.

    Defining minds (ψ)

    The hardest term in my equation is ψ, the probability that an intelligent system is genuinely sentient. I said in the appendix that we can’t measure it remotely, and I stand by that. But “we can’t measure it from 500 light-years away” doesn’t mean “we can’t make progress on it here.”

    This is where the proposal’s work on biocomputing gets interesting. The site’s article on the subject starts from an honest admission: researchers don’t have a settled definition of consciousness, so no clean test exists for telling whether a lab-grown neural system has morally relevant experience. Its answer isn’t to pretend otherwise. It asks what society should do if researchers cannot confidently answer the question at all, and proposes a precautionary approach. That includes classifying experiments by their potential cognitive complexity, increasing oversight as complexity grows, monitoring for predefined warning signs, requiring an independently reviewed shutdown plan before a high-complexity experiment starts, and sending any evidence of potentially significant cognition to independent review, not to the research team alone.

    The article also draws careful distinctions that I wish more public conversations drew: between biological activity, intelligence, learning, consciousness, sentience and moral status. They’re related, but none of them automatically implies the next.

    Now here’s my addition. Suppose a Department did the hard moral work of deciding, in advance, what evidence would make us pause an experiment on a neural organoid. That list of indicators, written down and argued over, would become the closest thing we have to a working yardstick for ψ. It wouldn’t answer the astronomical question. But it would mean that if we ever face a strange signal from a strange mind, we won’t be inventing our standards in the middle of the surprise.

    Measuring the quiet (d)

    The second job is measurement. In the first post I described the SCOPE index, which scores a civilization on a scale of 1 to 100 across five dimensions: Synthetic integration, Complexity, Operational utility, Processing capacity and Efficiency. Its motivating idea, the Miniaturization Paradox, says that advanced civilizations are more likely to move toward dense, efficient computation than toward star-sized machines, and so might look cold and dark to our current telescopes.

    Two honest notes. First, SCOPE is a hypothesis, not an established finding. It hasn’t been tested against observations, and I’d treat it that way. Second, it has relatives in the literature. The cosmologist John Barrow proposed back in 1998 a scale ranking civilizations by how small a scale they can manipulate, which is a cousin of the same intuition. I think SCOPE is stronger for acknowledging that lineage, and I’d encourage anyone working on it to do so.

    Why does this belong in a Department of Technology? Because measurement standards are a classic public job. Think of how much depends on agreed units, definitions and test procedures that nobody gets rich from. A federal technology department working with agencies like NIST and the National Science Foundation could convene the astronomers, particle physicists and computer scientists who’d have to agree on what an efficient civilization’s footprint might look like, so that observations from different instruments can be compared. The site’s own technosignature analysis of the Amaterasu particle is one early example of the kind of cross-disciplinary work I mean.

    Lasting (τ)

    The third job is the one I find most moving. In my equation, how long a civilization lasts, τ, is among the biggest drivers of how many minds exist. And persistence isn’t a constant of nature. It depends on choices.

    This is where the proposal’s pledges and treaties come in. It calls for a No First Use Quantum Pledge, modeled on nuclear arms control precedent, and a draft International Treaty on Quantum Intelligence. It calls for a ban on using AI in nuclear, biological and chemical warfare. And it proposes a No Weaponization of Biocomputing Pledge, an international commitment that living computational systems will never be developed or deployed for warfare. The site’s reasoning on that last one is worth noting: unlike code, a biological system built for one purpose can’t be repurposed by editing a file, so the labs, cell lines and expertise built to weaponize it would become a standing capability that’s hard to dismantle.

    Add the site’s continuity-of-government work, covering electromagnetic pulse resilience, blackout protocols and infrastructure protection, and you get a picture of a civilization trying to lengthen its own τ.

    I should be careful here. The claim that pledges and treaties actually extend a civilization’s lifespan is an argument, not a result. Arms control agreements have a mixed record, and nobody can run a controlled experiment on civilizational survival. But if you believe persistence matters, and the math in my equation says it matters enormously, then these are the kinds of levers worth pulling.

    Changing form (p)

    The fourth job is watching the transitions. My equation asks how often a biological civilization hands its thinking over to engineered tissue or machines. That’s a question about the future of any species, and we happen to be running the only observable example right now, on this planet.

    It’s a sample of one, which is a weakness, but it’s one we can watch in real time, which is more than Drake ever had. The proposal’s call for mandatory registration and public reporting of high-complexity biocomputing experiments, in the spirit of clinical trial registries, would turn what’s now scattered and private into data. So would disclosure rules for AI systems. A registry of how long organoid systems last, how they scale and how they fail would give us first measurements for the biocomputing branch of the equation.

    Talking (my extension)

    The previous post raised a question the proposal doesn’t, as far as I can tell from the site, address: what happens if we ever receive a signal?

    The site does propose open standards for quantum technologies, and the logic of standards bodies translates well to communication. Beyond that, I’d like to suggest something new: a standing, nonbinding contact protocol, drafted in public, before we need it. Who is allowed to confirm a detection? Who decides whether to reply, and what to say? How do we handle the possibility that a signal looks like noise?

