Technology Diffusion & Transformation Curves

How transformations actually land: slow compounding flywheels, high-friction liminal phases, decades-long diffusion — against the instant-revolution narrative.

Also known as: svinghjul / svinghjuleffekt (flywheel / flywheel effect), svinghjul, flywheel, flywheel effect, S-kurve (S-curve), S-curve, liminalfase (liminal phase), liminal phase, traktor-analogien (the tractor analogy), tractor analogy, co-evolution of practice, produktivitetsvekst (productivity growth), fra opposisjon til posisjon (from opposition to position), industrial revolution, industriell revolusjon

How authors use it

Oldest first (extraction date when no publication date is known).

Documents (16)

• Nils Brede Moe's Post Nils Brede Moe · 2026-09-23 Reader 🖥️
  Teams that genuinely pursue value from AI-assisted coding may work slower at first, because AI shortens code-writing but does not shorten the harder, team-level work of learning to specify tasks, verify quality, and build a shared practice — so the real productivity gain comes not from AI writing code faster but from the team developing a new, common way of working.
• How Organizations Use AI: Evidence From ChatGPT Aaron Chatterji, David Holtz, Neel Rakholia, Prasanna Tambe, Gawesha Weeratunga · 2026-08-11 Reader 🖥️
  Enterprise ChatGPT adoption and use, measured through OpenAI's own product telemetry across 1,500+ organizations and 17M+ messages, is a broad but highly uneven organizational phenomenon — concentrated among larger, more valuable, more intangible-capital-intensive firms, and diffusing unevenly across job functions, seniority levels and tasks within adopting firms — showing that organizations are still in an early, open-ended process of learning where generative AI belongs in their workflows rather than having already converted access into uniform deployment.
• AI as an Enterprise Operating System Tim O'Reilly · 2026-07-31 source ↗ Reader 🖥️
  Enterprise AI adoption is stalled not because models are undercooked but because almost no organization has done the deliberate, CEO-led mechanism-design work — a capability ladder tied to status and pay, hardened defaults, a skills-as-code habit, and a forced permissions audit — that Guido's Trail of Bits case study shows is what actually turns AI from a tool into an operating system.
• AI and the Looming Competition for Margin Philipp Carlsson-Szlezak and Paul Swartz (BCG) · 2026-07-30 Reader 🖥️
  AI productivity gains are inherently deflationary — because a widely diffused technology confers no durable competitive advantage, the industries best at converting AI into productivity growth will see the fiercest price competition and margin erosion, making consumers rather than firms the real winners, so executives must invest anyway (the risk of abstention is asymmetric) while expecting the payoff to be survival, not profit.
• Howard Yu's Post — American AI models went from 70% of usage to 30% in 12 months Howard Yu · 2026-07-11 source ↗ Reader 🖥️
  The AI-model market is flipping from US to Chinese dominance in real production usage, and the decisive driver is per-task cost — decisive precisely because the agentic era multiplies calls per task, so distribution consolidates on the cheapest 'good-enough' models faster than any residual US capability lead can matter.
• Lawyers are quietly becoming builders Ernie Svenson · 2026-07-10 source ↗ Reader 🖥️
  AI has redefined 'building' from writing software to iteratively correcting and reusing AI workflows for repeatable tasks, so any lawyer — not just a technical few — can and should become a builder without learning to code.
• Are We Entering a New Age of Creativity with the Help of AI? Harvard Business Review · 2026-07-07 source ↗ Reader 🖥️
  Laurene Powell Jobs's Atlantic-OpenAI content-licensing deal exemplifies the correct strategic response to generative AI for creative institutions: treat AI licensing as a short-term revenue and access play while doubling down on human-curated 'meaning' and top-tier creative talent, because genuine creativity — unlike AI's pattern recognition — remains scarce and is what audiences will still pay for.
• Ordinary Engineers, Not Heroic Inventors Tim O'Reilly · 2026-07-07 source ↗ Reader 🖥️
  Technological and economic leadership — for nations and companies alike — is won not by inventing or dominating the 'leading sector' of a new technology but by diffusing a general-purpose technology like AI more broadly and patiently than rivals across ordinary, unglamorous work, a slow organizational (not technical) process that depends on skill infrastructure and on incentives that reward the 'ordinary engineers' who actually do the diffusing.
• We've Stopped Arguing About Whether AI Changes Software Greg Herlein · 2026-07-07 source ↗ Reader 🖥️
  The debate over whether agentic AI transforms software engineering is settled; the transformation is a shift in the center of gravity of the work — from writing code to specifying, verifying, and curating knowledge.
• The Org Age of AI: A Collection of Enterprise AI Adoption Guides Turing Post · 2026-07-04 source ↗ Reader 🖥️
  Across its 'Org Age of AI' series, Turing Post treats enterprise AI value as gated by a sequence of organizational and engineering problems — workflow redesign, organizational legibility/maturity, the contrast between AI-native startups and existing enterprises, the workflow (not the model or agent) as the true unit of adoption, verified self-improving 'flywheels,' spec-driven engineering discipline, and workload-appropriate hybrid infrastructure — in which raw model capability is conspicuously absent from the list of binding constraints.
• DORA Community Discussion - Operationalizing DORA within a large organization DORA (speaker: Julia Jakubiec, ABN AMRO; host: Nathan Harvey) · 2026-06-26 source ↗ Reader 🖥️
  Operationalizing DORA at enterprise scale is fundamentally a change-management problem — solved by running Kotter's 8 steps to institutionalize a durable continuous-improvement culture — not by the metrics themselves, which serve only as a recognizable survey instrument, authority brand, and benchmarking catalyst.
• Én enkelt behandling kan gi svært langvarig kolesterolsenkning: – Noe radikalt nytt Bård Amundsen · 2026-05-29 source ↗ Reader 🖥️
  A single infusion of an in-vivo gene-editing therapy that permanently disables the PCSK9 gene produced deep and durable (18-month) LDL-cholesterol reduction in a small first-in-human trial, suggesting that cardiovascular disease prevention may shift from lifelong daily medication toward a one-time, vaccine-like treatment.
• 17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis Truls Jørgensen og Audun Fauchald Strand — Heis Podcast · 2026-05-18 source ↗ Reader 🖥️
  Large sociotechnical transformations — whether NAV's digital transformation a decade ago or the current AI shift — work by accumulating slow flywheel momentum that then snaps into a sudden S-curve climb through a high-friction liminal phase, and the leaders who guide organizations through such transitions are those who can 'take the elevator' across organizational floors and disciplines, translating between domain, technology and executive language while staying close enough to the craft that their advice is grounded in something real.
• Kunstig intelligens gir neppe en ny, industriell revolusjon Tobias Lunde · 2026-04-10 Reader 🖥️
  History shows that AI is unlikely to produce an economic revolution comparable to the Industrial Revolution, because AI does not remove fundamental physical constraints on production the way steam and electricity did, because technology diffusion takes decades, and because broad value creation depends on institutional and infrastructural preconditions that are not yet in place.
• Simon Wardley's Post — Industrialisation of ML: nine notes on what to expect Simon Wardley · 2025-12 Reader 🖥️
  What is happening with AI is the industrialisation of machine learning — a one-way evolutionary shift to commodity/utility — and the right posture toward it is neither resistance nor hype but disciplined adaptation through the predictable 5-8 year co-evolution of practices, with explicit attention to where bottlenecks, supply-chain power, and inequality will actually land.
• The End of Programming as We Know It Tim O'Reilly · 2025-02-04 source ↗ Reader 🖥️
  AI will not end programming or cause mass programmer unemployment; it is the latest in a long series of abstraction shifts that each redefined what 'programming' means and historically increased, not decreased, the number of programmers and the scope of what gets built — provided organizations and individuals invest in the 'learning by doing' needed to convert raw AI capability into deployed productivity.

