Category Archives: Government

How to Build a Local Value Score Without Starting a Partisan Firefight

How to Build a Local Value Score Without Starting a Partisan Firefight

by Braden Kelley and Art Inteligencia

In the first piece in this series, we lit the spark: absolute spending debates rarely move the needle, while peer-relative value, what similar communities achieve per dollar, can. This article opens the instrument. Not the full DIY toolkit yet. The method. The experience architecture. The discipline that turns civic anger into civic learning.

From spark to instrument

Innovation without an instrument is just a mood. Human-centered change without a shared unit of sense-making becomes another round of tribal theater. If you felt the first article land – What If You Could Prove the Government is Ripping Us Off? – the next question is practical:

How do we score a place fairly enough that good-faith people on the left, right, and exhausted middle can argue about the same dashboard?

That is an experience-design problem before it is a political problem. People cannot improve what they cannot sense. So we need an interface for civic value that ordinary neighbors can understand in under two minutes, and that a finance director can challenge without inventing motives.

Call the instrument a Relative Value Score (RVS): a transparent, peer-normalized view of outcomes per cost-adjusted dollar, with a clear path to “why the gap.” The revolutionary move is not inventing a grade that flatters your side. It is building a comparison citizens trust enough to use in budget season.

What a Relative Value Score actually measures

A Relative Value Score is not “how big is the budget?” Bigger places spend more. Harder case mixes cost more. Expensive labor markets pay more. Treating raw spend as proof of compassion, or of waste, is how we keep fighting about identity instead of delivery.

Conceptually:

RVS ≈ how close you are to the best peer in your cohort on value delivered per dollar—across a small set of outcome domains, cost-adjusted, with published weights and sources.

Think of it like comparing restaurants of the same type and price band on food quality per dollar, not comparing a food truck to a banquet hall and declaring a winner by total receipts. Futurology loves flashy national indexes. Experience design prefers local instruments that feel fair in the neighborhood where life is actually lived.

Three design rules keep the score human-centered:

  • Outcomes over optics. Prefer measurable delivery (response times, learning growth, pavement condition, permit latency, unit cost of capital) over press-release volume.
  • Cost-adjusted dollars. Adjust for regional price and wage differences so “expensive place” is not automatically “bad place.”
  • Relative, not absolute holiness. Normalize within the peer cohort. The best peer becomes the teacher. Everyone else gets a gap to close—or explain.

Improper payments, fraud risk, and opaque grantee chains still matter. They belong on a related but separate track (more below). Mixing “inefficient” with “corrupt” in one opaque number is how movements lose the room on the first contested claim.

Peers first: the fairness engine

Most “benchmarking” fails before the math starts—because the peer set is a vibes playlist. Comparing a dense coastal city to a rural county, or a high-poverty district to an affluent one using unadjusted test levels, is not accountability. It is ammunition.

Peer cohorts are the fairness engine. Build them first. Argue about them in public. Version them. That is change leadership: put the contested assumptions where people can see them.

Practical cohort dimensions by layer:

  • Cities: population band, density, metro/rural context, regional price/wage index.
  • Counties: population, urban share, mandate set (courts, jails, public health, roads, elections).
  • States: population scale, economic mix, federal transfer share—used carefully; states are multi-product machines.
  • School districts: enrollment, % students in special education / English learners / free-or-reduced lunch (or equivalent need measures), urbanicity, regional educator wages. Prefer learning growth / value-add over raw proficiency levels whenever possible.
  • Water and special districts: service population, infrastructure age/type, treatment complexity, geography—unit cost of delivered service beats “total budget” every time.

A good peer set is small enough to be intelligible and large enough that one outlier cannot redefine “normal.” Publish who is in the cohort and why. Invite the entity to propose alternate peers with evidence. Right-to-reply is not weakness. It is how trust compounds.

Domains, weights, and the “why the gap” story

Do not boil the ocean. Innovation teams ship thin slices. For a first local scorecard, pick 8–12 metrics max across a few domains—not 200 indicators nobody reads.

Example domain shapes (customize by entity type):

  • Service delivery: outcomes or service quality per dollar or per FTE (clearance, response, throughput, condition).
  • Administrative load: central office / admin share versus program or instructional spend.
  • Labor intensity: compensation density (payroll, overtime, benefits where available) versus outputs.
  • Contracting & capital: competitive bid share, concentration, unit cost and schedule slippage on comparable projects.
  • Transparency: timely CAFRs/budgets, machine-readable checkbooks, FOIA latency—inputs to trust, not vanity.

Weights should be public, few, and revisable. Publish a default weighting, and let citizens toggle sensitivity modes (“weight learning growth higher,” “weight admin share higher”) so critics can fork the story without assassinating the messenger. That is open innovation applied to civic sense-making.

The score is the headline. The product is the “why the gap” decomposition: which 2–3 drivers explain most of the distance from the best peer, each with sources and confidence. Without that story, a score is just a ranking for dunking. With it, a score becomes a change agenda.

Two tracks: performance vs. integrity

Human-centered accountability refuses false equivalence. A district can be expensive relative to peers and still honest. An agency can look “lean” on paper and still run integrity red flags. Smash those into one “corruption score” and you have built a weapon, not an instrument.

Keep two panels on the same dashboard:

  • Performance track (RVS): peer-relative outcomes per cost-adjusted dollar. This is the main civic interface.
  • Integrity overlay: audit findings, delayed reporting, sole-source patterns, related-party flags, outcome-light grantee chains—shown with higher evidentiary bars, source documents, and room to reply. Treat AI here as a highlighter for human review, never as a judge pronouncing guilt.

This separation is how a multipartisan movement survives contact with reality. Precision is the revolutionary ethic. Smear is the cheap substitute.

A weekend walkthrough for one jurisdiction

You do not need the full toolkit to practice the method. You need a first mile. Here is a human-sized walkthrough you can start this weekend—before the downloadable suite and guidebooks ship.

  • 1. Name the entity in one sentence. “Our K–12 district,” “our city general fund,” “our water district”—passion plus a border beats rage at “the system.”
  • 2. Pull Tier-A documents. Latest adopted budget, CAFR or annual financial report, salary schedule or checkbook export, and any published outcomes dashboards. Note what is missing.
  • 3. Draft a peer shortlist. Five to fifteen similars using the cohort dimensions above. Write one paragraph defending the set.
  • 4. Choose eight metrics you can source. Prefer unit costs and outcomes over vibes. If a metric cannot be sourced, park it—do not invent it.
  • 5. Build a crude gap view. For each metric, who is best in your peer set? Where are you? What is the story in two drivers?
  • 6. Separate integrity notes. If you see red flags, log them with links and confidence—not as the score itself.
  • 7. Write three budget-season questions. Short enough for a three-minute public comment. Specific enough that staff must answer on the record.

That walkthrough is reconnaissance, not a finished product. Finished products need versioned methods, entity right-to-reply, and consistent cohort rules. That is exactly what the forthcoming DIY tools and role-based guidebooks are for—so local teams are not reinventing discipline in the dark.

What comes next—and how to stay in formation

Method without movement is a PDF nobody uses. Movement without method is a mob with a megaphone. We are building both: open instruments for peer-relative value, and a network of roles—data gatherers, benchmarking intelligence creators, website hosts, promoters, meeting advocates, media partners—who can stand up local scoreboards when the kits are ready.

In the next pieces in this series, we will walk a single jurisdiction end-to-end as a chapter-lead story, then open the downloadable suite: data gathering, careful AI-assisted processing under human guardrails, and public benchmarking sites neighbors can actually use.

Between method and toolkit, stay on the bus.

If this article sharpened your sense of how fair comparison should work, subscribe to Human-Centered Change & Innovation Weekly. That list is how you get first notice when the guidebooks and DIY tools move from spark to usable firepower—practical next steps for the jurisdiction and role you choose.

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Forward this to the person who already knows the peer set in their head—and to the person who keeps saying the comparison “isn’t fair” without ever defining fair. Fairness is designable. That is the work.

Absolute spend is a slogan. Peer-relative value is an instrument. Instruments are how human-centered futures get built.

Frequently Asked Questions

What is a Relative Value Score (RVS)?

A Relative Value Score is a peer-normalized measure of outcomes per cost-adjusted dollar for a city, county, state, school district, or special district. It shows how close an entity is to the best performer among similar peers across a small set of transparent metrics—not whether its total budget is large or small in absolute terms.

How do you choose fair peers for comparison?

Fair peers share similar constraints: size and density for cities; mandate mix for counties; enrollment and student-need mix plus regional wages for school districts; service population and infrastructure profile for water and special districts. Publish the cohort rules, keep the set large enough to be meaningful, and allow entities to propose alternate peers with evidence. Unadjusted “leaderboards” that ignore poverty or cost of living are not fair benchmarks.

Should fraud and performance be one score?

No. Keep a performance track (peer-relative value) separate from an integrity overlay (audits, delayed reporting, sole-source patterns, opaque grantee chains) that requires stronger evidence and human review. Combining them into one “corruption score” destroys credibility and unfairly smears underperforming but honest agencies.

Image Credits: Pexels

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Cursor to clean up the article.

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What If You Could Prove the Government is Ripping Us Off?

What If You Could Prove the Government is Ripping Us Off?

by Braden Kelley and Art Inteligencia

We are living through a crisis of civic experience. People can feel that they’re being ripped off by their elected and administrative officials, but yet they lack a fair way to prove it. The future will not be built by louder arguments alone. It will be built by better comparisons: value delivered per dollar, relative to peers who look like us. That is the spark for a human-centered citizen movement. First, let’s look at the scale of the problem already documented in the public record.

The bill without a receipt

Most of us do not experience government as a spreadsheet. We experience it as a journey: the permit that takes months, the school board meeting that buries tradeoffs in jargon, the water bill that climbs while the story never quite lands. That journey is the real product. And if experience design has taught us anything, it is this: when the interface of truth is broken, people do not get smarter by trying harder, they get cynical.

Cynicism is not a character flaw. It is a rational response to incomplete design.

We are asked to fund systems whose performance is rarely presented in the only language that makes human comparison fair: what does a place like mine get for money like mine?

Imagine, just for a moment, learning that a school district with a nearly identical enrollment mix educates children with far less administrative drag and better learning growth per instructional dollar. Or that a peer city issues permits, paves roads, or clears cases at a cost structure that cannot be waved away with “we’re special.” That is not a conspiracy theory. That is a design question. Design questions can be answered, if we stop accepting fog as a management strategy.

Before we build the instruments of peer comparison, we have to stop understating what already leaks, bloats, and under-delivers in plain sight. Innovation begins with facing reality, not romanticizing it.

The scale hiding in plain sight

If this were only a few bad actors and a few delayed audits, a nice annual report and a press release would be enough. The public record says otherwise. What we are looking at is a systems problem: structural error and theft measured in hundreds of billions; spending that sprints past simple population-and-inflation baselines; and institutions whose staffing mix drifts toward administration while the work citizens think they are buying becomes relatively thinner.

Let’s start at the federal level, not because your city council or school board is less important, but because the national numbers are large enough that denial starts looking like a lifestyle choice.

The U.S. Government Accountability Office (GAO) estimated that direct annual financial losses to the federal government from fraud fall between roughly $233 billion and $521 billion a year, based on fiscal years 2018–2022 risk environments (GAO-24-105833 highlights; full report: PDF). That is not a rounding error. That is a second economy of loss living inside the first.

Then there are improper payments – the broader leak that includes overpayments, underpayments, documentation failures, and more. Agencies reported about $236 billion in improper payments in fiscal year 2023 (GAO FY2023 overview) and still about $162 billion in fiscal year 2024 after some pandemic programs wound down (GAO FY2024 press release; GAO-25-107753 PDF). Cumulative improper-payment estimates since fiscal year 2003 approach about $2.8 trillion (see GAO-25-107753). Earlier in the same stretch: roughly $281 billion in FY2021 (GAO) and $247 billion in FY2022 (GAO).