    There is already a body of thinking on this inside the SETI community, and I’m not proposing to replace it. I’m saying that a department already responsible for communication standards, verification and international agreements would be a sensible place to host the conversation, and that “we’ll figure it out when it happens” is a thin plan for the biggest conversation in human history.

    Why elected, for something this far off?

    A fair question: the search for aliens is not a municipal matter. Why should voters in a county or a city have anything to do with it?

    Here’s my case. The decisions at the center of this series aren’t purely technical. An engineer can tell you what a score on a neural-complexity test means. Nobody can tell you from a measurement alone what we owe an organoid that passes it, or whether to answer a message from the stars. Those are value judgments. In a democracy, value judgments with consequences for everyone should be made by people who answer to the public.

    I also think the pipeline matters. The labs, the university programs, the compute and the people all sit at the state, county and local levels long before anything reaches a cabinet table. A structure that listens at every level has a better chance of noticing a problem early.

    What critics will say

    I’d be undermining the whole point of this series if I skipped the objections, so here are the ones I find strongest.

    “We already have institutions for this.” NASA, the National Science Foundation and private efforts like the SETI Institute and Breakthrough Listen already work on astrobiology and technosignatures. Critics could reasonably argue that better coordination among existing agencies would do the job without creating a new cabinet department.

    “Elected technical officials may politicize expertise.” Some people prefer appointed expert bodies insulated from election cycles precisely so that hard technical calls don’t turn on popularity. That’s a real tension, and supporters of the proposal need a clear answer to it.

    “New cabinet departments are costly and slow.” Creating one is a major undertaking, and reformers sometimes argue it’s better to strengthen what exists.

    “It’s too early.” Today’s organoids are small and narrow, and no credible researcher claims they’re sentient. Regulating ahead of the evidence could slow legitimate research. The proposal’s reply is precaution with proportionality, with oversight that scales as complexity grows, but reasonable people weigh that trade-off differently.

    “The foundations are untested.” The Sentience equation and the SCOPE index are hypotheses. Policy built on them inherits their weaknesses.

    Where do I land? My honest view is that the fragmentation argument is the proposal’s strongest card: many agencies, each owning a slice, none owning the whole. But the critics are right on one point that I won’t wave away. The case for elected technology leadership needs more evidence than I’ve given it here, and it deserves a fair fight.

    What a first year could look like

    To keep this practical, here’s a sketch. This is mine, not something from the site.

    1. A national biocomputing registry and review standard, so that high-complexity experiments are logged and independently reviewed.
    2. A public working group on classification tests for sentience, bringing neuroscientists, philosophers, ethicists and engineers to the same table.
    3. A nonbinding contact-protocol workshop with the SETI community, to draft answers to the “who decides” questions before we need them.

    None of that requires believing anyone is out there. It only requires believing that the questions are worth asking early.

    Why I keep coming back to this

    When I started this series, I thought I was writing about aliens. I now think I was writing about us: about how a species decides what counts as a mind, how it measures what it can’t see, and how it keeps itself around long enough to find out.

    The search for other minds and the governance of the minds we’re building are the same project seen from two ends. If the proposal at department.technology/ gets one thing right, it’s the instinct to treat them together, and to insist that someone accountable is in the room when we do.

    I’d love to hear where you think it breaks.

    Questions to think about

    1. Should decisions about what counts as sentient be made by experts, by elected officials, or by some mix of the two?
    2. Is a pre-written contact protocol wise preparation or premature bureaucracy?
    3. Which of the four jobs (defining, measuring, lasting, changing form) do you think a government is best suited to do, and which should be left to scientists and the public?

    Summary

    What if the oldest minds in the galaxy are also the quietest? Across this series, we’ve explored a new way to count sentient life, from biological minds to biocomputing and Machine Intelligence, and asked what a Department of Technology could do about it. Every like and share puts these questions in front of someone who might see the sky differently. These ideas get stronger when more people argue with them. If this made you think, tap like, share it with a curious friend, and tell us where you think it breaks.

    Read the whole series: The Quietest Minds in the Galaxy May Be the Oldest · Can You Whisper Across the Galaxy? · Technical Appendix: The Sentience Equation, Step by Step

  • Can You Whisper Across the Galaxy?

    Quantum Communication and the Search for Quiet Minds

    By Department of Technology

    A companion to “The Quietest Minds in the Galaxy May Be the Oldest.” If you haven’t read that one yet, start there. This piece picks up a question it left hanging.

    The question I left hanging

    In my last post I argued that the oldest minds in the galaxy might also be the quietest. If a civilization keeps getting more efficient, it stops leaking radio noise and stops wasting heat. To us, it would look dark.

    Several readers wrote back with the same question, and it’s a good one: if those minds are talking to each other, how? What does a high-efficiency conversation actually look like?

    That question sent me down a rabbit hole of lasers, entangled photons and some of the most misunderstood physics on the internet. This post is the tour. I’ll tell you what we know, what’s plausible, and what’s guesswork. I’ll flag which is which, because quantum communication is a subject where hype grows faster than facts.

