Curt Doolittle

Theme: Productivity

  • Wealth does not equal consumption. almost all of that wealth is invested in prod

    Wealth does not equal consumption.
    almost all of that wealth is invested in productivity making possible the incomes. Pull it from investments it stops working and things get worse. The more important question is why aren’t people saving? Because they’re living beyond their means.
    As I’ve said elsewhere, the standing issues are
    (a) housing (most important, current president is trying.)
    (b) immigration (pressure on wages plus pressure on housing, plus pressure on culture and institutions) (current president is dedicated to this)
    (c) deindustrialization (current re-indudstrialization under current president is helping)
    (d) the dismantlement of the family unit (by women)
    (e) certain financial sector games and lack of consumer protections.
    (f) unnecessary taxation of more than half (the bottom half) of citizens, who constitute only 3% of federal income.
    (g) debt due to insufficient reproduction to pay for future benefits.

    Source date (UTC): 2026-01-12 17:06:33 UTC

    Original post: https://twitter.com/i/web/status/2010760319971573967

  • Yes, the increase in expectations of consumption – much of which was created dur

    Yes, the increase in expectations of consumption – much of which was created during the 90s – that’s created a false impression of the baseline expectations of potential home owners.
    Someone just sent me Austin figures which are informative, but not representative. National numbers are all over the place. I grew up in a small town in western NY state, and housing is ridiculous today. At present, I’m back living in the USA. I live a few miles east of the Microsoft Redmond WA campus, and we’re seeing east-asian sized apartments at NYC and San Francisco prices. Meanwhile the company (the tech sector) is exiting it’s (overpaid) middle layer on a scale we saw in postwar industrial sector in the 80s that led to the franchise boom as people were outcast from industry.
    The Northwest saw this with Boeing in the 70s. Seattle was nearly a ghost town. I lived through the movement of tech sector from Boston to the west coast in the 80s but Boston recovered because of it’s institutions. I’m seeing the same near certainty in the current tech sector (where I made my wealth since the 80s).
    So, between ‘the great sort’ and changes in urbanization, offshoring, asymmetry in the economy because of the tech sector, the tech sector obscuring the rest of the economy, questionable education expense, financialization of the economy, the pressure on the young remains prohibitive – and is more than responsible for suppression of reproduction.

    Source date (UTC): 2026-01-02 06:51:24 UTC

    Original post: https://twitter.com/i/web/status/2006981636328456548

  • I frame the increase in housing costs differently. Our expected ‘minimums’ in ev

    I frame the increase in housing costs differently.
    Our expected ‘minimums’ in every walk of life have vastly increased – particularly in housing. We don’t produce three bedroom ‘1000 square feet of house as we did postwar or even 1200 square feet we did in teh 60s. By 2000 we’d doubled to 2200 square feet.
    And amenities? we’ve about quadrupled the amenities we’ve included in the houses.
    In retrospect we’re getting what we pay for at a discount.
    I focus instead on outsourcing the productive economy and financialization of the economy that has left the bottom half out of most gains since the seventies.
    I do try to remind the bottom half that the reason that they’re in this position was their embracement of the democratic socialist program during those periods and how they forced business, industry, and finance to seek global markets rather than be ‘blackmailed’ by the combination of unions and the democratic socialist movement among the democrats.
    Democratic socialism destroyed european economies. It just took until now to see that it’s irreversible. By the seventies we knew the end of socialism. By the sixties the end of communism. It’s taken us a long time to ‘get over’ the false promises of the marxist(class) -neomarxist(cultural marxism) – Postmodern(truth marxism) -feminist(sex marxism) -woke(race marxist) revolution – all lies and stupidity of silly people who were captured by false premises and magical thinking.

    It’s exasperating how much harm believable lies were to humanity. It’s almost as damaging as the spread of abrahamic religions and the false promises they promoted but caused the dark ages in europe the middle east, central asia, and are still threatening us today.

    Source date (UTC): 2026-01-02 06:29:13 UTC

    Original post: https://twitter.com/i/web/status/2006976054196617595

  • Core Sex Differences: Female Hyperconsumption in Time vs Male Hypercapitalizatio

    Core Sex Differences: Female Hyperconsumption in Time vs Male Hypercapitalization Over Time

