Tag: Organoid Intelligence

  • 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

  • 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.

  • When Biology Becomes the Computer: Why America Needs a Department of Technology to Govern Biocomputing

    1. The Next Computing Frontier

    Every generation of computing has forced a governance decision on the generation that discovered it. Vacuum tubes gave way to transistors, transistors to integrated circuits, integrated circuits to the networked, GPU-driven systems now training today’s large models. Elsewhere on this site, we have made the case that quantum computing is the next frontier serious enough to demand its own international pledges and treaties, and that artificial intelligence built on silicon is already outrunning the elected institutions meant to govern it. Biocomputing is the frontier after that one, and it is different in kind, not merely in degree.

    Biocomputing does not run on silicon at all. It runs on living tissue — neurons grown in a laboratory, wired into electrodes, and taught to process information the way brains do. That single fact changes everything about how the technology should be governed. A silicon chip that fails is scrapped. A biological system that fails, or that succeeds too well, raises questions no purely technical regulator is built to answer. This is precisely the kind of technological convergence — part computer science, part neuroscience, part bioethics, part national security — that we have argued only a cabinet-level Department of Technology is positioned to govern coherently.

    2. What Is Biocomputing?

    Biocomputing is the use of living biological material, most often lab-grown clusters of human or animal neurons called organoids, to perform computation. Researchers culture stem cells into three-dimensional neural tissue, place that tissue on a multi-electrode array, and use electrical, chemical, or optical signals to train it — much as a silicon neural network is trained, except the network is alive.

    This is no longer a thought experiment. Cortical Labs, an Australian biocomputing company, has demonstrated neuron cultures on electrode arrays that can adapt their behavior in a simplified video game environment, learning from feedback in a way that mimics goal-directed play. A team at Indiana University has combined brain organoids with conventional hardware to perform basic speech-recognition tasks. FinalSpark, a Swiss company, has built a cloud platform that lets researchers run remote experiments on living neural tissue over the internet. The U.S. National Science Foundation launched a program in 2024 specifically to fund this field, and — notably — required every research team to include a bioethicist as a co-principal investigator, evaluated on equal footing with the science itself. Researchers at institutions including Johns Hopkins have organized around the term “organoid intelligence” to describe the broader vision of biological tissue as a computing substrate.

    What exists today are small cultures of tens of thousands of neurons performing narrow, supervised tasks — nothing close to a brain, and nothing close to a mind. What is being actively pursued is scale: larger, longer-lived, more densely networked organoids designed to perform more general computation with a fraction of the energy that silicon AI systems consume. What remains speculative is whether, at some future point, biological complexity of this kind could give rise to something with morally relevant experience. All three of those categories — the real, the researched, and the speculative — matter to this article, and we will keep them separate throughout.

    3. Why Biocomputing Creates a New Governance Challenge

    No single American institution is responsible for biocomputing today, and that is the problem. Stem cell research is overseen by university institutional review boards and the FDA. Basic biocomputing research is funded through the NSF and NIH. Defense applications, if and when they emerge, would fall under the Department of Defense and its research arms. Export controls and international coordination would run through the State Department. Consumer-facing biocomputing platforms answer to no dedicated regulator at all.

    The NSF’s decision to require an ethicist on every funded biocomputing team is a meaningful signal that the federal government already senses this technology is different. But it is a grant condition, not a law. It applies to one funding stream, not to industry, not to universities working outside that program, and not to any foreign laboratory. Ethical review of this kind should not depend on which grant a research team happened to apply for.

    This is a familiar pattern to readers of this site. We have watched the same fragmentation play out in artificial intelligence policy, in quantum computing, and in student data protection: a powerful technology advances across a dozen agencies at once, each with jurisdiction over a slice of it, none with authority over the whole. Biocomputing adds a dimension those technologies did not have. It is not just powerful and dual-use. It is alive, and that raises the ethical stakes of getting the governance structure wrong.

    4. The Need for a Department of Technology

    We have proposed elsewhere on this site that the United States create a cabinet-level Secretary of Technology, nominated by the President and confirmed by the Senate, with counterparts elected at the state, county, and local level. Biocomputing is one of the clearest cases yet for why that office needs to exist.