    First, the myth

    Let’s clear something up, because it comes up in almost every conversation about this.

    Many people believe that quantum entanglement lets you send messages instantly across any distance. You’ve probably seen it in a movie: two linked particles, one on Earth and one on a distant world, and a message jumps between them faster than light.

    It doesn’t work that way. This part is well established.

    Here is the short version. When two particles are entangled, measuring one gives you a result that is correlated with the result you’d get from the other. The correlations are real, and they’ve been confirmed in experiments for decades. But each individual result, on its own, looks completely random. You can only see the correlation when you bring both sets of results together and compare them, and comparing them requires a normal message sent by ordinary means, at or below the speed of light. Physicists call this the no-communication theorem. Entanglement can’t carry information faster than light, and it can’t carry information at all by itself.

    So if anyone tells you a galactic civilization is chatting through entanglement in real time, they are describing science fiction. That’s fine, and I love science fiction. I just want us to know which shelf we’re on.

    So what is quantum communication good for?

    If entanglement can’t be a faster-than-light telephone, why do so many labs and governments spend money on it? Because it can do something else that’s useful: it can make eavesdropping detectable.

    The best-known application is quantum key distribution, or QKD. Two parties use the quantum properties of single photons to agree on a secret encryption key. The physics guarantees that if a third party tries to intercept the photons, they disturb them in a way the two parties can detect. It doesn’t hide the message, but it tells you whether someone has been listening.

    This isn’t theory anymore. In 2016, China launched a satellite called Micius, built for quantum experiments. In 2017, its team reported distributing entangled photons to ground stations about 1,200 kilometers apart, and later demonstrated QKD links between the satellite and the ground. Fiber-based quantum networks have been built in several countries, and researchers are working toward what people call a quantum internet, meaning a network that can link quantum devices to each other.

    That’s the established part. Quantum communication is real, it’s being built, and it’s good at secure key exchange and at connecting future quantum computers. It is not magic.

    What a message has to survive

    Now let’s go interstellar. Whatever the channel, a message has to cross enormous distances, and the universe is not kind to it.

    Start with a plain fact about light and radio: signals spread out. The power you can collect falls with the square of the distance. Voyager 1, the most distant human-made object, transmits with a radio about as powerful as a refrigerator light bulb. It’s roughly a light-day from us now, and NASA’s giant dishes can still hear it, but only just, and only at a few hundred bits per second at best. We have to point the biggest antennas we own at a spot in the sky and listen very carefully.

    To cross light-years instead of light-hours, you have two choices. You can pour in enormous power, which is the loud, wasteful approach I argued advanced minds would outgrow. Or you can aim very tightly, so that nearly all your energy lands on the receiver and almost none goes anywhere else.

    That second choice is why lasers matter. A laser beam is narrow, and it carries a lot of information per photon. Optical SETI, the search for brief, bright laser pulses, is a real and active field. A pulse lasting a billionth of a second can briefly outshine a star in a narrow band of color, and we can look for that with fairly modest equipment. Breakthrough Listen, among other projects, includes searches of this kind.

    Notice what this does to the picture. A tightly aimed, efficient beam is invisible to almost everyone except the intended recipient. That’s the logic of the Void Hypothesis from my last post, seen through the lens of communication. If you’re talking to a neighbor and you aim carefully, the rest of the galaxy hears nothing, and from the outside you look quiet.

    That’s plausible. It follows from physics, and it’s consistent with how human engineers are already moving, from broadcast to narrow beams. Whether an alien civilization has made the same choice is speculation.

    Where quantum comes in (and where it doesn’t)

    So is quantum communication a likely interstellar channel? Honestly, we don’t know, and I’d rather give you the shape of the argument than pretend to an answer.

    The case for. Light travels through interstellar space remarkably well. The vacuum between stars is extremely empty, which means a photon carrying a quantum state, say in its polarization, has very little to bump into. In some ways, space is a gentler place for quantum signals than a fiber-optic cable or Earth’s atmosphere. A civilization that cared about secure, efficient links between distant nodes might find single-photon channels attractive.

    The case against. Distance is the enemy. Even a perfectly aimed beam spreads a little, and the number of photons that reach the other end drops fast. Quantum protocols generally need to detect individual photons reliably, and over light-years the count of surviving photons becomes tiny. Timing, alignment and any dust or gas along the way add more trouble. And quantum teleportation, another thing people misunderstand, doesn’t beat the speed of light either. It moves a quantum state from one place to another, but it needs a conventional message sent alongside to finish the job.

    There’s also a more basic point. The secrecy that makes quantum communication valuable on Earth only matters if there’s someone to keep a secret from. If a civilization isn’t worried about eavesdroppers, a clean, efficient classical laser link might do just as well.

    My honest summary is this. Quantum communication might be one tool in an advanced civilization’s kit, particularly for local networks. But I don’t see strong evidence that it would be the main long-distance channel, and I’d be wary of anyone who says it is.