Let’s be clear, as any honest change leader must be: improper payments are not all intentional fraud. But they are the proof that our control experience is broken. If you cannot reliably send the right money to the right place, you do not have a “communications” problem. You have a design problem.

Bar chart of federal improper payment estimates for FY2021 through FY2024, declining from roughly $281 billion to $162 billion but remaining very large.

Federal agency-reported improper payment estimates remain measured in hundreds of billions even after the pandemic peak. Sources:
GAO FY2021 (~$281B);
GAO FY2022 (~$247B);
GAO FY2023 (~$236B);
GAO FY2024 (~$162B).
Scale of loss is one window. Scale of spend is another. Total federal net outlays rose from roughly $3.5 trillion in fiscal year 2014 to roughly $6.7 trillion in fiscal year 2024 – nearly a doubling in a decade in nominal dollars, with the pandemic rewriting the shape of the curve. Those figures come from the U.S. Office of Management and Budget historical outlay series published via the Federal Reserve Bank of St. Louis as FRED series FYONET, with the broader tables at OMB Historical Tables.

More spending can mean more service, more obligations, an older population, emergencies. Futurology without humility is just sci-fi cosplay. But here is the innovation insight most public debates miss: absolute “we spent more” tells citizens almost nothing about unit cost, quality, or leakage. When the size of the system grows, the need for peer-relative instruments grows with it. Otherwise we are asking people to navigate a denser fog with the same broken map.

Bar chart showing U.S. federal net outlays rising from about $3.5 trillion in FY2014 to about $6.7 trillion in FY2024.

U.S. federal net outlays, selected fiscal years (nominal dollars). Source:
OMB Federal Net Outlays (FYONET) via FRED.
See also OMB Historical Tables.
Now bring it closer to home – for many of us, literally. Washington State’s Near General Fund–Outlook (NGF-O) operating budget grew from about $31.2 billion in the 2011–13 biennium to about $80.2 billion for 2025–27, roughly a 157% increase in nominal dollars, as laid out by the Washington Policy Center using the state’s NGF-O series. The 2023–25 NGF-O package, after the 2024 supplemental, was about $71.9 billion (Legislative Budget Notes PDF). The same analysis notes that if spending since the mid-2010s had tracked only population and inflation, today’s scale would be tens of billions lower. Growth is real. “We only kept up with costs” is a different claim. Official statewide spending trails also live at fiscal.wa.gov.

This is not a Washington-only story. It is a pattern story: when the bar chart climbs and the experience of value does not climb with it, citizens notice – even if they cannot yet prove the gap against peers.

Bar chart of Washington state Near General Fund–Outlook biennial operating budgets rising from $31.2 billion in 2011–13 to about $80.2 billion in 2025–27.

Washington NGF-O biennial operating budget scale (nominal). Sources:
Washington Policy Center NGF-O growth summary
(2011–13 ≈ $31.2B; 2025–27 ≈ $80.2B; ~157%);
2023–25 NGF-O ≈ $71.9B (Legislative Budget Notes PDF);
mid-decade ~$38B scale as described in the same WPC overview of the decade-long climb.
Then there is administrative gravity, especially in higher education, where parents still believe they are buying teaching first. The Delta Cost Project at the American Institutes for Research documented something experience designers would call a quiet interface change: as managerial and professional administrative ranks grew, the average number of faculty and staff per administrator fell by roughly 40 percent at many four-year institutions between 1990 and 2012, landing near 2.5 or fewer faculty and staff per administrator (Desrochers & Kirshstein, Labor Intensive or Labor Expensive?, 2014 (PDF); AIR press summary). Professional non-faculty roles often grew faster than full-time instructional capacity, even as campuses leaned harder on part-time instructors. The underlying institutional data ecosystem lives in IPEDS and the Delta Cost Project database.

Is every new position waste? Of course not. Human organizations need coordination. The revolutionary question is not “abolish administration.” It is: do we have a transparent, peer-relative view of what administration costs relative to learning and outcomes — or are we just hoping for the best?

Bar chart showing faculty and staff positions per administrator declining from roughly 3.3 around 1990 to about 2.3 around 2012.

Illustrative faculty-and-staff-per-administrator levels reflecting Delta Cost Project / AIR findings of roughly a 40% decline from 1990 to 2012 at many four-year campuses, averaging about 2.5 or fewer faculty and staff per administrator.
Source: Desrochers & Kirshstein (2014) PDF.
And then there is the softer, stickier problem: corruption perceptions and nonprofit “grift” that thrives in long outcome chains. In occupational-fraud cases studied by the Association of Certified Fraud Examiners (ACFE), government organizations and for-profit firms land around a median loss of about $150,000 per case, while nonprofits still show up in about one in ten cases, with smaller median losses (about $76,000) that can still wreck a thin-margin mission (Occupational Fraud 2024: A Report to the Nations (PDF)).

Here is the human-centered insight: when public dollars pass through contractors and tax-exempt intermediaries, the distance between the taxpayer’s intention and the citizen’s experience often grows. High overhead, related parties, vague deliverables, and glossy stories without unit costs are not always illegal – but they can be a failure of value design. Law can punish fraud. Only better metrics can expose underperformance that still has good branding.

Trust tracks this story. Transparency International’s Corruption Perceptions Index has scored the United States in the mid-60s out of 100 in recent years, with multi-year deterioration flagged by Transparency International U.S. (see their CPI 2025 statement (PDF)). When perceptions fall, governance gets more expensive — because every negotiation becomes theater, and every reform takes more energy to land.

So stack it up without theatrics: hundreds of billions a year in federal fraud-loss risk and improper-payment leakage; state operating budgets that can more than double across a decade and a half; staffing mixes that load coordination relative to the work many people believe they are buying; pass-through chains that hide unit economics. That is enough reason to innovate how citizens see value. It is not a license to smear every public employee. Revolutionary change worth having is precise, not performative.

Sources for the scale claims above

Why shouting about “too much spending” never ends the argument

American public argument too often collapses into two dead ends. One side treats every dollar as proof of compassion. The other treats every dollar as proof of waste. Both can be partially right, and still leave neighbors holding a slogan instead of a shared fact.

Absolute spend is a weak instrument for learning. Larger places spend more. Harder case mixes cost more. Expensive labor markets pay more without automatically proving mismanagement. When numbers ignore context, people fight about identity instead of performance. That is not governance. That is sportswashing for budgets.

Experience design offers a simpler truth: people cannot improve what they cannot sense. If the “interface” of civic truth is either opaque PDFs or cable-news moral theater, the lived journey of taxpayers becomes cynicism – then disengagement – then the quiet permission structure where bloat, under-delivery, and yes, fraud, get more time than they deserve.

The future of healthier institutions will not be won by volume alone. It will be won by better comparisons ordinary people can use without a PhD in public finance. That is human-centered change in one sentence: redesign the sense-making layer, and better action becomes possible.

A better unit of civic truth: value relative to peers

Futurology has a bad habit: it over-promises technology and under-specifies culture. Here is the cultural upgrade that matters first.

Score places the way adults already rank experiences in the rest of life, relative to alternatives that should be similar.

Peer-relative value asks a sharper question than “how much did we spend?” It asks: among entities of roughly the same size and constraints, who delivers more real outcomes per dollar – and who is the best performer we should be learning from?

That shift changes the emotional temperature of accountability. It is harder to dismiss a neighbor when the comparison is another city that looks like yours, another county with a similar mandate set, another school district with a similar student population, another water district with similar infrastructure age. The conversation stops being “are you for or against government?” and starts being “why are we so different from our best peers?”

Relative performance does not erase values. It clarifies delivery. Compassion with weak unit economics is still compassion, and also an unfinished design problem. Efficiency without outcomes is just thrift cosplay. Citizens deserve both: what was intended, and what was delivered, at a cost that survives peer daylight.

This is innovation in the classic sense: not novelty for its own sake, but a better way of creating and measuring value — at the civic layer, where most of life is still lived.

Where fraud, grift, and bloat actually hide

Serious fraud is not always cinematic. More often it is procedural: sole-source patterns that never face real competition; nonprofit pass-throughs that struggle to show outcomes while still collecting public purpose; administrative layers that expand faster than service quality; capital projects whose unit costs and schedule slips never get benchmarked against places that built something comparable; delay as a shield because documents arrive too late to matter.

This is where passion becomes useful, and where discipline becomes non-negotiable. Performance gaps and integrity red flags are not the same thing. Treating every underperforming budget line as a crime story destroys trust the first time a good-faith agency is smeared. Treating every audit as “politics” is how poor design gets tenure.

A mature citizen practice separates the tracks, think of them as three instrument panels on the same dashboard:

  • Performance: outcomes and service quality relative to cost among peers.
  • Structure: administration share, contracting concentration, salary density versus output.
  • Integrity: delayed reporting, audit findings, related-party patterns, outcome-light grantee chains—presented with sources, confidence, and room to reply.

That separation is not gentleness toward corruption. It is how legitimate pressure remains standing when the pushback arrives. Revolutions that last are the ones that can still tell the truth under scrutiny.

The future of accountability is local (and buildable)

National drama can make local work feel small. It is not. Your life is administered by boards, districts, counties, cities, and agencies that set real prices for real services within a few miles of your door. Those are also the levels where a committed group of citizens can still change the information environment in a single budget cycle.

What is newly possible, and this is the innovation hinge, is not “AI as magic.” Magic is for marketing decks. Real innovation is AI as scale applied to the boring, necessary labor of extraction, classification, plain-language briefing, and pattern spotting — always subordinated to transparent methods and primary records. The opportunity is a suite of do-it-yourself tools that a passionate local team can download, stand up, and own: gather public data, process it with clear human oversight, and publish peer benchmarks for the jurisdiction they care about.

Think of it as open experience architecture for citizenship: not a single national score imposed from above, but many local instruments speaking the same comparative language. Cities. Counties. States. School districts. Water boards and special districts. Same idea. Local ownership. Peer daylight.

Movements fail when they ask everyone to wait for a capital-city hero. They gain power when they give capable people a way to begin where they already have skin in the game—and when the path from “I care” to “I can host a public scoreboard” is designed, documented, and downloadable. That is human-centered change: remove friction between intention and action.

From spark to local firepower: what you can actually do

Enthusiasm without a next step is just another scroll. So here is the honest promise of this movement: we are building the instruments — and you choose the jurisdiction, the intensity, and the role that fits your life.

Start with a target you can describe in one sentence. Your school district. Your city budget. Your county contracting. Your state’s administrative stack. Your water or sewer or flood-control district, the special-purpose governments that spend real money while almost nobody is watching. Passion plus a defined entity beats vague anger about “the system” every time.

Then choose a depth of start that matches your week, not your fantasy of free time:

  • Weekend scout: Pull the latest budget, CAFR, or checkbook export. List the top five cost centers. Note what is missing — outcomes, headcount by function, sole-source awards. That alone is civic reconnaissance.
  • Meeting witness: Show up once a month with three peer-comparison questions written in advance. Serious questions change how staff prepare—and how journalists listen.
  • Budget-season cadence: Build a small team and a calendar tied to when appropriations still can move. That is when numbers still have opponents who can feel them.
  • Local scoreboard: When the toolkit ships, stand up a public site — data intake, AI-assisted processing under human guardrails, and peer-relative benchmarks neighbors can share without translating bureaucracy dialect.

You do not need to do everything. You need a first mile. The guidebooks we will publish will walk those miles: how to FOIA without burning out, how to structure a peer cohort fairly, how to avoid turning a performance gap into a defamation trap, how to brief a board in three minutes, how to partner with a local reporter as an ally rather than an ambush.