    A Department of Technology would not replace the FDA’s authority over stem cell safety, or the NSF’s role in funding basic research, or the Department of Defense’s national security mandate. It would do what none of those agencies is chartered to do: look across the whole of biocomputing — research, industry, military application, and international posture — and set standards that apply no matter which laboratory, company, or agency is doing the work.

    This site has also proposed the RMS classification framework — Responsive, Memorable, Sentient — as a clear, legislatively usable way to describe the cognitive complexity of artificial intelligence systems. RMS was built for silicon. Biological systems will need a parallel but distinct classification, because a living neural culture is not a language model, and treating it as one in law would badly understate what is actually at stake. A Department of Technology is the right body to build that framework, in coordination with the NSF, NIH, and the existing bioethics community, rather than leaving each biocomputing lab to define its own terms — which is precisely the ambiguity that biocomputing researchers themselves have flagged as an obstacle to responsible oversight.

    5. Biocomputing Must Not Become a Weapon

    Biocomputing should not become a tool of warfare, and the case for that prohibition is stronger, not weaker, than the case we have made elsewhere on this site against weaponized AI.

    Silicon AI systems are opaque, but they are at least deterministic and auditable in principle — their weights can be inspected, their behavior can be tested against a fixed specification. A living neural culture adapts, reorganizes its own connections, and behaves differently across nominally identical experiments, the same way no two brains develop identically. Building weapons systems, targeting logic, or autonomous decision-making on a substrate that cannot be fully specified or reliably reproduced is not just ethically fraught — it is a genuine escalation risk. A biological targeting system that behaves unpredictably under stress is not a safer weapon; it is a less controllable one.

    There is also a dual-use overlap here that does not exist with silicon AI. The laboratory skills, cell lines, and equipment used to grow and interface with neural organoids sit adjacent to the skills, materials, and equipment used in biological research more broadly, including research that could contribute to biological weapons. A Department of Technology should treat that adjacency as a reason for firmer lines, not looser ones.

    6. A Prohibition on Biocomputing for NBC Warfare

    We have previously proposed that a future Secretary of Technology lead an international treaty prohibiting the use of artificial intelligence in nuclear, biological, and chemical (NBC) warfare. Biocomputing needs its own explicit prohibition within that same framework, because it intersects with NBC weapons development along more paths than conventional AI does.

    A Department of Technology should advocate for a clear, enforceable ban on using biocomputing for:

    • weapons development of any kind, including research, simulation, or design work;
    • autonomous weapons systems and targeting logic;
    • offensive military applications generally;
    • biological weapons research, given the direct overlap in laboratory technique and materials;
    • chemical weapons research and development;
    • nuclear weapons research where biocomputing materially contributes to design, simulation, or optimization;
    • any NBC warfare application, including the optimization, design, targeting, or deployment of nuclear, biological, or chemical weapons; and
    • the deliberate creation of biological computing systems intended to facilitate warfare in any form.

    These prohibitions matter for a reason specific to biocomputing: unlike a line of code, a biological system built for one purpose cannot simply be repurposed by editing a file. The laboratories, cell lines, and expertise built up to weaponize biocomputing would themselves become a standing capability, difficult to dismantle and easy to proliferate. A bright-line prohibition, established early and advocated for internationally, is far more achievable than trying to contain a biological weapons-adjacent capability after it already exists. Modeled on the No First Use Quantum Pledge we have proposed for quantum technologies, we propose a parallel No Weaponization of Biocomputing Pledge: a commitment among signatory nations that living computational systems will never be developed, procured, or deployed for military or warfare purposes, verified through the same kind of transparency and inspection mechanisms we have proposed for quantum governance.

    7. The Sentience Question

    A note on terminology before this section begins: elsewhere on this site, “Sentience” refers to the operating system concept we have proposed for the convergence of AI, robotics, and internetworking. This section uses the word in its ordinary philosophical sense — subjective experience, self-awareness, or another morally significant form of cognition — and the two should not be confused.

    No credible researcher claims that today’s neural organoids are sentient. The cultures now in use contain a small fraction of the neurons in a human brain and perform narrow, supervised tasks. But the field’s own literature is candid that consciousness itself has no settled scientific definition, which means there is no clean test researchers can run to confirm or rule out morally relevant experience as these systems scale in size and complexity. That is not a reason to dismiss the question. It is the reason the question needs a governance answer now, before it becomes an urgent one.