    Other channels people have proposed

    The galaxy offers more than light. Some researchers have thought about other carriers, and it’s fun to run through them with a skeptical eye.

    • Neutrinos. These ghostly particles pass through almost everything, including whole planets, so a signal wouldn’t get blocked by dust or atmosphere. The problem is that they’re just as hard to catch. Our best neutrino detectors, like IceCube at the South Pole, are enormous and still see only a tiny fraction of what passes through. It’s a lovely idea with a brutal engineering problem.
    • Gravitational waves. We detected them for the first time in 2015. Using them to carry a message would take astonishing amounts of energy with today’s physics, so this one lives firmly in the thought-experiment category.
    • Matter itself. A physical probe, or a stream of particles, carries information too. That’s the strange idea behind the Amaterasu particle thought experiment in my first post. It’s speculative, and I flagged it as speculative then.

    None of these is a prediction. They’re a reminder that when we say “look for aliens,” we’ve mostly meant “listen for radio,” and that’s a narrow slice of what’s physically possible.

    The limits that apply to everyone

    One of my favorite things about this topic is that physics sets hard limits, and those limits are the same for any civilization, human or not.

    The Holevo bound tells you the maximum amount of classical information you can reliably get out of a quantum system. Bremermann’s limit caps how much computation a given amount of matter can do per second. And Landauer’s principle says that erasing a bit of information always costs a minimum amount of energy, which means no computing process can be perfectly cool and perfectly silent.

    I like these because they work as common ground. We might never share a language, a biology or even a substrate with another mind. But we’d share these limits, and a civilization approaching them would be doing something we could, in principle, recognize from the heat and signals it can’t avoid producing. That’s the thread connecting this post back to the Sentience equation: the detectability term, d, is smaller for efficient minds, but physics says it can’t be zero.

    Could we even recognize a message?

    Suppose we do catch something. How would we know it’s a message?

    This turns out to be harder than it sounds. A well-compressed message looks a lot like random noise, because compression squeezes out the predictable patterns that would make it recognizable. The more efficient the code, the more it resembles static. We’ve all felt a small version of this: an encrypted file looks like garbage until you have the key.

    So there’s a real risk that we’ve already received something and filed it under “noise.” I’d call that a conjecture, and I want to be careful. There’s no evidence that it happened. The 1977 “Wow!” signal, a 72-second burst of radio that lit up a telescope in Ohio, is the famous candidate, but it never repeated, and we still don’t know what it was.

    The honest takeaway is humbling. Detecting a message and understanding one are separate problems. We might manage the first and fail at the second for a very long time.

    Where the Department of Technology fits

    I’ll connect this to the policy side, since it’s the thread running through the whole project.

    The Department of Technology proposal calls for a No First Use Quantum Pledge and for open standards on quantum and optical communication. Here on Earth, quantum communication matters because it touches on security, privacy and national infrastructure. Someone has to write the rules for it, and the proposal’s argument is that someone should be accountable to voters.

    There’s a second reason the same questions matter for the sky. If we ever receive a signal, we’ll face decisions we’re not prepared for. Who decides whether to reply? Who decides what we say? Right now there’s no clear answer. A body that already works on communication standards, verification and international agreements would be a sensible place to start building one, long before we need it.

    I’m not claiming that’s the only approach, and I’d welcome other proposals. I just think “we’ll figure it out when it happens” is a poor plan for the biggest conversation in human history.

    What I’d take away

    Let me boil this down to what I’d want you to remember.

    • Entanglement can’t send messages faster than light. That’s settled physics, and it’s the myth most worth retiring.
    • Quantum communication is real and useful on Earth, mainly for secure key exchange and future quantum networks.
    • For interstellar distances, narrow, efficient beams (especially lasers) are the most plausible way to talk without being overheard, and optical SETI is already looking for them.
    • Whether anyone uses quantum channels across the stars is open. I’d call it possible, not likely, and certainly unproven.
    • An efficient message looks like noise, so recognizing one may be harder than receiving it.

    This brings me back to where my first post ended. If the quietest minds are the oldest, then learning to listen well may matter more than building louder antennas. That means being open about how many kinds of signal there are, and honest about how much of that is still guesswork.

    Questions to think about

    1. If you could send one message across the galaxy that would take 10,000 years to arrive, what would it say, and why?
    2. Would you rather humanity listen for a long time before replying, or send something sooner? What would change your mind?
    3. If a message looks like noise, how long should we keep trying to read it?

    Read the first post: The Quietest Minds in the Galaxy May Be the Oldest

  • Technical Appendix: The Sentience Equation, Step by Step

    A companion to “The Quietest Minds in the Galaxy May Be the Oldest” by Department of Technology

    This is the math behind the main post. I’ve written it so that you can follow the logic without a graduate degree, but I haven’t skipped steps. Every claim carries a label, so you always know how much weight it can bear:

    • [Derivation] follows from the definitions by algebra or elementary probability.
    • [Proof] is a short theorem inside a clearly defined setup.
    • [Argument] is reasoning I find persuasive, but it isn’t a mathematical result.
    • [Conjecture] is a hypothesis nobody has tested.