Lighting a fire does not mean burning institutions down. It means raising the temperature of truth until fog can no longer survive as a management strategy—and giving thousands of local teams the same matchbook.

Pick a role that fits how you show up

From the outside, movements look monolithic. From the inside, they are division of labor — just like every innovation team that ever shipped anything that mattered. You do not have to become a full-stack auditor, web host, and public speaker on the same Tuesday night. Find the work that matches your temperament. Then find one person whose temperament complements yours.

Data gatherers and custodians. You enjoy documents more than microphones. You pull budgets, salary schedules, bid awards, board packets, 990s tied to public grants. You file public-records requests, keep the source folder honest, and leave a trail so nothing depends on a single hero’s laptop. Without clean intake, every downstream score is theater.

Benchmarking intelligence creators. You want the “so what.” You define peer sets carefully, normalize costs, choose outcome metrics that survive scrutiny, and write method notes so a critic can challenge the math without inventing motives. You turn spreadsheets into stories: unit cost of pavement, administrative share of a district, permit latency per FTE, learning growth per instructional dollar — always versus true peers. When the AI processing stack is ready, this role runs it with human judgment still on the wheel.

Website hosts and local product owners. You are willing to stand something up for your community: a place where neighbors can see the score, the sources, the trends, and an invitation to correct errors. You care about reliability and clarity – not turning a civic tool into a partisan meme machine. The downloadable suite is for you: templates, deployment path, content structure—so passion is not blocked by “I don’t know how to ship a site.”

Promoters, translators, and evangelists. You are the bridge. You do not have to invent the model. You make sure it reaches PTAs, rotary clubs, neighborhood groups, faith communities, taxpayer groups, student journalists, and people who will never open a CAFR unprompted. You translate peer-relative value into plain language, share uncomfortable comparisons without contempt, and keep the movement multipartisan enough that the score—not the team jersey—remains the headline.

Meeting advocates and budget-season operators. You take the brief to the microphone. Three questions. One peer chart. A written record. You show up when the appropriation can still move. The action packs we will release are for this role: scripts, FOIA companions, and “why the gap” one-pagers for boards and councils.

Local media partners and explanation designers. You help facts travel. A retired editor, a podcast host, a newsletter writer, a visual explainer—anyone who can turn a transparent ranking into public attention that demands reply rather than rumor. Credible pressure almost always needs a second institution’s megaphone.

Tutors of the top decile. When a peer is crushing your entity on value, someone should study them without ego—procurement habits, staffing ratios, open-data practices, facility utilization, grantee outcomes. Celebrating excellence is not a side quest. It is how reform becomes copyable instead of merely shaming. That is continuous improvement in civic form.

If you have ever left a public meeting thinking, “Someone should document this properly,” there is a role with your name on it. If you can explain a hard idea at a kitchen table, there is a role. If you can keep a folder organized, there is a role. The only non-role is permanent spectator—assuming someone else will finish the counting.

From newsletter spark to the bonfire of tools

The first act of a movement is not a software download. It is a shared refusal: we will no longer treat uncompared spending as a finished explanation of life. We will learn to ask for peer-relative value. We will demand receipts ordinary people can follow. We will treat the best performers as teachers, not enemies.

The second act is capability. That is the work now underway: do-it-yourself tooling for data gathering, careful AI-assisted processing, and public benchmarking intelligence — plus a series of role-based guidebooks so data gatherers, intelligence creators, website hosts, promoters, and budget-season operators are not inventing discipline from scratch in the dark.

Between spark and bonfire, there is a simple way to stay in formation: join the people who want the heads-up when the kits go live, when the next methods article drops, and when the first local teams start publishing peer scores others can fork and improve.

If this article lit something in you, subscribe to Human-Centered Change & Innovation Weekly. Use that signup as your seat on the bus for this movement. When the downloadable tools and guidebooks are ready — beyond the idea, into usable firepower — that is how you will know first, with practical next steps you can take in the jurisdiction you choose.

Prefer the full signup page? Open the newsletter signup page.

Tell a friend who sits through the same meetings and mutters the same unfinished sentence. Forward this to the person who always says, “If someone would just pull the numbers…” Forward it to the person who already pulls numbers but has nowhere trusted to publish them. Movements scale by invitation more than by manifesto.

For now, sit with the question every zip code deserves:

What if your community pays more and gets less than its true peers — and the only reason it continues is that nobody has finished the counting?

If that question lands, you are already part of the movement. Choose a jurisdiction. Choose a role. Get on the list. We will build the matchbooks — toolkits, methods, and guidebooks — so when you are ready to strike, the fire has somewhere local, human, and bright to go.

Frequently Asked Questions

What is “peer-relative value,” and why is it better than arguing about total spending?

Peer-relative value compares what similar governments achieve per dollar — schools with similar student needs, cities of similar size and density, utilities with similar infrastructure ages — rather than treating raw budget size as proof of success or failure. It makes accountability fairer because it adjusts for context, and sharper because it points to real best performers citizens can learn from. In experience-design terms: it gives people a better interface for understanding value.

Does this approach accuse every high-spending community of fraud?

No. Serious, human-centered accountability separates performance gaps (weaker outcomes or higher unit costs than peers) from integrity red flags (audit issues, opaque grantee chains, noncompetitive contracting patterns) that require stronger evidence. Relative benchmarking creates pressure for better results; it is not a substitute for investigation, law, or due process—and it should never be used as a license to smear people who serve in good faith.

How can I get involved before the tools are fully available?

Choose one jurisdiction you care about, start basic public-record reconnaissance, and pick a role that fits how you show up — data gathering, benchmarking intelligence, website hosting, promotion, meeting advocacy, or media partnership. Subscribe to Human-Centered Change & Innovation Weekly at bradenkelley.com/contact-me/newsletter-signup/ so you are first in line when downloadable toolkits and role-based guidebooks are ready to help stand up local peer-benchmarking sites.

Image Credits: Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Cursor to clean up the article.

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We Need More Innovators and Scientists in Leadership Roles

We Need More Innovators and Scientists in Leadership Roles

GUEST POST from Pete Foley

Our world is changing at an unprecedented rate. We are in an innovation driven economy. AI, genetic manipulation, energy innovation, climate, and virtually anything driving change are all highly technical and complex. And all come with high stakes pros and cons.

Scientists and innovators navigating this requires strategic leadership that understands technical complexity, uncertainty and that collectively has some knowledge of basic science and engineering. 

Politics Lacks Scientists: Today, while more than half of US Senators have a law background, only one has a science PhD.  I believe this creates a serious gap in fundamental knowledge between our strategic leaders and the innovators that are driving change.

Experts or Oracles? Of course, our leaders have access to ‘experts’ to help them with complex topics.  But when the fundamental knowledge gap between leaders and experts becomes too big, experts become oracles. They pronounce rather than persuade. When this happens we risk the determining factor in strategy becoming superior communication skills, instead of knowledge or superior ideas.  The ideas (and regulations) that win are not the necessarily best ones, but the ones championed by good communicators, salesmen scientists or smooth talking lobbyists.  It’s dangerous to follow the science blindly, and even riskier to regulate what we don’t understand. That invites dangerous unintended consequences. But increasingly, that is the path we are on.
 

Why We Need More Innovators and Scientists in Leadership Roles

Of course, our leaders don’t need to all be 160 IQ polymaths with PhD’s in quantum mechanics. But to make good decisions they do need to at least be able to understand and apply critical thinking to the inevitably conflicting opinions of experts.

Communicating Science and Technology: Now of course, much of the onus for promoting understanding of complex technology lies with us in the broader innovation and science community.  If we cannot communicate knowledge to people who own resources and executive power, then we risk that knowledge becoming redundant.

But communication is always a two way street. Bridging between leaders and experts requires some common ground.  It’s really hard to have a useful discussion with someone who does even have a basic vocabulary for a topic. As technology and innovation become increasingly important, without more technically savvy leaders we risk a disconnect between strategy, regulation and knowledge. As our leaders get older, and more disconnected from the science driving change they rely less on quality of ideas, and more on appealing framing of ideas, or perhaps familiarity with equally disconnected experts. That is a dangerous path.

Non Scientific Mindsets Facing Technical Challenges. One key danger is the tendency to view choices as binary, another is sunk cost. Binary choices are superficially easy, but in the real world most innovation is not black and white, but instead involves some form of trade off.  Whether it is AI, energy strategy, pharmaceutical development or one of the other ever growing list of emerging technologies, there are benefits, but also costs.  With AI for example, the benefits of gaining and holding global leadership of the technology are likely as economically huge as the opportunity cost of not doing so.  But with big opportunity also comes big risks, including the environmental costs of data centers, risks to societal structure, and even existential risk to humanity itself.  The stakes don’t get much higher.

The Uncertainty Principle: And this is multiplied by the sunk cost fallacy. Over commitment to an incorrect binary choice can be really risky. While we know there are going to be pros and cons to any new technology, we rarely understand them very well in advance.  Innovation is by definition a dive into the unknown, and that makes accurately predicting both upsides and downsides really difficult.  This requires flexible, agile thinking, openness to new data, and a willingness to adjust mid-flight, skills inherent to science and technology . 

But as a society, if anything we seem to be moving away from flexible thinking, and towards more rigid viewpoints that are often heavily pre-primed by affiliations, preconceptions and bizarrely, politics.  People are often passionately for or against AI, but all too often without really knowing why. ‘Green’ energy is polarizing, climate change is divisive.  But while passion and ownership have their place, often the best answer is not cheerleading for a team. Instead it’s beneficial to find a flexible balance that acknowledges the pros and cons, and that ideally identifies non zero sum answers for those contradictions. But that again typically requires nuance, and some level of technical understanding. 

Finding Non Zero Sum Answers: The good news is that once we step away from polarized and binary thinking, non zero sum solutions are sometimes not as hard to find as we think.  Just as an example, with AI, there is potential to have our cake and eat it.   If we cut out digital slop, it’s conceivable that could we achieve and maintain technology leadership, but with much lower environmental cost.  For example, using AI to solve complex medical problems may be a net benefit that is worth some damage to our wilderness, or use of our scarce resources.  But action figures, generic illustrations, mediocre music and often pointless copies of master artists not so much!  I’m sure all of the latter help advance our knowledge to some degree, and help to justify AI investment, but by being more selective, could we achieve the same or similar ends with a superior benefit/cost ratio? 


The Human Advantage: But making smart trade-off decisions like this requires flexible and creative thinking.  Ironically that is one of the things humans still do better than AI.  We just need to embrace our human strengths, but also make sure our leaders also reflect those strengths.

Innovators in Leadership Roles: This means we need a more balanced and scientific approach to leadership if we are navigate the increasingly technology driven future.  Having lawyers making laws is not bad per se, but I passionately believe we need a more diverse set of skills at our upper leadership levels if we are to effectively navigate the coming years. That means the innovation and scientific community needs to step up.  We also need to get much better, and mea culpa, at communicating complex issues.  It’s critical to be clear and simple but not simplistic.

The Tyranny of Simplicity: Simplistic answers, memes, and binary choices have a great deal of superficial appeal.  And politicians and the media exploit this very effectively. In our information overloaded, time constrained world, everybody’s cognitive bandwidth is stretched.  We often seek answers rather than understanding because that’s all we have time for.  But from a leadership perspective, we need to understand that limited cognitive bandwidth is not the same as limited intelligence. People may grasp for simplistic answers, but because they have no commitment to them based on their own knowledge or critical thinking, that grasp is tenuous. This means that being simplistic can be self defeating in the long run.  For example, take the much quoted, ‘globally agreed’ climate target; to not exceed a 1.5 degrees Celsius increase since pre-industrial times. For sure, some people will accept this without question. But other enquiring minds will ask if 1.49C OK? Is this a tipping point? Do we fall of a cliff at 1.51C. Conversely, what happens if we exceed that limit and nothing dramatic happens?  Do we discard that boundary, or move it? Then there are obvious questions around how we address that boundary. What will it take to prevent crossing it?  What are the trade offs?  Who has the sphere of influence to actually make a difference?  It’s OK to have a simplistic position, but it needs to be supported by layered reasoning.