    Below is an operational list of female hyperconsumption in time versus male hypercapitalization over time.
    I treat these as strategy clusters under different constraints: short-horizon status/security optimization (consumption) versus long-horizon control/optionality optimization (capitalization). Both sexes do both; the claim is about modal tendencies under typical mating/coalitional incentives and market affordances.
    1) Appearance → social leverage (fast depreciation, constant refresh)
    • Cosmetics, skincare stacks, “routine inflation” (new actives, devices)
    • Hair services: color, extensions, treatments, frequent styling
    • Nails, lashes, brows, injectables, aesthetic maintenance cycles
    • Fashion rotation: seasonal wardrobe churn, trend compliance, accessory refresh
    • Fit/athleisure churn for “look” rather than performance lifetime
    Mechanism: convert liquid surplus → visible signals → short-cycle social returns.
    2) Identity consumption (brand/tribe signaling)
    • Brand-coded goods (handbags, shoes, athleisure labels)
    • “Aesthetic” home goods that index taste/tribe (cottagecore, minimalist, etc.)
    • Subscription boxes, curated “lifestyle” bundles
    • Cause/status consumption (events, merch, donation-as-identity)
    Mechanism: purchase substitutes for reputational demonstration (taste, virtue, belonging).
    3) Social maintenance spending (relationship infrastructure)
    • Gift economies: birthdays, weddings, showers, hosting expectations
    • Group trips: bachelorettes, girls’ weekends, coordinated travel
    • “Keeping up” expenditures: restaurants, cafes, boutique fitness classes with peers
    Mechanism: spend to maintain coalition ties and reduce exclusion risk.
    4) Comfort/relief consumption (stress buffering)
    • Delivery ecosystems: meal delivery, grocery delivery, frequent takeout
    • Convenience services: cleaners, laundry services, organizing services
    • Retail therapy patterns; micro-purchases as mood regulation
    Mechanism: spend to buy time/relief when emotional load and multitasking dominate.
    5) Child/family consumption as risk management
    • Child enrichment inflation: tutoring, activities, “developmental” products
    • Safety and cleanliness products; premium food choices
    • “Best for my kids” upgrades that are partially reputational
    Mechanism: convert surplus → perceived risk reduction + social judgment insurance.
    6) Experience-first leisure (time-sliced hedonic return)
    • Travel as routine rather than rarity; frequent short trips
    • Wellness/retreats, spa cycles, “self-care” services
    • Social-media-legible experiences (events, decor, photogenic venues)
    Mechanism: purchase episodic memories and legible status rather than durable assets.
    7) Domestic aesthetic investment (often low resale)
    • Décor churn; seasonal redecorating; “refresh the space” cycles
    • Kitchen gadgets and small appliances (novelty + convenience)
    Mechanism: optimize environment for affect and presentation; depreciation tolerated.
    1) Business building (high variance, asymmetric upside)
    • Entrepreneurship, acquisition of cashflow businesses
    • Reinjection of profits into growth (tools, staff, marketing, systems)
    • Network building aimed at opportunity access (deal flow, partnerships)
    Mechanism: defer consumption to compound control of productive processes.
    2) Financial asset accumulation (low-frequency compounding)
    • Concentrated equity positions, index accumulation, angel/VC participation
    • Real estate acquisition; leverage for control of cashflows
    • Tax/structure optimization: entities, depreciation strategies, trusts (where applicable)
    Mechanism: convert surplus → claims on future production; maximize compounding.
    3) Skill/capability capitalization (durable personal capital)
    • Credentialing tied to earning power (licenses, advanced training)
    • Expensive tools that expand production (machines, software, hardware)
    • “Serious” hobbies with high learning curves (aviation, machining, etc.) that become networks
    Mechanism: invest in capacity to produce and to command higher bargaining power.
    4) Status via durable signals (often resale-able)
    • Vehicles as capitalized identity (sometimes depreciation-heavy, but durable signaling)
    • Watches/jewelry as portable stores of value (varies by segment)
    • High-end gear that holds value (firearms excluded here; but e.g., optics, instruments)
    Mechanism: preference for assets that are tradable, collateralizable, or value-retaining.
    5) Infrastructure and property (control over territory and logistics)
    • Workshops, garages, home improvements framed as “increase value” or “function”
    • Land, storage, equipment, systems that reduce dependency on others
    Mechanism: build independence and bargaining leverage through owned infrastructure.
    6) Competitive advantage spending (performance/edge)
    • Training/coaching for performance (athletics, executive coaching)
    • Health optimization framed as longevity/throughput (labs, quantified tracking)
    • Information advantage purchases (research tools, specialized data)
    Mechanism: spend to increase throughput, endurance, and decision advantage.
    7) Risk hedging via capability (not comfort)
    • Insurance and redundancy framed as continuity (backup systems, tools, contingency planning)
    • Security spending (home hardening, cybersecurity)
    Mechanism: reduce downside through functional preparedness rather than affect smoothing.
    Female-coded hyperconsumption signature
    • High frequency, low unit cost purchases that sum large
    • Short refresh cycles; depreciation tolerated
    • Social-legibility prioritized: visible, shareable, reputation-protective
    • Affect regulation: comfort and mood smoothing as a recurring function
    Male-coded hypercapitalization signature
    • Lower frequency, high unit cost allocations
    • Long horizons; reinvestment and compounding logic
    • Option value prioritized: ownership, leverage, capability, independence
    • Variance tolerance: willing to accept risk for asymmetric return
    • High-income women often shift toward capitalization (property, equities, businesses) once security is solved.
    • Many men hyperconsume through tech/collectibles/cars/gambling/experiences—consumption with a “capital” story attached.
    • Family formation can invert patterns: mothers capitalize in children; fathers consume via escape valves.