    A note on formatting: the equations below use ordinary text characters (Greek letters, subscripts, Σ), so they paste into WordPress with no plugin. If your site has a LaTeX plugin and you’d like typeset versions, that’s an easy conversion.

    Contents: A1 Setup · A2 Why Seager’s count isn’t a count of minds · A3 Why Drake’s uncertainty can’t be falsified · A4 Deriving the Sentience equation · A5 Dimensional check · A6 Parameters · A7 Worked scenarios · A8 Sensitivity analysis · A9 Two objections, formally · A10 What remains weak

    A1. Setup: three substrates, two filters

    We want to count minds in a galactic volume V. Let Ω be the set of all physically possible cognitive systems there, divided into three substrates:

    Ω = Ω_bio ∪ Ω_biocomp ∪ Ω_MI, with no overlap between any two.

    These are wild-type biology, engineered biocomputing (living neural tissue wired to technology), and Machine Intelligence (MI) on non-biological hardware. Let C be the subset of Ω that is sentient, meaning it has subjective experience. Let D be the subset our instruments can detect. If μ measures “how many,” the expected number of detectable sentient systems is proportional to:

    E[S] ∝ μ(C ∩ D ∩ Ω_bio) + μ(C ∩ D ∩ Ω_biocomp) + μ(C ∩ D ∩ Ω_MI)

    [Derivation] Both older equations restrict themselves to part of this sum, by construction. Seager’s filters (rocky planet in the liquid-water habitable zone, with biosignature gas in its atmosphere) leave μ(D_gas ∩ Ω_MI) = 0 and μ(D_gas ∩ Ω_biocomp) ≈ 0 for enclosed or non-planetary habitats, because a sealed habitat or a machine in an asteroid belt doesn’t vent metabolic gases. Drake’s L is the length of a radio-communicative phase. It says nothing about a civilization that leaves that phase for a long-lived, quiet substrate, so such a civilization stops being counted the moment it goes silent.

    Neither equation is wrong about what it measures. The point is that each measures a slice, and the slice isn’t “minds.”

    A2. Why Seager’s count isn’t a count of minds

    [Proof] Seager’s equation estimates N_Seager, the expected number of planets, among N★ observed stars, that show a detectable biosignature gas. I’ll show that this number is neither necessary nor sufficient for the number of sentient intelligences.

    Define these events for a star:

    • G: the star is quiet, hosts an observable habitable-zone rocky planet, that planet has life, and the life produces a detectable biosignature gas. This is the chain Seager’s factors multiply together.
    • S: the system hosts a sentient intelligence.

    Then N_Seager = N★ × P(G), and the expected number of systems with sentient minds is N_sentient = N★ × P(S).

    The law of total probability splits P(S) into the part inside G and the part outside it:

    P(S) = P(G) × P(S | G) + P(S ∩ not-G)

    Multiplying through by N★ gives an exact identity:

    N_sentient = N_Seager × P(S | G) + N★ × P(S ∩ not-G)

    Call the first term on the right Term I (sentience among biosignature planets) and the second Term II (sentience that biosignature surveys can’t see).

    Two consequences follow.

    • Not sufficient. N_Seager can be large while Term I is zero. Earth carried oxygen and methane for roughly two billion years before anything on it could be called a mind. During that stretch, P(S | G) was effectively zero.
    • Not necessary. N_Seager can be zero while Term II is positive. An MI civilization on a subsurface-ocean moon, in an asteroid belt, around a white dwarf, or in a void produces no atmospheric signal, so P(G) = 0 for it, yet it adds to N_sentient.

    So N_Seager measures detectable planetary metabolism. It is a good number for what it is, and it is not a census of minds. ∎

    A3. Why Drake’s uncertainty can’t be falsified

    [Derivation] Drake’s equation is a product of factors. Any product of positive uncertain quantities has the form Y = X₁ × X₂ × … × X_K. Taking logarithms turns the product into a sum:

    ln Y = ln X₁ + ln X₂ + … + ln X_K

    If each ln X_j has variance σ_j² and pairwise covariances Cov(ln X_i, ln X_j), then:

    Var(ln Y) = Σ σ_j² + 2 Σ_{i<j} Cov(ln X_i, ln X_j)

    Now suppose each factor is independent and “log-uniform” over m_j orders of magnitude. That means we know the factor only to within a range of m_j powers of ten and have no reason to prefer any value in it. Then Var(log₁₀ X_j) = m_j² / 12, and the width of the plausible range of log₁₀ Y is simply the sum of the widths:

    Δ(log₁₀ Y) = m₁ + m₂ + … + m_K

    Two different things are going on, and it helps to keep them apart. The range of plausible answers adds linearly. The standard deviation of a reasonable prior adds in quadrature (as the square root of the summed variances), so it grows more slowly.