Cry Wolf: I’m not suggesting that climate scientists who promote 1.5C don’t grasp this complexity.  But somewhere in the path from science to politicians and media the real world complexity it often gets lost in translation.  And thats not trivial, as it creates the risk of ‘cry wolf’ effects, and of leaders being perceived as manipulative.   If we overstate the importance of 1.5 C, and it proves to be wrong, or at least a softer limit than previously advertised, we risk people perceiving that they have been mislead or manipulated.  That then feeds skepticism, and even gives support to some of the wilder ‘conspiracy theories’. Once a source has become discredited on one vector, it is typically discredited on everything. 

No easy answers to this.  But I believe innovators and scientists really need to take a bigger leadership role in a world where innovation is increasingly the driving force. Politicians generally don’t get elected because they deeply understand complex issues, but because they understand how to motivate, communicate, simplify and manipulate. They often rely on peoples limited cognitive bandwidth, as this helps them to craft simple slogans, concepts, and sometimes trigger fear and division. Remember that we dislike losing something about twice as much as we like gaining it, which makes fear a very powerful manipulative tool. That brings power, but not necessarily wisdom. But limited cognitive bandwidth is not the same as limited intelligence. And simplistic concepts are vulnerable to challenge, or evolving data.

Of course, we don’t want to make every issue a PhD thesis.  But we do need to acknowledge increasing complexity and uncertainty, and at the very least develop authentic, layered narratives that acknowledge complexity and the inevitable uncertainty of an innovation driven world.  Without that, our strategies become extremely fragile, and easily shattered the first time we are proved wrong. Even if we may start from a position of intense conviction, we must also change paths in the face of compelling evidence. Scientists and innovators tend to be good at this. It’s a skill that maybe needs to be used more broadly

Image credits: Google Gemini

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The End of AI Data Centers

Why Decentralized Compute is the Only Resilient Future

LAST UPDATED: May 11, 2026 at 11:24 AM

The End of AI Data Centers

by Braden Kelley and Art Inteligencia


I. Introduction: The Fragility of the AI “Crown Jewels”

The race to dominate artificial intelligence has triggered a global construction boom unlike anything the technology industry has ever seen. Governments and corporations are pouring hundreds of billions of dollars into massive AI data centers packed with advanced GPUs, specialized networking hardware, and enough electrical infrastructure to power small cities. These facilities are rapidly becoming the economic and strategic “crown jewels” of the twenty-first century.

But in the rush to scale AI capability, we may be building exactly the wrong architecture for the world that is emerging around us.

The current model of AI infrastructure is overwhelmingly centralized. Instead of distributing compute across millions of smaller nodes, we are concentrating unprecedented amounts of economic, military, and technological capability into a relatively small number of gigantic facilities. Each hyperscale AI campus represents not only a massive financial investment, but also a critical dependency for national competitiveness, intelligence operations, logistics, cybersecurity, and military decision-making.

In effect, the AI industry has unintentionally created the ultimate single point of failure.

As AI becomes increasingly essential to economic productivity and national defense, these centralized facilities naturally evolve from commercial assets into strategic targets. Their importance guarantees that adversaries will study them, map them, probe them, and eventually develop methods to disrupt or destroy them. The more valuable these AI fortresses become, the more irresistible they become as targets during geopolitical conflict.

This reality formed the basis of a previous argument that the AI data centers of 2030 may ultimately require sovereign-level protection — potentially functioning more like hardened military installations than traditional commercial real estate. Once AI infrastructure becomes critical to national security, protecting it may no longer be optional.

But militarizing data centers only treats the symptom, not the disease.

Building bigger walls around centralized AI infrastructure may delay catastrophe, but it does not eliminate the underlying strategic vulnerability. A fortress is still a fortress. It still has a location. It still has supply lines. It still has power dependencies. And most importantly, it still presents adversaries with a concentrated target whose destruction could create disproportionate economic and military disruption.

Modern warfare is increasingly demonstrating that concentration itself is becoming obsolete.

The emerging lesson from contemporary conflict is that large, static, centralized assets are becoming dangerously vulnerable in an era of cheap autonomous systems, distributed attacks, cyber-physical warfare, and AI-enabled targeting. Resilience no longer comes from concentrating strength behind thicker walls. Resilience comes from distribution, redundancy, mobility, and the elimination of obvious centers of gravity.

The future of AI infrastructure may therefore require a fundamental architectural shift — away from the “Fortress” model and toward something far more decentralized and resilient.

Instead of concentrating compute into a handful of hyperscale compounds, the smarter long-term strategy may be to distribute AI capability across millions of interconnected nodes embedded throughout society itself. Homes, businesses, vehicles, factories, and local energy systems could collectively form a resilient national AI fabric that is vastly harder to disrupt because it has no singular brain to destroy.

In other words, the ultimate defense against the vulnerabilities of centralized AI infrastructure may not be better fortifications at all.

It may be the elimination of the fortress entirely.

II. Lessons from the Front: Operation Spiderweb and the Death of “Large & Static”

For decades, military doctrine revolved around concentration of force. Nations projected power by building larger air bases, larger aircraft carriers, larger command centers, and larger logistical hubs. Strategic advantage often came from assembling overwhelming capability in centralized locations that could be defended through scale, distance, and hardened infrastructure.

But modern warfare is beginning to expose a dangerous flaw in that logic.

Ukraine’s Operation Spiderweb offered a glimpse into the future of asymmetric conflict — and a warning for anyone investing heavily in centralized AI infrastructure. In the operation, relatively inexpensive drones launched from concealed shipping containers reportedly destroyed or severely damaged billions of dollars of Russian military hardware. The attack demonstrated how low-cost autonomous systems can bypass traditional defensive assumptions and threaten even heavily protected strategic assets.

The significance of the operation was not merely tactical. It was architectural.

A modern military aircraft may cost tens or even hundreds of millions of dollars to build, maintain, and defend. Yet those investments can now be threatened by autonomous systems costing a tiny fraction of the target’s value. This is the new asymmetry of modern conflict: increasingly cheap offensive capabilities versus increasingly expensive centralized assets.

The implications extend far beyond the battlefield.

Hyperscale AI data centers are emerging as the civilian equivalent of concentrated military infrastructure. A single AI campus may contain billions of dollars worth of GPUs, networking equipment, transformers, cooling systems, and backup power infrastructure concentrated within a relatively small geographic footprint. These facilities consume enormous amounts of electricity, require extensive water access, and depend on stable transportation and communication links.

In strategic terms, they are ideal targets.

Even if protected by advanced cybersecurity systems, physical security barriers, and military-grade defenses, the economics of attack versus defense are increasingly unfavorable. A nation may spend tens of billions hardening an AI fortress, while adversaries invest comparatively little developing autonomous drones, cyber-physical sabotage systems, electromagnetic disruption tools, or attacks against supporting infrastructure such as substations and fiber routes.

The uncomfortable reality is that static concentration itself is becoming the vulnerability.

This same lesson is already reshaping military thinking. Around the world, defense planners are reconsidering centralized command structures, massive forward operating bases, and tightly clustered logistics hubs. The future military is likely to become more distributed, more mobile, and more redundant — relying on decentralized command systems, autonomous coordination, modular logistics, and dispersed operational assets that can continue functioning even when individual nodes are destroyed.

AI infrastructure must evolve the same way.

If artificial intelligence becomes the backbone of economic productivity, national security, industrial automation, cybersecurity, healthcare, transportation, and military operations, then centralized AI compute becomes too strategically important to remain concentrated in a handful of giant facilities. The more essential AI becomes, the more dangerous centralization becomes.

The lesson of Operation Spiderweb is not simply that drones are dangerous.

The deeper lesson is that resilient systems survive by distributing critical capability across wide networks rather than concentrating it into singular targets. A decentralized system may lose individual nodes without catastrophic failure. A centralized system risks collapse if its core infrastructure is compromised.

In the emerging era of autonomous conflict, resilience increasingly belongs to the distributed.

III. The Social & Political Bottleneck: The Rise of the “NIMBY” Data Center

Even if centralized AI mega-campuses could somehow be fully protected from military and cyber threats, they still face another growing obstacle that may ultimately prove just as limiting: public opposition.

Across the United States and around the world, communities are increasingly resisting the construction of massive data centers in their neighborhoods. What was once viewed as relatively harmless digital infrastructure is now being recognized as an enormous industrial footprint with significant demands on land, water, electricity, and local infrastructure.

Residents are beginning to ask uncomfortable questions.

Why should local communities absorb rising utility costs, water consumption concerns, constant construction traffic, backup generator noise, and visual blight so that a handful of technology companies can consolidate AI power? Why should neighborhoods sacrifice scarce electrical capacity for facilities that may create relatively few permanent local jobs compared to their physical scale and resource consumption?

As AI adoption accelerates, these tensions are likely to intensify rather than diminish.

The scale of future AI infrastructure requirements is staggering. Advanced AI models require immense amounts of compute power, and every new generation of models appears to demand exponentially more energy and hardware than the last. Entire regions are already experiencing concerns about grid strain, water availability, permitting delays, and environmental impact as hyperscale facilities compete for resources with local populations.

This creates a growing sovereignty conflict between national strategic priorities and local community interests.

From the perspective of national governments, AI infrastructure increasingly resembles critical infrastructure on par with ports, railroads, telecommunications networks, or energy systems. Nations that fail to secure sufficient AI compute capacity may find themselves economically disadvantaged, technologically dependent, or strategically vulnerable.

But from the perspective of local residents, a giant AI campus often appears as an unwanted industrial intrusion that consumes disproportionate resources while providing limited direct community benefit.

The collision between these perspectives could become one of the defining infrastructure battles of the next decade.

Governments may attempt to override local opposition through federal permitting reforms, strategic infrastructure designations, or national security arguments. Technology companies may offer tax incentives, local investments, or infrastructure improvements to secure approval. Yet none of these approaches fundamentally solve the underlying tension created by concentrating massive amounts of AI compute into highly visible facilities.

The more AI infrastructure grows in scale, the harder it becomes to hide its impact.

This is why decentralization may represent not only a strategic advantage, but also a political one. It is partly because of expected increases in opposition to terrestrial AI data centers that Elon Musk and others are advocating for space-based AI data centers. But, even on earth we can solve both for fragility/vulnerability and growing political/social opposition.

Instead of forcing communities to accept gigantic industrial AI campuses, future infrastructure could become embedded into the fabric of everyday life itself. Rather than concentrating compute into enormous fortified compounds, AI processing power could be distributed across homes, apartment buildings, offices, vehicles, factories, and local energy systems.

In this model, AI infrastructure becomes largely invisible.

The electrical grid itself offers an instructive analogy. Most people rarely think about the countless distributed components that collectively generate and manage electrical power. The system works precisely because it is distributed, redundant, and woven into the broader physical environment rather than concentrated into a few singular facilities.

Decentralized AI compute could evolve in much the same way.

Instead of building isolated industrial parks dedicated exclusively to AI, society could gradually transform millions of existing structures into intelligent compute nodes. Homes equipped with solar panels, battery storage, smart electrical systems, and AI acceleration hardware could collectively form a national compute fabric that scales organically alongside everyday infrastructure upgrades.

The strategic benefit is resilience.

The political benefit is acceptance.

Infrastructure people barely notice is often infrastructure they are far more willing to live with.

Distributed AI infrastructure - PulteGroup, Nvidia, and Span

IV. The New Architecture: Residential AI Nodes (The Nvidia-Pulte-Span Model)

The transition from centralized AI fortresses to distributed AI infrastructure may sound futuristic, but early versions of this architecture are already beginning to emerge.