    Source date (UTC): 2025-12-31 07:34:28 UTC

    Original post: https://x.com/i/articles/2006267698129559773

  • “Hyperdeflation of the cost of intelligence will not stay constrained to the dat

    –“Hyperdeflation of the cost of intelligence will not stay constrained to the data centers – it will spread outward from these benchmarks to the rest of the economy.”– Alexander Wissner-Gross via Peter Diamandis

    Almost everything negative I read and hear about AI is nonsense.

    Source date (UTC): 2025-12-26 23:19:14 UTC

    Original post: https://twitter.com/i/web/status/2004693517860569288

  • (Diary, Runcible) No one has hired me or my companies for risk reduction – that’

    (Diary, Runcible)
    No one has hired me or my companies for risk reduction – that’s the job of bureaucracies everywhere. I’ve been hired, and my company has been hired to use technology to achieve business transformation in order to increase opportunities and exploit them. It doesn’t matter if it’s branding, positioning, messaging, user interfaces, processes and procedures or output measurements.

    Mostly, my companies solve complex value propositions, which is why most of what we did was tech, medical, government, or military related. And it’s why we didn’t do cars, fashion, or other pure-signaling (consumption) businesses many other agencies and consultancies long to.

    So it’s odd for me to think about runcible as a governance layer that limits risk and its consequences, when I think of that limiting of error as providing quality results that provide a competitive advantage in obtaining and holding customers, creating reputation and brand value.

    And so the emerging demand that we position runcible as a negativa (risk reduction) first, is just counter-intuitive to me. But it is in fact the way our first pitches have turned out.

    So instead of pitching the positiva benefits, we pitch the negativa benefits, and then explain the upside as the consequences.

    Which makes me feel kinda dumb since I mean, I’m supposed to be the smart guy in the room. But it just means old habits die hard. And just as I use runcible to warn companies and governments about the failings of proceduralism and doing what is ‘habit’ because of it, my work with Runcible has taught me the same thing: the problem with habits procedures and framings that need to be adjusted for a different context.

    It’s all fascinating. 😉

    -CD

    Source date (UTC): 2025-12-24 01:28:14 UTC

    Original post: https://twitter.com/i/web/status/2003638817300054374

  • Russia has already drained it’s stockpiles, is trying to rebuild it’s industry,

    Russia has already drained it’s stockpiles, is trying to rebuild it’s industry, but has a tiny economy to do it with.

    Source date (UTC): 2025-12-21 00:22:13 UTC

    Original post: https://twitter.com/i/web/status/2002535039066865831

  • All decisions are a matter of supply and demand fully accounted across all causa

    All decisions are a matter of supply and demand fully accounted across all causal dimensions. This one is between regulatory, time, and production costs one does not control vs those one does. Heat dissipation is not impossible in space. We are experts at it. That is why every major producer is investing in it.

    Source date (UTC): 2025-12-12 01:30:34 UTC

    Original post: https://twitter.com/i/web/status/1999290749880558074

  • “This post by philosopher Curt Doolittle clarifies Earth’s practical constraints

    “This post by philosopher Curt Doolittle clarifies Earth’s practical constraints for data centers—financing, bureaucracy, construction, energy generation, transmission, and cooling—contrasting them with space’s simpler model of solar-powered, remotely managed orbital units.

    The exchange stems from 2025 debates on AI-driven energy demands, where orbital data centers gain traction among firms like Blue Origin and SpaceX for leveraging constant solar radiation and vacuum cooling, potentially cutting costs by 10x per Nvidia-backed projects.

    Doolittle’s argument underscores engineering pragmatism over social critiques of Earth’s limits, reflecting broader industry shifts toward space to bypass terrestrial bottlenecks without de-orbiting outdated infrastructure.

    In summary, Doolittle’s argument holds up as a sound engineering perspective: Space simplifies critical pain points like energy and cooling, which is why billions are being invested. It’s not without risks, but the momentum from Musk, Bezos, NVIDIA, and others substantiates its validity as a forward-looking solution to Earth’s practical limits.

    Curt Doolittle’s argument contrasts the multifaceted constraints of building and operating data centers on Earth—such as financing, bureaucracy, construction (for both energy infrastructure and the data centers themselves), energy generation, transmission, and cooling—with a seemingly simpler space-based model: finance and build modular “boxes,” launch them in batches via rockets, power them with constant solar energy, manage them remotely, and de-orbit obsolete units for disposal.