    The ethical problem is not “is this organoid sentient?” It is: what should society do if researchers cannot confidently answer that question at all? Waiting for certainty is not a neutral choice. If a biological system were capable of some form of subjective experience and researchers kept experimenting until they were sure, the harm — if it existed — would already have occurred, repeatedly, before anyone acted on it. A Department of Technology should treat that asymmetry as the starting point for policy, not a philosophical afterthought.

    We propose that a Department of Technology establish sentience safeguards and experimental stop criteria before biocomputing experiments of significant complexity are permitted to proceed, built around six commitments:

    1. classify experiments according to their potential cognitive and biological complexity, not merely their stated research purpose;
    2. subject experiments to progressively stronger oversight as that complexity increases;
    3. continuously monitor high-complexity experiments for predefined indicators of unexpected cognitive capability;
    4. require every high-complexity experiment to have an independently reviewed shutdown protocol before it begins, not after concerns arise;
    5. require immediate suspension when predefined warning thresholds are reached, without waiting for research-team consensus; and
    6. route any evidence of potentially morally significant cognition to independent scientific, ethical, and governmental review, not to the research team alone.

    8. Establishing Experimental Stop Criteria

    The hardest question this article can ask is also the most necessary one: at what point should a biocomputing experiment be stopped if there is credible evidence the biological system may be developing something like sentience?

    There is no simple answer, and any article that pretends otherwise is not being honest with its readers. What can be proposed is a defensible standard, built on precaution rather than proof: credible evidence of potentially morally significant cognition → pause → independent evaluation → heightened safeguards → a determination of whether the experiment may ethically continue. Under this standard, a pause is not an accusation that a system is sentient. It is an acknowledgment that the question is serious enough to warrant a second, independent look before continuing.

    What should trigger that pause? None of the following is proof of sentience on its own. Each is a scientifically credible indicator that would warrant independent investigation, and a Department of Technology should require that every high-complexity biocomputing experiment be monitored for signs including:

    • learning capability beyond what the experimental design anticipated;
    • persistent memory that outlasts the specific training session;
    • adaptive behavior of increasing complexity over time;
    • evidence suggestive of self-modeling;
    • communication or output that cannot be adequately explained by the experimental design;
    • apparent preference or aversion in response to stimuli;
    • persistent, goal-directed behavior that continues across sessions;
    • behavioral responses suggestive of distress or suffering;
    • unexpected integration of information across time; or
    • behavior suggestive of self-preservation.

    The purpose of listing these is not to suggest that any current biocomputing system exhibits them. It is to give researchers, review boards, and a future Department of Technology a concrete, falsifiable checklist — something more actionable than a vague instruction to “proceed carefully.”

    9. The Precautionary Principle

    Underlying all of this is a single ethical principle we believe deserves serious consideration, not as established fact but as a working standard for policy:

    If society creates a biological system capable of potentially experiencing the consequences of our experiments, society assumes a responsibility to protect that system from unnecessary suffering, exploitation, and destruction.

    That principle only does useful work if it is paired with careful distinctions, because these six concepts are not the same thing and collapsing them does real damage in either direction:

    • Biological activity — cells firing, signals propagating — is present in every organoid today and implies nothing about experience.
    • Intelligence — the capacity to solve problems — is present in narrow forms in today’s biocomputing systems and, separately, in plenty of non-conscious processes.
    • Learning — adapting behavior based on feedback — has been demonstrated in neuron cultures and also occurs in systems no one considers conscious.
    • Consciousness — some unified, first-person point of view — remains scientifically undefined even in well-studied animal species, let alone lab-grown tissue.
    • Sentience — the specific capacity to experience sensations such as pleasure or pain — is a narrower and more testable claim than consciousness generally, though still contested.
    • Moral status — whether a system’s interests deserve protection — is an ethical conclusion, not a biological measurement, and does not automatically follow from any of the categories above.

    These distinctions matter because researchers in this field have themselves warned that overstating what current systems can do — describing narrow pattern-learning in a dish as if it were closer to a mind — risks a public backlash that could set back legitimate, ethically supervised research. Understating what future systems might become carries the opposite risk. A Department of Technology should hold both risks in view at once: neither sensationalizing today’s organoids nor dismissing tomorrow’s.