One of the clearest signals came from the 2026 partnership between PulteGroup, Nvidia, and Span — an alliance that hinted at a radically different vision for the future of AI compute. Instead of treating homes solely as passive consumers of electricity and internet services, the partnership pointed toward a future where residential properties themselves become intelligent infrastructure nodes participating in a larger distributed compute network.

At the center of this shift is the growing convergence of three technologies that historically operated independently: AI acceleration hardware, residential energy systems, and intelligent electrical management.

Nvidia provides the AI compute layer through increasingly compact and energy-efficient GPU systems optimized for local inference and edge processing. Span contributes the intelligent electrical infrastructure capable of dynamically managing household energy loads, battery systems, solar generation, and grid interaction. PulteGroup represents the large-scale residential deployment mechanism capable of embedding these systems into new homes at scale.

Together, these technologies begin to transform the modern home into something entirely new: a residential AI node.

This concept fundamentally changes the role homes play within both the energy grid and the digital economy. Traditionally, homes consume electricity, bandwidth, and cloud services while contributing relatively little back into the broader infrastructure ecosystem. But with intelligent power management, local battery storage, rooftop solar generation, and dedicated AI hardware, homes can evolve into active participants in a distributed national compute fabric.

In practical terms, this means millions of homes could collectively provide enormous amounts of distributed AI inference capacity without requiring the construction of massive standalone data centers.

The timing of this shift is important because AI workloads themselves are evolving.

Training frontier AI models will likely continue requiring large-scale centralized infrastructure for the foreseeable future. But inference — the process of actually running AI models to serve applications, automate tasks, power agents, process data, and support real-time decision-making — is increasingly capable of operating on smaller, distributed hardware systems.

That distinction changes everything.

Instead of routing every AI request through hyperscale facilities, future AI ecosystems may distribute inference workloads dynamically across millions of geographically dispersed residential nodes. AI processing could occur closer to the end user, reducing latency, improving resilience, lowering bandwidth costs, and minimizing pressure on centralized infrastructure.

The energy implications are equally significant.

One of the biggest criticisms of hyperscale AI infrastructure is its extraordinary power consumption. Massive data centers require huge dedicated energy resources that often strain local grids and trigger political resistance. Distributed residential AI nodes offer a different model by leveraging energy systems that are already being deployed into homes for broader electrification efforts.

Homes equipped with solar panels and battery packs effectively become micro-energy systems capable of storing and managing local power generation. Smart electrical panels can determine when energy demand is low, when renewable generation is abundant, or when excess electricity would otherwise go unused. During those periods, AI inference workloads could be activated opportunistically across distributed residential infrastructure.

In effect, AI compute becomes partially synchronized with the natural rhythms of the electrical grid.

Instead of building ever-larger centralized facilities that demand constant peak power availability, distributed AI infrastructure could absorb excess off-peak generation, stabilize demand curves, and make more efficient use of existing electrical capacity.

The homeowner incentives could also be compelling.

Just as homeowners today can sell excess solar generation back to the grid, future residential AI systems could potentially generate compute revenue by contributing idle processing power to distributed inference networks. Reduced utility costs, subsidized hardware, lower internet expenses, and participation payments could transform homes from passive infrastructure liabilities into productive digital assets.

This creates a powerful alignment between national strategic interests and individual economic incentives.

Governments gain a far more resilient and geographically distributed AI infrastructure. Technology companies gain scalable edge compute capacity without constructing as many hyperscale facilities. Electrical grids gain flexible demand management capabilities. And homeowners gain direct economic participation in the AI economy itself.

Most importantly, the resulting system becomes dramatically harder to disrupt.

A centralized AI fortress presents adversaries with a concentrated target. A distributed residential AI fabric diffuses compute capability across millions of ordinary structures woven throughout society. What once existed inside a handful of highly visible compounds instead becomes embedded everywhere and nowhere at the same time.

In the emerging era of strategic AI competition, that distinction may prove decisive.

V. Strategic Advantages of the Distributed AI Grid

If centralized AI infrastructure represents a high-value target with concentrated risk, then decentralized AI infrastructure represents the opposite: a system designed around dispersion, redundancy, and continual adaptability. The advantages of this shift are not incremental — they are structural.

The most immediate benefit is what might be called kinetic resilience. In a centralized model, a single facility may represent a critical node whose disruption could degrade national AI capability in a meaningful way. In a distributed model, however, compute is spread across thousands or millions of independent nodes. No single strike, outage, or localized failure can meaningfully degrade the system as a whole. The network simply reroutes, reallocates, and continues operating.

This changes the strategic calculus entirely. Instead of defending a small number of high-value assets at extraordinary cost, resilience is achieved through ubiquity. The system becomes less like a fortress and more like a living ecosystem — continuously adapting to localized disruptions without systemic collapse.

A second advantage is power efficiency and grid stability. Hyperscale data centers often require dedicated energy infrastructure, new transmission lines, and significant upgrades to local grids. They tend to behave like industrial-scale energy sinks, demanding predictable and sustained power delivery at massive scale.

A distributed AI grid behaves differently. By embedding compute capability into residential and commercial environments already connected to the electrical system, AI workloads can be dynamically aligned with existing energy flows rather than forcing entirely new ones.

In practical terms, this enables several efficiencies:

  • Utilization of residential solar generation that would otherwise be unused or exported inefficiently
  • Charging and discharging of home battery systems in coordination with AI workload demand
  • Shifting inference tasks to off-peak hours when grid demand is lower and electricity is cheaper
  • Reducing the need for large new transmission infrastructure dedicated solely to AI growth

Instead of AI competing with other sectors for scarce centralized power capacity, it becomes a flexible participant in a broader distributed energy ecosystem.

A third advantage is latency reduction and proximity to the user. As AI becomes more embedded in daily life — powering assistants, autonomous systems, real-time translation, predictive services, and physical automation — the distance between compute and user begins to matter more.

Distributed inference at the edge of the network enables faster response times, reduced dependency on long-haul network routing, and greater robustness during partial connectivity disruptions. In many cases, AI systems embedded in homes, vehicles, and local infrastructure can respond instantaneously without requiring round trips to distant centralized servers.

Taken together, these advantages suggest that decentralization is not simply a defensive posture against geopolitical risk — it is also an optimization of efficiency, responsiveness, and system-wide adaptability.

Perhaps most importantly, the distributed model reduces systemic fragility at exactly the moment AI systems are becoming more deeply integrated into critical societal functions. The more intelligence we embed into infrastructure, the more dangerous it becomes to concentrate that intelligence into a small number of failure-prone locations.

In this sense, decentralization is not a retreat from progress. It is an evolution toward resilience.

VI. Conclusion: From Fortresses to Fabrics

The trajectory of AI infrastructure is often described as a race toward scale: larger models, larger clusters, larger data centers, and larger investments concentrated into fewer and fewer locations. On the surface, this appears to be the natural endpoint of technological progress — efficiency achieved through consolidation.

But that framing assumes a world where concentration remains an advantage. Increasingly, the opposite may be true.

As AI becomes more deeply embedded in national economies, critical infrastructure, and defense systems, the risks associated with centralization grow in parallel with its capabilities. What once looked like an optimization problem begins to resemble a resilience problem. And resilience, in complex systems, rarely comes from concentration.

The “AI Fortress” model — massive, highly capable, strategically critical data centers protected by layers of physical and digital security — may represent an important transitional phase. It enables rapid scaling of capability at a moment when demand is exploding and architectures are still stabilizing. But it is unlikely to represent the final stable equilibrium.

Over time, the logic of vulnerability, energy distribution, political friction, and technological enablement all converge on a different structure: one that is distributed by default, not by exception.

In that future, AI compute is no longer something that exists “somewhere.” It is something that exists everywhere — embedded into homes, vehicles, factories, grids, and local systems, continuously interacting with the physical world rather than being isolated from it.

This is the shift from fortresses to fabrics.

A fortress is defined by its boundaries: inside is protected, outside is excluded, and value is concentrated at the center. A fabric, by contrast, derives its strength from interconnection. It is resilient not because it is hardened in one place, but because it is woven across many places. Damage to one thread does not collapse the structure; it is absorbed, rerouted, and contained.

A distributed AI fabric would behave in the same way. Compute capacity would be ubiquitous but not centralized, powerful but not singularly fragile, intelligent but not dependent on any single point of control or failure.

In this model, the question is no longer how to protect the brain of the system by enclosing it within ever more secure walls. Instead, the question becomes how to ensure there is no single brain to target in the first place.

That shift has profound strategic implications.

It reframes AI infrastructure from something that must be defended at a few critical locations into something that must be designed as a resilient, adaptive system distributed across society itself. It also aligns national security objectives with individual participation, energy efficiency with compute demand, and technological advancement with infrastructural sustainability.

In an era shaped by asymmetric threats, autonomous systems, and rapidly evolving geopolitical risk, the most robust systems will not be those that concentrate power most effectively, but those that distribute it most intelligently.

The future of AI infrastructure may therefore not be a monument.

It may be a mesh.

And in that shift from fortresses to fabrics lies the real foundation of long-term resilience in the age of artificial intelligence.

FAQ: Decentralized AI Compute and Infrastructure Resilience

FAQ

Why are centralized AI data centers considered vulnerable?
Centralized AI data centers concentrate massive compute, energy, and strategic value into a small number of physical locations. This creates single points of failure that can be targeted by physical attacks, cyber operations, or infrastructure disruptions, potentially causing disproportionate economic and national security impact.

What is meant by a “distributed AI fabric”?
A distributed AI fabric refers to an architecture where AI compute is spread across millions of interconnected nodes such as homes, businesses, and edge devices. Instead of relying on a few large data centers, intelligence is embedded throughout the network, improving resilience, reducing latency, and eliminating critical single points of failure.

How could residential AI nodes support the power grid and economy?
Residential AI nodes can leverage solar power, home battery systems, and off-peak electricity to run AI inference workloads locally. This helps balance grid demand, utilize excess renewable energy, reduce strain on centralized infrastructure, and potentially allow homeowners to participate economically in distributed compute networks.

EDITOR’S NOTE: You should read this article to learn more about Why the AI Data Centers of 2030 Will Be Sovereign Fortresses.

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from ChatGPT and Google Gemini to clean up the article, add images and create infographics.

Image credits: Google Gemini, SPAN (via mortgagepoint.com)

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The Consumption Collapse – When the Feedback Loop Bites Back

Why the Great American Contraction is leading to a crisis of demand and a re-imagining of the American Social Contract.

LAST UPDATED: April 17, 2026 at 3:58 PM

The Consumption Collapse - When the Feedback Loop Bites Back

GUEST POST from Art Inteligencia


The Ghost in the Shopping Mall

In our previous exploration, The Great American Contraction,” we identified a fundamental shift in the American story. For the first time in our history, the foundational assumption of “more” — more people, more labor, and more expansion — has been inverted. We discussed how the exponential rise of AI and robotics is dismantling the traditional value chain of human labor, moving us from a nation of “doers” to a necessary, albeit smaller, elite class of “architects.”

However, as we move closer to the two-year horizon of the next United States Presidential election, a more insidious shadow is beginning to fall across the landscape. It is no longer just a crisis of employment; it has evolved into a crisis of consumption. This is the “Feedback Loop of Irrelevance.”

The logic is as cold as the algorithms driving it: As increasing numbers of knowledge workers and service providers are displaced by autonomous agents, their disposable income evaporates. When people lose their financial footing, they spend less. When they spend less, the revenue of the very companies that automated them begins to shrink. To protect their margins in a declining market, these companies are forced to cut back even further — often doubling down on automation to reduce costs — which in turn removes more consumers from the marketplace.