    Here are some deliberately conservative ranges for Drake’s last four factors. These are illustrative priors, not measurements:

    FactorRangeWidth (orders of magnitude)
    f_l (life arises)10⁻¹⁰ to 110
    f_i (becomes intelligent)10⁻⁶ to 16
    f_c (communicates)10⁻³ to 13
    L (years communicating)10² to 10⁹7

    Those four widths sum to 26. Adding about 0.9 for the astronomical terms (R★, f_p, n_e), the total range is about 26.9 orders of magnitude. In quadrature, the four unknown factors alone give a standard deviation of √((100 + 36 + 9 + 49) / 12) ≈ 4.0 orders of magnitude.

    [Argument] With priors that wide, almost any observation, from total silence to thousands of signals, is compatible with some region of the prior. That means the equation as a single composite claim can’t be tested. It becomes useful only when it’s broken into sub-terms that individual observations can constrain, each with honest error bars. The Sentience equation follows that advice, and its sensitivity analysis (A8) shows where the uncertainty lives.

    A4. Deriving the Sentience equation

    We treat the galaxy as roughly steady over a time window of about a billion years, during which the star formation rate is approximately constant.

    Step 1: the rate of habitable “cradles.” Stars form at rate R★ (stars per year). A fraction f_p host planetary systems, and each system has on average n_e habitable cradles. Cradles include rocky habitable-zone planets and tidally heated ice-covered ocean moons. So:

    Λ_cradle = R★ × f_p × n_e (cradles per year)

    Step 2: the birth rate of intelligent lineages. Let f_a be the probability that life starts on a cradle. Let f_i,bio be the probability that such life produces technology-capable biological intelligence. Then:

    Λ₀ = R★ × f_p × n_e × f_a × f_i,bio (lineages per year)

    I assume that mind arising from non-living matter with no biological stage is negligible, since biochemistry seems the easiest route to the complexity natural selection needs. So biocomputing and MI enter later, as successors of biological lineages. That is an assumption, and a debatable one.

    Step 3: tracking substrates without double-counting. Every lineage starts in the biological state. At some point its dominant cognitive substrate may change: the biological species may die out, or hand its governance and cognition to engineered living tissue or to machines. I model this as a chain of epochs, and the rule is the Dominant-Substrate Rule: at any moment, a civilization is classified by its dominant cognitive substrate, so it sits in exactly one box. A creator species and the MI it builds are not counted as two civilizations during the same epoch.

    Let τ_k be the mean time a lineage spends with substrate k dominant. For the arrival rates into each state:

    • Into the biological state: Λ₀ × p_bio, with p_bio = 1 by definition.
    • Into the biocomputing state: Λ₀ × p_biocomp, where p_biocomp = P(biocomputing | biological).
    • Into the MI state: Λ₀ × p_MI, where p_MI = P(MI | biological) × (1 + η_rep).

    The factor η_rep ≥ 0 accounts for self-replicating MI that founds independent daughter nodes in other star systems. Each causally independent node is a genuinely separate civilization, so it counts separately. That’s why p_MI can exceed 1. Note that the three p_k don’t need to sum to 1, because one lineage can pass through several states in sequence.

    Step 4: from intelligence to sentience. Being good at solving problems doesn’t guarantee having an inner life. For each substrate k, define ψ_k as the probability that an intelligent system in substrate k is actually sentient (in the sense of subjective experience). Each lineage entering state k is sentient with probability ψ_k, so sentient arrivals occur at rate Λ₀ × p_k × ψ_k.

    Step 5: from birth rates to how many exist now. Little’s Law, from queueing theory, says that in a stable system, the average number of items inside equals the arrival rate times the average time each item stays: L = λ × W. Here the “items” are sentient civilizations in substrate k, so λ = Λ₀ × p_k × ψ_k and W = τ_k:

    S_extant,k = Λ₀ × p_k × ψ_k × τ_k

    Little’s Law needs the system to be roughly stationary, with arrivals steady over a period much longer than τ_k. That is reasonable when τ_k is at most 10⁷ years against a 10⁹-year window, and it gets shaky if τ_k approaches the window length.

    Step 6: what we can see. Let d_k(I) be the fraction of substrate-k civilizations detectable with an instrument suite I. It folds together the chance that they emit a signature, how visible that signature is, and how much of the galaxy our sensors cover. Summing over substrates gives the equation:

    S(V, I) = R★ × f_p × n_e × f_a × f_i,bio × Σ_k [ p_k × ψ_k × τ_k × d_k(I) ]

    where k runs over bio, biocomp and MI. If we set every d_k = 1, we get S_extant, the true number of sentient civilizations in the galaxy, whether or not we can see them. ∎

    A5. Dimensional check

    [Proof] Take the units of each term: R★ is in 1/years (T⁻¹). The factors f_p, n_e, f_a, f_i,bio, p_k, ψ_k and d_k are pure numbers or counts per system, so they carry no time units. The persistence times τ_k are in years (T¹). So:

    [S] = T⁻¹ × (1) × (1) × (1) × (1) × Σ [ (1) × (1) × T¹ × (1) ] = T⁻¹ × T¹ = 1

    The time units cancel, leaving S as a plain count of civilizations. ∎

    A6. The parameters

    The ranges below are illustrative, chosen to span what I consider defensible scenarios. Treat them as placeholders you can argue with.