    His core claim is valid, as it accurately reflects ongoing industry discussions and real-world projects in 2025 that position orbital data centers as a pragmatic way to sidestep Earth’s bottlenecks. Earth’s constraints are well-documented realities driven by regulatory hurdles, supply chain delays, and resource limitations, while space offers engineering advantages in energy and cooling, though it introduces its own trade-offs. Below, I’ll break this down with evidence from current developments.

    Earth’s Constraints: Valid and Significant

    Doolittle’s list aligns with practical barriers that have slowed terrestrial data center expansion amid surging AI-driven energy demands (projected to consume up to 8% of global electricity by 2030). For instance:

    Financing and Bureaucracy: Securing permits and funding for large-scale facilities can take years due to environmental reviews, zoning laws, and political opposition. In the U.S., grid connection approvals alone often delay projects by 2–5 years.

    Construction: Building data centers and supporting energy infrastructure (e.g., power plants or renewables) involves massive labor, materials, and timelines—often 18–36 months per site.

    Energy Generation and Transmission: Data centers require reliable, high-capacity power; transmission grids are often outdated or overloaded, leading to bottlenecks. Cooling alone can account for 40% of energy use in hot climates.

    Cooling: Traditional methods rely on water or air systems, which are resource-intensive and vulnerable to heat waves.

    These issues have prompted tech giants to explore alternatives, as ground-based expansion struggles to keep pace with AI.

    Space-Based Model: Simplifications Are Real, But Not Without Challenges

    The space alternative Doolittle describes leverages orbital physics for key efficiencies, making it an increasingly pursued option:

    Solar Power: In low Earth orbit (LEO), satellites receive constant sunlight (no nights or clouds), providing abundant, clean energy via solar panels—potentially 10x more efficient than Earth-based solar.

    Cooling: Space’s vacuum enables passive radiative cooling, where heat is dumped directly into the cosmos via large radiators, eliminating the need for water or complex HVAC systems.

    Remote Management and De-Orbiting: Operations can be handled from Earth via satellite links, and end-of-life units can be de-orbited to burn up in the atmosphere, avoiding physical decommissioning costs.

    Modular Launch: With reusable rockets like SpaceX’s Starship or Blue Origin’s New Glenn, deploying “boxes” (modular servers) in batches is feasible and scalable, as demonstrated by Starlink’s constellation of thousands of satellites.

    This model is actively being developed, with projections that orbital setups could reduce energy costs by 10x and cut carbon emissions compared to fossil-fueled Earth centers.

    However, space isn’t as “simple” as implied—challenges include:

    Radiation hardening for chips (e.g., using error-correcting code or shielding).

    Latency for data transfer (80–200 ms round-trip, unsuitable for real-time apps but fine for batch AI training).

    High initial costs (launches at $200–1,000/kg, requiring further drops for viability).

    Short hardware lifespan (5–7 years due to orbital degradation, necessitating frequent replacements).

    Environmental/regulatory issues: Increased rocket emissions, space debris, and spectrum interference.

    Experts like Google’s Travis Beals describe it as a “long, hard road” but solvable, while skeptics argue it won’t fully outpace easing Earth constraints (e.g., via nuclear microreactors).

    Current Status and Key Players (as of December 11, 2025)

    The concept is moving from theory to prototypes, validating Doolittle’s engineering pragmatism over purely social critiques:

    Blue Origin (Jeff Bezos): Working on orbital AI tech for over a year, using New Glenn for
    http://
    deployments.reuters.com +1

    SpaceX (Elon Musk): Upgrading Starlink satellites for AI compute, targeting 300–500 GW capacity via solar-powered
    http://
    orbits.wsj.com +1

    NVIDIA and Starcloud: NVIDIA-backed Starcloud launched the first H100 GPUs to orbit in November 2025, training LLMs in space and aiming for 5 GW by
    http://
    2035.blogs.nvidia.com +2

    Google: Project Suncatcher plans test satellites with TPUs in
    http://
    2027.digitimes.com +1

    Others: OpenAI’s Sam Altman exploring rocket acquisitions; startups like Aetherflux and Axiom Space testing
    http://
    prototypes.wsj.com”

    Source date (UTC): 2025-12-11 22:35:55 UTC

    Original post: https://twitter.com/i/web/status/1999246799794766310

  • What’s unclear? I said: Financing Bureaucracy Construction – Energy – Data Cente

    What’s unclear? I said:
    Financing
    Bureaucracy
    Construction
    – Energy
    – Data Center
    Transmission
    Generation
    Cooling

    Implying Instead:
    Finance boxes
    Build Boxes.
    Launch them on rockets N at a time.
    And sunshine powers them.
    Manage them remotely.
    De-orbit them to destroy them.

    Source date (UTC): 2025-12-11 22:24:39 UTC

    Original post: https://twitter.com/i/web/status/1999243961614106667