We are witnessing the birth of a deflationary death spiral where corporate efficiency threatens to cannibalize the very markets it was designed to serve. Over the next 24 months, this cycle will redefine the American psyche and set the stage for an election year unlike any we have ever seen.

It is time to look beyond the immediate shock of job loss and examine the structural integrity of our economic operating system. If the “Old Equation” of labor-for-income is a sinking ship, we must decide what happens to the passengers before we reach the horizon of 2028.

The Vicious Cycle of Automated Austerity

The transition from a growth-based economy to a Great Contraction is not a linear event; it is a recursive loop. As AI adoption accelerates, we are witnessing a phenomenon I call “Automated Austerity.” This is the process where short-term corporate gains from labor reduction lead directly to long-term market erosion. The cycle progresses through four distinct, overlapping phases:

Phase 1: The First Wave Displacement

We are currently seeing the replacement of both low-skilled physical labor and high-skilled knowledge work by autonomous systems. This isn’t just about factory floors; it’s about the “Architect” roles we once thought were safe. As companies replace $150k-a-year analysts with $15-a-month compute tokens, the immediate impact is a massive surge in corporate profit margins.

Phase 2: The Wallet Effect

The friction begins here. Displaced workers initially rely on savings or severance, but as those dry up, the “gig economy” safety net is nowhere to be found — because AI is already performing the freelance writing, coding, and administrative tasks that used to provide a bridge. Disposable income doesn’t just dip; for a significant percentage of the population, it vanishes. This causes a sharp contraction in discretionary spending.

Phase 3: The Revenue Mirage

This is the trap. Companies that automated to save money suddenly find their top-line revenue shrinking because their customers (the former workers) can no longer afford their products. The efficiency gains are real, but the market size is artificial. We are entering a period where companies may be 100% efficient at producing goods that 0% of the displaced population can buy.

Phase 4: The Secondary Contraction

Faced with shrinking revenues, boards of directors demand even deeper cost-cutting to protect investor dividends. This leads to a second, more desperate wave of layoffs, further reducing the tax base and consumer spending power. This feedback loop creates a Deflationary Death Spiral that traditional monetary policy is ill-equipped to handle.

“When you automate the consumer out of a job, you eventually automate the business out of a customer.” — Braden Kelley

Over the next two years, this cycle will move from the periphery of Silicon Valley to the heart of every American household, forcing a radical re-evaluation of how we distribute the abundance that AI creates.

Vicious Cycle of Automated Austerity

The Two-Year Horizon: 2026–2028

As we navigate the next twenty-four months, the gap between traditional economic indicators and the lived reality of American citizens will become a canyon. We are entering a period of Economic Bifurcation, where the distance between those who own the “compute” and those who formerly provided the “labor” creates a new social stratification.

The Rise of the ‘Hollow’ Recovery

Expect to hear the term “efficiency-led growth” frequently in the coming months. Wall Street may remain buoyant as AI-integrated corporations report record-breaking margins per employee. However, this is a hollow success. While the stock market reflects corporate optimization, our Alternative Economic Health Measures—like the Genuine Progress Indicator (GPI) — will likely show a steep decline. We are becoming a nation that is technically “wealthier” while the average citizen’s ability to participate in that wealth is structurally dismantled.

The Shift from ‘Doer’ to ‘Architect’ Burnout

The “Great American Contraction” is not just about those losing roles; it is about the immense pressure on those who remain. The survivors — the Architect Class — are tasked with managing sprawling AI ecosystems. This creates a new kind of cognitive load. By 2027, I predict we will see a peak in “Technological Burnout,” where the speed of AI-driven change outpaces the human capacity to design for it. This is where Human-Centered Innovation becomes a survival skill rather than a corporate luxury.

The Mindset of Survivalist Innovation

As the feedback loop of shrinking revenue intensifies, we will see American citizens taking radical actions to decouple from a failing labor market. This includes:

  • Hyper-Localization: A resurgence in local bartering and community-based resource sharing as a hedge against the volatility of the automated economy.
  • The ‘Off-Grid’ Digital Economy: Individuals utilizing open-source AI models to create value outside of the traditional corporate gatekeepers, leading to a “shadow economy” of peer-to-peer services.
  • Consumption Sabotage: A psychological shift where citizens, feeling irrelevant to the economy, consciously reduce their consumption to the bare essentials, further accelerating the contraction.

This period will be defined by a search for meaning in a post-labor world. The American citizen of 2027 is no longer asking “How do I get ahead?” but rather “How do I remain relevant in a world that no longer requires my effort to function?”

The Survivalist Innovation Framework

Beyond GDP: New Vitals for a Contracting Economy

As the “Old Equation” fails, the metrics we use to measure national success are becoming dangerously obsolete. In a world where AI can drive productivity while simultaneously hollowing out the consumer class, GDP is no longer a compass; it is a rearview mirror. To navigate the next two years, we must shift our focus to alternative economic health measures that prioritize human vitality over transactional velocity.

1. The Genuine Progress Indicator (GPI)

Unlike GDP, which counts the “cost of cleaning up a disaster” as a positive, the GPI factors in income inequality and the social costs of underemployment. As we move toward 2028, we must demand a GPI-centered view of the economy. If AI-driven efficiency creates wealth but destroys the social capital of our communities, the GPI will show we are regressing, providing a much-needed reality check to “hollow” stock market gains.

2. The U-7 ‘Utility’ Rate

Standard unemployment figures (U-3) are increasingly irrelevant. We need a U-7 ‘Utility’ Rate to track those who are “technologically displaced”—individuals whose roles have been absorbed by algorithms or whose wages have been suppressed to the point of working poverty. This metric will highlight the Architect Gap: the growing number of people who have the capacity for high-value human contribution but lack access to the compute resources required to compete.

3. The Social Progress Index (SPI)

The goal of an automated economy should be to improve the human condition. The SPI measures outcomes that actually matter: Access to advanced education, personal freedom, and environmental quality. By 2027, the SPI will be the most honest indicator of whether the Great Contraction is a managed transition to a better life or a chaotic collapse of the middle class.

4. Value of Organizational Learning Technologies (VOLT)

We must begin measuring the “Agility Score” of our nation. VOLT measures how effectively we are using AI to solve complex problems rather than just replacing workers. A high VOLT score paired with a low SPI suggests we are building a “learning machine” that has forgotten its purpose: to serve the humans who created it.

“A high-GDP nation with a crashing Social Progress Index(SPI) is merely a failed state in a gold tuxedo.”

The political battleground of the next two years will be defined by a new set of metrics similar to these (but likely different). The 2028 election will not just be a choice between candidates, but a choice between maintaining the illusion of growth or designing a system of sovereignty for the American citizen.

The Localized Pivot

The Sovereign Tech-Stack & The Localized Pivot

As the “Feedback Loop of Irrelevance” continues to shrink traditional income, we are witnessing a radical grassroots response: The Localized Pivot. When the macro-economy fails to provide value to the individual, the individual stops providing value to the macro-economy and turns inward to their community.

The Rise of the ‘Personal AI’ Infrastructure

By 2027, the barrier to entry for sophisticated production will vanish. We will see a surge in “Sovereign Tech-Stacks” — individuals and small collectives using localized, open-source AI models to run micro-manufactories, automated vertical farms, and peer-to-peer service networks. This is Innovation as a Survival Tactic. These citizens are essentially “unplugging” from the hollowed-out corporate ecosystem and creating a shadow economy that traditional GDP cannot track.

From Global Chains to Hyper-Local Resilience

The contraction of consumer spending will lead to the death of the “long supply chain” for many goods. In its place, we will see the rise of Regional Circular Economies. AI will be used not to maximize global profit, but to optimize local resource sharing. Imagine community AI agents that manage local energy grids or coordinate the bartering of skills — human-centered design at its most fundamental level.

The ‘Architect’ of the Commons

In this phase, the “Architect” role I’ve discussed previously becomes a civic one. These are the individuals who design the systems that keep their communities thriving while the national revenue shrinks. They are the ones building the Human-Centered Guardrails that ensure technology serves the neighborhood, not the shareholder. This shift represents a move from Global Consumerism to Local Sovereignty.

“When the national economic engine stops fueling the household, the household must build its own engine, or it dies.” — Braden Kelley

This localized movement will be the wild card of 2028. It creates a class of “Un-Architected” citizens who are no longer dependent on the federal government or major corporations, creating a profound tension for any political candidate trying to promise a return to the ‘Old Equation’.

The Road to 2028: The Politics of Human Relevance

As we approach the next Presidential election, the political discourse will undergo a seismic shift. The traditional “Left vs. Right” battle lines over tax rates and social issues will be superseded by a more existential debate: The Individual vs. The Algorithm. The 2028 election will likely be the first in history centered entirely on the consequences of a post-labor economy.

The ‘Humanity First’ Tax and Sovereign Solvency

The most contentious issue will be how to fund a shrinking state as the labor-based tax system collapses. We will see the rise of the “Compute Tax” — a proposal to tax AI tokens and robotic output rather than human hours. This isn’t just about revenue; it’s about sovereign solvency. When companies reinvest profits into compute rather than wages, the “Economic OS” crashes. Expect candidates to run on a platform of Universal Basic Everything (UBE) — providing the results of automation (healthcare, housing, and energy) directly to the people as the tax base from labor vanishes.

The Compute Tax

The Death of Traditional Immigration Debates

As I noted in our initial look at the Contraction, the old argument about immigrants “taking jobs” or “filling gaps” is dead. In 2028, the focus will shift to “Strategic Talent Acquisition.” The debate will center on how to attract the world’s few remaining irreplaceable “Architect” minds while managing a domestic population that is increasingly surplus to the needs of capital. This will create a strange political alliance between protectionists and humanists, both seeking to shield human value from digital devaluation.

Mindset and Likely Actions of the Citizenry

By the time voters head to the polls, the American mindset will have shifted from aspiration to preservation. We are likely to see:

  • The Rise of ‘Neo-Luddite’ Activism: Not a rejection of technology, but a demand for “Human-Centered Guardrails” that prevent AI from cannibalizing the last remaining sectors of human connection.
  • The Search for Non-Monetary Meaning: A surge in candidates who focus on “Quality of Life” metrics rather than fiscal growth, appealing to a class of people who no longer derive their identity from their “job.”
  • Algorithmic Populism: Politicians using AI to personalize fear and hope at scale, creating a feedback loop where the technology used to displace the worker is also used to win their vote.

The central question of the 2028 election will be simple but devastating: “What is a country for, if not to support the thriving of its people — even when those people are no longer ‘productive’ in a traditional sense?” The winner will be the one who can design a new social contract for a smaller, more resilient, and truly innovative nation.

Conclusion: Designing a Thrivable Contraction

The Great American Contraction is no longer a theoretical “what-if” for futurists to debate; it is an active restructuring of our reality. As the feedback loop of automated austerity begins to bite, we are discovering that a country built on the relentless pursuit of “more” is fundamentally ill-equipped to handle the arrival of “enough.”

The next two years will be a period of intense friction as our legacy systems — our tax codes, our education models, and our social safety nets — grind against the frictionless efficiency of the AI era. We will see traditional economic metrics fail to capture the quiet struggle of the consumer, and we will watch as the 2028 election turns into a referendum on the value of a human being in a post-labor world.

But contraction does not have to mean collapse. If we shift our focus from transactional velocity to human vitality, we have the opportunity to design a new version of the American Dream. This new dream isn’t about the quantity of jobs we can protect from the machines, but the quality of the lives we can build with the abundance those machines create. It is about moving from a nation of “doers” who are exhausted by the grind to a nation of “architects” who are inspired by the possible.

“The goal of innovation was never to replace the human; it was to release the human. We are finally being forced to decide what we want to be released to do.” — Braden Kelley

The road to 2028 will be defined by whether we choose to cling to the wreckage of the growth-based model or whether we have the courage to embrace a smaller, smarter, and more human-centered future. The contraction is inevitable, but the outcome is ours to design.