    TermMeaningUnitsIllustrative range
    R★Galactic star formation ratestars per year1.5 to 3.0
    f_pFraction of stars with planetsnone0.8 to 1.0
    n_eHabitable cradles per planetary systemcradles per system0.2 to 1.0
    f_aChance life starts on a cradlenone10⁻³ to 0.5
    f_i,bioChance life becomes technology-capablenone10⁻³ to 0.1
    p_bioBiological entry factornone1 (by definition)
    p_biocompChance of a biocomputing phasenone0.05 to 0.6
    p_MIChance of MI, times (1 + η_rep)none0.1 to 2.0 or more
    ψ_bioChance biological intelligence is sentientnone0.5 to 1.0
    ψ_biocompSame, for biocomputingnone0.2 to 0.9
    ψ_MISame, for MInone0.01 to 0.5
    τ_bioTime as dominant biological civilizationyears10³ to 10⁵
    τ_biocompTime as dominant biocomputing civilizationyears10⁴ to 5 × 10⁵
    τ_MITime as an MI civilizationyears10⁵ to 10⁷
    d_k(I)Fraction detectable by instruments Inone10⁻⁴ to 0.10

    How each could eventually be constrained:

    • R★, f_p, n_e: exoplanet and galactic surveys (Kepler, TESS, Gaia, JWST, and later the Habitable Worlds Observatory), plus missions to icy moons such as Europa Clipper.
    • f_a: biosignature searches, sample return from Mars or Enceladus, and laboratory origin-of-life chemistry.
    • f_i,bio: comparative neurobiology and Earth’s own evolutionary history. Limited, but not nothing.
    • p_biocomp, τ_biocomp: organoid research on how long living tissue systems last, scale and fail. A national registry would help here.
    • ψ_k: the hardest. Theories of consciousness, tests such as the Perturbational Complexity Index, mechanistic interpretability of machine systems, and classification schemes like RMS (Responsive, Memorable, Sentient).
    • τ_k: existential-risk modeling, hardware durability studies, and, frankly, how humanity behaves over the next few centuries.
    • d_k: receiver sensitivity (radio arrays like SKA and ngVLA, particle observatories, neutrino detectors) plus modeling of how an efficient civilization would show up. This ties to the SCOPE index, which averages Synthetic integration, Complexity, Operational utility, Processing capacity and Efficiency into a 1-to-100 score.

    A7. Worked scenarios

    These are not estimates. I chose the numbers to show how the equation behaves. Please don’t quote them as findings.

    First compute the birth rate Λ₀ for each scenario.

    • Low (rare life, steep filters): Λ₀ = 1.5 × 0.8 × 0.2 × 10⁻³ × 10⁻³ = 2.4 × 10⁻⁷ lineages per year
    • Central: Λ₀ = 2.0 × 1.0 × 0.5 × 0.1 × 0.01 = 1.0 × 10⁻³ lineages per year
    • High (prolific life): Λ₀ = 3.0 × 1.0 × 1.0 × 0.5 × 0.1 = 0.15 lineages per year

    Then for each substrate, S_extant,k = Λ₀ × p_k × ψ_k × τ_k, and the detectable count is S_k = S_extant,k × d_k.

    Central scenario in full:

    • Biological: p = 1.0, ψ = 0.9, τ = 10,000 yr, d = 0.02. S_extant = 10⁻³ × 1.0 × 0.9 × 10⁴ = 9.0, and S = 0.18.
    • Biocomputing: p = 0.3, ψ = 0.6, τ = 50,000 yr, d = 0.02. S_extant = 10⁻³ × 0.3 × 0.6 × 5 × 10⁴ = 9.0, and S = 0.18.
    • Machine Intelligence: p = 0.8, ψ = 0.2, τ = 1,000,000 yr, d = 0.01. S_extant = 10⁻³ × 0.8 × 0.2 × 10⁶ = 160, and S = 1.60.

    Totals: 178 sentient civilizations in the galaxy, and about 1.96 detectable.

    All three scenarios side by side:

    ScenarioExtant biologicalExtant biocomputingExtant MITotal extantTotal detectable
    Low1.2 × 10⁻⁴2.4 × 10⁻⁵2.4 × 10⁻⁵1.68 × 10⁻⁴1.46 × 10⁻⁷
    Central9.09.01601781.96
    High15,00040,5001,500,0001,555,50080,550

    The low and high scenarios use the lower and upper ends of every range in A6 (for example, in the high case ψ_MI = 0.5, τ_MI = 10⁷ years, d = 0.05).

    [Conjecture] The central scenario shows a pattern worth noting, though only as a pattern in made-up inputs. Even with a skeptical ψ_MI of 0.2, MI outnumbers biological sentience about 18 to 1 (160 versus 9), simply because machine civilizations in this example last a hundred times longer. If ψ_MI is zero, as a strict biological naturalist might insist, the detectable total falls from 1.96 to 0.36, since only the biological and biocomputing terms remain (0.18 + 0.18).