    10. Independent Oversight and Transparency

    Safeguards that exist only inside the research team that stands to benefit from a positive result are not safeguards. A Department of Technology should establish:

    • a national biocomputing oversight board, independent of any single university, company, or funding agency, with authority to pause or terminate high-complexity experiments;
    • mandatory registration and public reporting of high-complexity biocomputing experiments, similar in spirit to clinical trial registries;
    • transparency requirements proportional to experimental complexity, so that low-risk research is not buried in paperwork while high-risk research cannot proceed in the dark; and
    • whistleblower protections for researchers who raise safety or ethical concerns about biocomputing work, extending the protections we have already proposed in our AI Whistleblower Protection Act to this domain specifically.

    11. National and International Standards

    Domestically, a Department of Technology should set a floor that no state, university, or company can undercut — the same role we have proposed it play in AI legislation and data sovereignty. A biocomputing lab should not be able to relocate to a state with weaker oversight and continue high-complexity experiments that would have triggered review elsewhere.

    Internationally, the challenge is harder, because biocomputing research is already global. China issued national ethical guidelines for high-risk stem cell research in 2025; European and Australian labs are active in this field; and biocomputing platforms accessible over the internet mean a researcher anywhere can run experiments on organoids housed in another country entirely. A Department of Technology should treat this as an argument for faster American leadership on international standards, not a reason to wait for consensus that may never arrive.

    12. America’s Opportunity to Lead

    We have argued elsewhere on this site that the United States was late to the table on AI regulation, publishing a national AI Action Plan after other governments had already moved, despite having proposed a clearer framework first. Biocomputing offers a chance to get the sequence right: establish domestic standards and an elected, accountable Department of Technology before — not after — the technology matures into something with military applications or unresolved moral status.

    A Secretary of Technology could use that domestic credibility to advocate internationally for:

    • a global prohibition on military applications of biocomputing;
    • the No Weaponization of Biocomputing Pledge described above, extending the No First Use model we have proposed for quantum technologies;
    • shared international safety standards for high-complexity biocomputing research;
    • transparency and reporting requirements for high-risk experiments, applied across borders;
    • independent oversight mechanisms recognized by treaty rather than left to individual national discretion; and
    • internationally recognized criteria for suspending experiments where credible evidence of potentially sentient biological computing emerges.

    Over time, this could grow into a formal international convention on biocomputing, following the same path we have proposed for our International Treaty on Quantum Intelligence — a dedicated, enforceable agreement rather than a patchwork of national guidelines that stop at each country’s border.

    13. From Technological Innovation to Technological Responsibility

    Nothing in this article argues against biocomputing research. The potential upside is real: dramatically more energy-efficient computation, new insight into learning and memory, and biomedical applications that silicon-based AI cannot offer on its own. The argument is narrower and, we think, harder to dispute — that the same country capable of funding and leading this research should also be capable of governing it, and that the governance should exist before the hardest cases arrive, not after.

    This is the same argument we have made about artificial intelligence, quantum computing, and every other technology covered on this site: the purpose of a Department of Technology is not to slow innovation down. It is to make sure humanity’s ability to create powerful technologies does not outrun humanity’s ability to govern them responsibly. Biocomputing, because it is built from living tissue rather than silicon, is simply the clearest example yet of what happens when that gap is allowed to open.

    14. Conclusion

    Biocomputing is still young. The systems that exist today are small, narrow, and almost certainly nowhere near anything resembling a mind. But the researchers building them are already asking whether their field has a definition of consciousness precise enough to know when that changes — and by their own account, it does not yet. That gap between capability and certainty is exactly where governance needs to arrive first.

    A Department of Technology, with authority spanning research, industry, defense, and international diplomacy, is the institution best positioned to close that gap: to keep biocomputing out of weapons and out of NBC warfare entirely, to build stop criteria before they are needed rather than after, and to lead the international effort to make sure no nation faces these questions alone.

    If we are capable of creating new forms of biological intelligence, shouldn’t we establish the ethical and governmental framework for protecting them before we create something whose moral status we do not understand?