STAY TUNED: On Tuesday my friend Braden Kelley (with a little help from me) is publishing an article featuring one hypothesis for what an AI SOFT LANDING might look like.

Image credits: Google Gemini

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Win Your Way to an AI Job

Anduril’s AI Grand Prix: Racing for the Future of Work

LAST UPDATED: January 28, 2026 at 2:27 PM

Anduril's AI Grand Prix: Racing for the Future of Work

GUEST POST from Art Inteligencia

The traditional job interview is an antiquated artifact, a relic of a bygone industrial era. It often measures conformity, articulateness, and cultural fit more than actual capability or innovative potential. As we navigate the complexities of AI, automation, and rapid technological shifts, organizations are beginning to realize that to find truly exceptional talent, they need to look beyond resumes and carefully crafted answers. This is where companies like Anduril are not just iterating but innovating the very hiring process itself.

Anduril, a defense technology company known for its focus on AI-driven systems, recently announced its AI Grand Prix — a drone racing contest where the ultimate prize isn’t just glory, but a job offer. This isn’t merely a marketing gimmick; it’s a profound statement about their belief in demonstrated skill over credentialism, and a powerful strategy for identifying talent that can truly push the boundaries of autonomous systems. It epitomizes the shift from abstract evaluation to purposeful, real-world application, emphasizing hands-on capability over theoretical knowledge.

“The future of hiring isn’t about asking people what they can do; it’s about giving them a challenge and watching them show you.”

— Braden Kelley

Why Challenge-Based Hiring is the New Frontier

This approach addresses several critical pain points in traditional hiring:

  • Uncovering Latent Talent: Many brilliant minds don’t fit the mold of elite university degrees or polished corporate careers. Challenge-based hiring can surface individuals with raw, untapped potential who might otherwise be overlooked.
  • Assessing Practical Skills: In fields like AI, robotics, and advanced engineering, theoretical knowledge is insufficient. The ability to problem-solve under pressure, adapt to dynamic environments, and debug complex systems is paramount.
  • Cultural Alignment Through Action: Observing how candidates collaborate, manage stress, and iterate on solutions in a competitive yet supportive environment reveals more about their true cultural fit than any behavioral interview.
  • Building a Diverse Pipeline: By opening up contests to a wider audience, companies can bypass traditional biases inherent in resume screening, leading to a more diverse and innovative workforce.

Beyond Anduril: Other Pioneers of Performance-Based Hiring

Anduril isn’t alone in recognizing the power of real-world challenges to identify top talent. Several other forward-thinking organizations have adopted similar, albeit varied, approaches:

Google’s Code Jam and Hash Code

For years, Google has leveraged competitive programming contests like Code Jam and Hash Code to scout for software engineering talent globally. These contests present participants with complex algorithmic problems that test their coding speed, efficiency, and problem-solving abilities. While not always directly leading to a job offer for every participant, top performers are often fast-tracked through the interview process. This allows Google to identify engineers who can perform under pressure and think creatively, rather than just those who can ace a whiteboard interview. It’s a prime example of turning abstract coding prowess into a tangible demonstration of value.

Kaggle Competitions for Data Scientists

Kaggle, now a Google subsidiary, revolutionized how data scientists prove their worth. Through its platform, companies post real-world data science problems—from predicting housing prices to identifying medical conditions from images—and offer prize money, and often, connections to jobs, to the teams that develop the best models. This creates a meritocracy where the quality of one’s predictive model speaks louder than any resume. Many leading data scientists have launched their careers or been recruited directly from their performance in Kaggle competitions. It transforms theoretical data knowledge into demonstrable insights that directly impact business outcomes.

The Human Element in the Machine Age

What makes these initiatives truly human-centered? It’s the recognition that while AI and automation are transforming tasks, the human capacity for ingenuity, adaptation, and critical thinking remains irreplaceable. These contests aren’t about finding people who can simply operate machines; they’re about finding individuals who can teach the machines, design the next generation of algorithms, and solve problems that don’t yet exist. They foster an environment of continuous learning and application, perfectly aligning with the “purposeful learning” philosophy.

The Anduril AI Grand Prix, much like Google’s and Kaggle’s initiatives, de-risks the hiring process by creating a performance crucible. It’s a pragmatic, meritocratic, and ultimately more effective way to build the teams that will define the next era of technological advancement. As leaders, our challenge is to move beyond conventional wisdom and embrace these innovative models, ensuring we’re not just ready for the future of work, but actively shaping it.

Anduril Fury


Frequently Asked Questions

What is challenge-based hiring?

Challenge-based hiring is a recruitment strategy where candidates demonstrate their skills and problem-solving abilities by completing a real-world task, project, or competition, rather than relying solely on resumes and interviews.

What are the benefits of this approach for companies?

Companies can uncover hidden talent, assess practical skills, observe cultural fit in action, and build a more diverse talent pipeline by focusing on demonstrable performance.

How does this approach benefit candidates?

Candidates get a fair chance to showcase their true abilities regardless of traditional credentials, gain valuable experience, and often get direct access to influential companies and potential job offers based purely on merit.

To learn more about transforming your organization’s talent acquisition strategy, reach out to explore how human-centered innovation can reshape your hiring practices.

Image credits: Wikimedia Commons, Google Gemini

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A New Era of Economic Warfare Arrives

Is Your Company Prepared?

LAST UPDATED: January 9, 2026 at 3:55PM

A New Era of Economic Warfare Arrives

GUEST POST from Art Inteligencia

Economic warfare rarely announces itself. It embeds quietly into systems designed for trust, openness, and speed. By the time damage becomes visible, advantage has already shifted.

This new era of conflict is not defined by tanks or tariffs alone, but by the strategic exploitation of interdependence — where innovation ecosystems, supply chains, data flows, and cultural platforms become contested terrain.

The most effective economic attacks do not destroy systems outright. They drain them slowly enough to avoid response.

Weaponizing Openness

For decades, the United States has benefited from a research and innovation model grounded in openness, collaboration, and academic freedom. Those same qualities, however, have been repeatedly exploited.

Publicly documented prosecutions, investigations, and corporate disclosures describe coordinated efforts to extract intellectual property from American universities, national laboratories, and private companies through undisclosed affiliations, parallel research pipelines, and cyber-enabled theft.

This is not opportunistic theft. It is strategic harvesting.

When innovation can be copied faster than it can be created, openness becomes a liability instead of a strength.

Cyber Persistence as Economic Strategy

Cyber operations today prioritize persistence over spectacle. Continuous access to sensitive systems allows competitors to shortcut development cycles, underprice rivals, and anticipate strategic moves.

The goal is not disruption — it is advantage.

Skydio and Supply Chain Chokepoints

The experience of American drone manufacturer Skydio illustrates how economic pressure can be applied without direct confrontation.

After achieving leadership through autonomy and software-driven innovation rather than low-cost manufacturing, Skydio encountered pressure through access constraints tied to upstream supply chains.

This was a calculated attack on a successful American business. It serves as a stark reminder: if you depend on a potential adversary for your components, your success is only permitted as long as it doesn’t challenge their dominance. We must decouple our innovation from external control, or we will remain permanently vulnerable.

When supply chains are weaponized, markets no longer reward the best ideas — only the most protected ones.

Agricultural and Biological Vulnerabilities

Incidents involving the unauthorized movement of biological materials related to agriculture and bioscience highlight a critical blind spot. Food systems are economic infrastructure.

Crop blight, livestock disease, and agricultural disruption do not need to be dramatic to be devastating. They only need to be targeted, deniable, and difficult to attribute.

Pandemics and Systemic Shock

The origins of COVID-19 remain contested, with investigations examining both natural spillover and laboratory-associated scenarios. From an economic warfare perspective, attribution matters less than exposure.

The pandemic revealed how research opacity, delayed disclosure, and global interdependence can cascade into economic devastation on a scale rivaling major wars.

Resilience must be designed for uncertainty, not certainty.

The Attention Economy as Strategic Terrain and Algorithmic Narcotic

Platforms such as TikTok represent a new form of economic influence: large-scale behavioral shaping.

Regulatory and academic concerns focus on data governance, algorithmic amplification, and the psychological impact on youth attention, agency, and civic engagement.

TikTok is not just a social media app; it is a cognitive weapon. In China, the algorithm pushes “Douyin” users toward educational content, engineering, and national achievement. In America, the algorithm pushes our youth toward mindless consumption, social fragmentation, and addictive cycles that weaken the mental resilience of the next generation. This is an intentional weakening of our human capital. By controlling the narrative and the attention of 170 million Americans, American children are part of a massive experiment in psychological warfare, designed to ensure that the next generation of Americans is too distracted to lead and too divided to innovate.

Whether intentional or emergent, influence over attention increasingly translates into long-term economic leverage.

The Human Cost of Invisible Conflict

Economic warfare succeeds because its consequences unfold slowly: hollowed industries, lost startups, diminished trust, and weakened social cohesion.

True resilience is not built by reacting to attacks, but by redesigning systems so exploitation becomes expensive and contribution becomes the easiest path forward.

Conclusion

This is not a call for isolation or paranoia. It is a call for strategic maturity.

Openness without safeguards is not virtue — it is exposure. Innovation without resilience is not leadership — it is extraction.

The era of complacency must end. We must treat economic security as national security. This means securing our universities, diversifying our supply chains, and demanding transparency in our digital and biological interactions. We have the power to stoke our own innovation bonfire, but only if we are willing to protect it from those who wish to extinguish it.

The next era of competition will reward nations and companies that design systems where trust is earned, reciprocity is enforced, and long-term value creation is protected.

Frequently Asked Questions

What is economic warfare?

Economic warfare refers to the use of non-military tools — such as intellectual property extraction, cyber operations, supply chain control, and influence platforms — to weaken a rival’s economic position and long-term competitiveness.

Is China the only country using these tactics?

No. Many nations engage in forms of economic competition that blur into coercion. The concern highlighted here is about scale, coordination, and the systematic exploitation of open systems.

How should the United States respond?

By strengthening resilience rather than retreating from openness — protecting critical research, diversifying supply chains, aligning innovation policy with national strategy, and designing systems that reward contribution over extraction.

How should your company protect itself?

Companies should identify their critical knowledge assets, limit unnecessary exposure, diversify suppliers, strengthen cybersecurity, enforce disclosure and governance standards, and design partnerships that balance collaboration with protection. Resilience should be treated as a strategic capability, not a compliance exercise.

Image credits: Google Gemini

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Addressing the Veteran Mental Health Crisis

A New Frontier in Healing for Memorial Day Weekend

Addressing the Veteran Mental Health Crisis

by Braden Kelley and Art Inteligencia

As a nation, we have an enduring obligation to the brave individuals who have served in our military. On this Memorial Day weekend, while we honor their sacrifice, we must also look toward a future where we care for the psychological wounds of war. One of the greatest challenges we face is the veteran mental health crisis, with high rates of PTSD, depression, and suicide. Emerging research suggests that psychedelic treatments could significantly alleviate these conditions, providing a new pathway to healing that we cannot afford to ignore.

Understanding the Crisis

The statistics are alarming. According to the Department of Veterans Affairs (VA), approximately 17 veterans die by suicide every day. Furthermore, the VA estimates that around 15% of Vietnam veterans, 12% of Gulf War veterans, and 11-20% of veterans who served in Operations Iraqi Freedom and Enduring Freedom suffer from PTSD in a given year. Traditional treatments like psychotherapy and pharmacotherapy have proven beneficial for some, but many veterans experience symptoms that persist despite these interventions.