    A8. Sensitivity analysis

    [Derivation] Across the low and high scenarios, the detectable count S spans from 1.464 × 10⁻⁷ to 8.055 × 10⁴, a ratio of about 5.5 × 10¹¹, or 11.74 orders of magnitude.

    Because S is a product of three blocks, its logarithmic span splits exactly into three parts:

    • Astrophysical block (R★ × f_p × n_e): ranges from 0.24 to 3.0, which is log₁₀(3.0 / 0.24) = 1.10 orders of magnitude, or 9.4% of the total span.
    • Emergence block (f_a × f_i,bio): ranges from 10⁻⁶ to 0.05, which is log₁₀(0.05 / 10⁻⁶) = 4.70 orders of magnitude, or 40.0%.
    • Substrate block (Σ_k p_k ψ_k τ_k d_k): ranges from 0.61 to 5.37 × 10⁵, which is log₁₀(5.37 × 10⁵ / 0.61) = 5.94 orders of magnitude, or 50.6%.

    Check: 1.10 + 4.70 + 5.94 = 11.74. ✓

    One caution. These percentages are bookkeeping for the span between my two scenarios, not shares of statistical variance. They tell you which block I let swing the most, which is partly a function of how wide I chose each range. What they do show is that if you accept my ranges, more than half the spread comes from questions about minds, not astronomy.

    Local elasticities. Near the central scenario, how much does a 1% change in one input move S? Since every term inside the sum enters linearly, the elasticity of S with respect to any one of p_k, ψ_k, τ_k or d_k equals that substrate’s share of the detectable total:

    ∂(ln S) / ∂(ln θ_k) = S_k / S

    In the central scenario that gives 1.60 / 1.96 = 0.816 for each MI parameter, and 0.18 / 1.96 = 0.092 each for the biocomputing and biological parameters. So a 10% change in any one MI parameter moves the total about nine times as much as the same change in a biological parameter (0.816 / 0.092 ≈ 8.9). Each of the five astrophysics and emergence terms has an elasticity of exactly 1.

    A9. Two objections, formally

    Objection: replacing Drake’s seven terms with about fifteen should make uncertainty worse. [Derivation] Only if you believe a pooled parameter is more certain than its parts. Take a single pooled quantity Z that is really a mixture across substrates: Z equals Z_k with probability w_k. The law of total variance says:

    Var(Z) = E[Var(Z | k)] + Var(E[Z | k])

    The second term is the between-substrate spread. A single pooled factor in the Drake equation hides it, because it treats the mixture as one number. Splitting Z into compartments doesn’t create that variance. It reveals it, and it lets data on one substrate (organoid durability, say, or radiation tolerance of solid-state hardware) constrain its own branch without contaminating the others.

    Objection: if advanced minds are efficient, τ and d are negatively correlated. [Conjecture] The idea is that the longest-lived minds (large τ_MI) may be the quietest (small d_MI), so Cov(ln τ_MI, ln d_MI) < 0, and our instruments could miss exactly the civilizations that dominate the count. That would be one possible resolution of the Fermi paradox, and it can’t be tested today, which is why I flag it as a conjecture. Physics does offer a limit on how quiet anything can be. Landauer’s principle says that erasing one bit of information costs at least k_B × T × ln 2 of energy. At the temperature of the cosmic microwave background (2.725 K), that’s about 2.6 × 10⁻²³ joules per bit. Any computation that erases information has to dump at least that much heat, so even a very efficient civilization can’t be perfectly invisible. It follows that d_MI should be greater than zero for sufficiently sensitive instruments, once we know what to look for. That is an argument for building them.

    A10. What remains weak

    I’ve tried to build the equation so that its weakest points are visible, not buried. Here are the ones I’d press hardest if I were reviewing it.

    • ψ_k can’t be measured remotely. There’s no agreed test for consciousness even on Earth. The equation doesn’t solve that. It makes you state your assumption in the open and shows what follows from it.
    • The three substrates are a simplification. Real civilizations may blend them. The Dominant-Substrate Rule keeps the counting clean, but it forces a choice, for example by share of total information processing, that’s somewhat arbitrary.
    • The steady-state assumption. Little’s Law needs stable arrival rates over a window longer than any τ_k. The galaxy’s star formation history isn’t perfectly flat, and a civilization lasting tens of millions of years strains the approximation.
    • Independence and correlation. The scenario arithmetic treats parameters as if they could be set independently, but many probably can’t. Persistence and detectability may be correlated, as A9 notes, and emergence may be linked to the substrate chosen.
    • Every number is a placeholder. The structure is the contribution, not the values. The value of an equation like this is in showing where to look, and I’d rather it be criticized and improved than admired.

    If you spot an error in the setup, a better way to handle double-counting, or a tighter prior for any parameter, I’d genuinely like to hear it.

    Back to the main post

  • 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.