The Promise of Psychedelics

In recent years, researchers have turned their attention to the therapeutic potential of psychedelic substances such as MDMA, psilocybin, and LSD. These substances are showing promise in treating PTSD, depression, and other mental health issues. A landmark study conducted by the Multidisciplinary Association for Psychedelic Studies (MAPS) in collaboration with the VA found that 67% of participants treated with MDMA-assisted therapy no longer met the diagnostic criteria for PTSD after three sessions. This is a groundbreaking finding that cannot be ignored.

Similarly, psilocybin, the active compound in “magic mushrooms,” has shown potential in alleviating depression and anxiety symptoms in numerous studies. A study from Johns Hopkins Medicine demonstrated that psilocybin-assisted therapy resulted in rapid and sustained reductions in depression severity, with effects lasting for weeks and even months. The therapeutic mechanisms of psychedelics, which include altering neural network connectivity and promoting emotional processing, offer a new realm of possibilities for treatment.

Legal and Regulatory Challenges

Despite promising results, the legal status of these substances remains a significant barrier. Classified as Schedule I substances under the Controlled Substances Act, they are currently deemed to have “no accepted medical use.” However, as the evidence base strengthens, there is growing momentum for reevaluating this classification. States like Oregon and cities such as Denver have decriminalized psilocybin, paving the way for broader acceptance and access.

Building a Comprehensive Support System

To address the veteran mental health crisis effectively, we must take a multi-faceted approach:

  1. Policy Revision and Advocacy: It is crucial for policymakers to prioritize the revision of regulations surrounding psychedelics. We need comprehensive legislative efforts to reclassify these substances, allowing for more extensive research and greater accessibility.
  2. Research and Training: Increased funding for research into psychedelic-assisted therapies is essential. Universities, independent research organizations, and the VA should collaborate to expand clinical trials. Alongside research, training programs for mental health professionals must be developed to ensure they are well-equipped to provide these treatments safely and effectively.
  3. Education and Awareness: Public awareness campaigns can help destigmatize mental health and psychedelic treatments. Stories of healing and recovery should be shared, and educational resources must be made available to veterans, their families, and the general public.
  4. Holistic Care Models: Veteran care must incorporate holistic and integrative approaches, including mindfulness, nutrition, and community support, alongside psychedelic treatments. These support systems are vital for sustaining mental health and can multiply the therapeutic effects of psychedelics.
  5. Veteran-Centric Programs: Programs tailored specifically to veterans’ unique experiences and needs should be developed. Peer support systems, where veterans can share their experiences and support one another through healing, can enhance recovery outcomes.

The Role of Community

Community plays a pivotal role in healing. As a nation, we must foster environments that not only support veterans but actively engage them in the healing process. Community centers focused on veteran well-being, alongside integration programs that help veterans transition back into civilian life with purpose and support, can be transformative.

The Moral Imperative

As we commemorate Memorial Day, we must also reflect on our moral duty to those who have served. The veteran mental health crisis is a call to action—an opportunity not only to acknowledge the sacrifices of our military personnel but to invest in their healing and well-being. Psychedelic treatments represent a beacon of hope, backed by rigorous science and positive outcomes. It is essential for us to come together as a society, to push for changes that reflect our commitment to caring for veterans in the most effective and compassionate ways possible.

Conclusion

The journey to mental health recovery for veterans is not an easy one, but it is a journey we must undertake collectively. By embracing innovation and fostering an environment of openness and support, we can lead the way in addressing the mental health crisis that afflicts our veterans. The time to act is now. With courage, compassion, and collaboration, we can chart a course toward healing and honor the legacy of those who have served with dignity and responsibility.

In the spirit of unity and progress, let us stand together to advocate for effective solutions and a brighter future for all veterans. Their healing is our mission. Let us not falter in this duty.


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Image Credit: Microsoft CoPilot

Content Authenticity Statement: Most of the paragraphs in the article were created with the help of OpenAI Playground.

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China’s Disastrous One Child Policy

Unraveling the Unintended Consequences

China's Disastrous One Child Policy

GUEST POST from Robert B. Tucker

In 1979, China’s leaders implemented the now-infamous “One Child Policy.” Designed to curb population growth, the policy succeeded in reducing birth rates almost immediately. But it also unleashed a cascade of severe and unintended social consequences that the nation is still trying to untangle.

Because many Chinese couples favored boys over girls, the One Child policy began to skew the gender ratio. It gave rise to the so-called “little emperor” syndrome among only children. Most significantly, a birth dearth gave rise to a rapidly aging population.

Today, that aging population poses a long-term crisis threatening to upend China’s economic momentum. With a shrinking workforce and fewer young workers, productivity has declined as soaring healthcare and pension costs strain national resources.

Decades of restricting birth have created a demographic imbalance. Fewer caregivers are available to support a growing elderly population. Once a driver of China’s growth, consumer spending is shifting away from housing, education, and discretionary goods. Industries across the board are feeling the squeeze, while the burden on younger generations grows ever heavier.

China is scrambling to undo the decision: raising the retirement age, pushing automation in fields and factories, and offering incentives for couples to have more children. But the results have been underwhelming. Reversing the unintended consequences of that single 1979 policy decision has been anything but easy.

Governmental responses include birth subsidies, stronger maternity and paternity leave, and numerous efforts to bolster workplace protections for women. No matter how creatively or emphatically the government promotes fertility, young Chinese couples are simply not making more babies.

Result: China stands to lose five to ten million working-age adults each year, while gaining an equal number of elderly people.

In researching a new book on decision-making in an uncertain world, I frequently encounter unintended consequences. The Trump administration’s recent imposition of across-the-board tariffs is an example. The announcement of these controversially named “reciprocal tariffs” prompted retaliation from trade partners and immediately triggered a stock market crash. The aggressive U.S. tariff policy will trigger a significant slowdown in the U.S. economy this year and next, with the median probability of recession in the next 12 months approaching 50 percent, according to economists polled by Reuters.

At the time, China’s One-Child Policy seemed like a no-brainer, a logical response to burgeoning, unsustainable population growth. But its long-term impacts on culture, economics, and national competitiveness were profoundly underestimated.

Key point: When making decisions of significant impact, consider what you want to happen and if your plan will bring this desired state into being. But consider also what might unfold if your plan doesn’t work — and if your plan works all too well. The payoff from taking the extra time will be worth it. Just ask China.

This article originally appeared in Forbes

Image credit: Pexels

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Six Key Lessons From COVID-19

Six Key Lessons From COVID-19

GUEST POST from Robert B. Tucker

During the fall of 2019, in a lab in Wuhan, China, a cluster of atoms weighing less than one-trillionth of a gram mutated ever so slightly, cascading into the greatest disruption to human life in over a century. COVID-19 would go on to the lives of over 20 million people worldwide and over a million in the United States.

In a matter of months, the coronavirus had reshaped our world. It forced us indoors, upended economies, and brought suffering and loss to millions. It exposed cracks in our systems and magnified existing inequalities. And it tested the leadership of institutions, governments, and businesses.

Yet amidst the upheaval, it also accelerated innovation in vaccine development, proved the potential of global collaboration, and offered valuable lessons—lessons we dare not ignore.

Five years on, with the benefit of hindsight, what lessons did we learn from the Covid Crisis? What are the takeaways? What ideas can we carry forward? And how can we better prepare for next time? Here are six enduring lessons that the pandemic offers:

1. COVID-19 United Us at First But Divided Us at last.

According to a recent Pew survey, seventy-two percent of Americans believe COVID-19 did more to drive the country apart than to bring it together. Trust in government plummeted to a new low. In Covid’s Wake: How Our Politics Failed Us, a new book that reviews the crisis, concludes that the scientific community overestimated the dangers of the virus and stifled dissenting scientific opinion. Models were designed solely to reduce deaths, failing to include other criteria, such as the effects of social isolation on children’s mental health. Locking down at the pandemic’s start may have been necessary, say these authors, but continuing the lockdowns for so long created lasting hardship and divisions.

Key takeaway: In a politically polarized era, one-size-fits-all health mandates from the National Institute of Health must be avoided. Public trust must be maintained, and local control ensured.

2. Resilience Is No Longer a Luxury—It’s a Necessity.

When COVID-19 struck, organizations and individuals who demonstrated resilience – did best. They exhibited the ability to keep calm, remain flexible, and adapt readily, and weathered the storm far better than those who went into denial mode or dismissed COVID-19 as a hoax or government conspiracy. When supply chains buckled, when health officials enforced lockdowns, organizations that had invested in contingency planning and crisis management demonstrated resilience and staying power.

Key takeaway: Leaders who encouraged experimentation found the path forward. Those that did not floundered and went out of business. Individuals who cultivated a learning mindset, continuously monitoring and following the latest directives, kept functioning and recovered faster.

3. Health Security Is National Security.

In a 2015 TED Talk, Bill Gates warned that the greatest threat to humanity would come “not from a missile but a microbe.” Before the pandemic, a cascade of warnings went unheeded. In 2019, White House economists warned that a pandemic could devastate America. As the pandemic unfolded, delayed responses, magical thinking, mixed messages, and lack of coordination cost precious time and countless lives.

Before COVID-19, most thought little about public health infrastructure or infectious disease modeling. COVID-19 made clear that underinvesting in public health is not just a medical risk but a geopolitical and economic risk as well. In today’s interconnected world, a virus emerging in one corner of the globe can bring entire economies to a halt and overwhelm healthcare systems thousands of miles away.

Key takeaway: Investments in early warning systems, stockpiling of essential medical supplies, and better international coordination must be considered strategic imperatives, not budget line items. Health security is just as important as military security.

4. Inequality Doesn’t Disappear in Crisis—It Gets Exposed.

While the virus itself was biologically impartial, its impacts were anything but. Marginalized and vulnerable communities bore the brunt of both the health and economic fallout. Disparities in access to healthcare, employment protections, digital connectivity, and even clean air and water became painfully visible.

Essential workers—once taken for granted—emerged as the backbone of society. Grocery clerks, delivery drivers, sanitation workers, and healthcare aides kept our systems running while risking their own health. For a brief moment, the conversation around equity and inclusion gained renewed urgency.

Key takeaway: The challenge now is to act on that awareness going forward. The post-pandemic world must actively work to close gaps, not widen them, because, in the next crisis, those disparities will come back to haunt us all over again.

5. Innovation Is Our Lifeline in Crisis, and in the Future We Create.

In the darkest days of the pandemic, human ingenuity shined. Scientists across borders collaborated at unprecedented speeds to develop vaccines using novel mRNA technology. Educators adapted to online teaching. Companies retooled their operations, launched new services, and shifted to digital business models practically overnight.

The rapid rise of video-conferencing tools transformed the workplace and accelerated the remote work revolution. Long-standing barriers to telemedicine were swept away, and the technology sector didn’t just survive; it became a vital infrastructure for continuity.

Key takeaway: Innovation wasn’t optional in meeting the Covid-19 criai—it was oxygen. And the systems that encouraged experimentation, rapid iteration, and bold thinking fared better. The lesson is clear: we must nurture innovation not just in emergencies but as a daily discipline.

6. Leadership in Times of Crisis Reveals Character

Every crisis is a test of leadership. COVID-19 revealed which leaders were prepared and which were not. Some communicated clearly, showed empathy, and made smart decisions that saved lives and stabilized communities. Others disappeared, floundered, delayed, denied, or deflected—often with tragic consequences.

Effective crisis leadership wasn’t about knowing all the answers. It was about asking the right questions, adapting quickly, and staying in touch with stakeholders. The best leaders demonstrated transparency, built trust, and showed compassion. The worst fueled division and confusion and stoked fear.

Key Takeaway: Leadership in crises reveals who we really are. The next disruption—whether from climate disaster, cyberattack, nuclear fallout, or global pandemic—won’t wait for us to prepare. Preparedness is a mindset we must cultivate for the times in which we suddenly find ourselves living.

This article originally appeared in Forbes

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