Propositions touching this concept (11)

By publication month, oldest first (extraction month when no publication date is known).

2025-12

• explicitcorroboratedPractices will not stabilise before 2029, because co-evolution of practice with industrialising technology typically takes 5-8 years.
  Simon Wardley's Post — Industrialisation of ML: nine notes on what to expect · p2

2026-04

• explicitThe comparison between AI and the Industrial Revolution rests on a misunderstanding of what caused the Industrial Revolution.
  Kunstig intelligens gir neppe en ny, industriell revolusjon · p2

2026-05

• explicitcorroboratedLarge organisational transformations follow an S-curve composed of many simultaneous smaller S-curves (deploy frequency, team formation, developer share, etc.), each switching on around the same time.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis · p1
• explicitdevelopedBetween paradigms there is a liminal phase characterised by high friction and risk that cannot be shortcut — old structures must be released before new ones settle.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis · p2
• explicitGenerative AI is a fundamental abstraction shift — from high-level instructions to expressing intent — and we are at the start of its S-curve, not somewhere on the plateau.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis · p8

2026-06

• explicitCivilizational progress itself follows an S-curve-like 'progress law,' and whether we are near a saturation point (technological maturity) or still early on a much deeper, largely unexplored tech tree is currently unknown — 'most likely' saturating, but the timing could be close or far.
  46 thoughts on the near future · p15

2026-07

• explicitThe S-curve is produced by a tug-of-war between a reinforcing growth loop (which makes growth easier as it accumulates) and an inhibiting loop (which eventually slows and caps growth).
  Canon 3X: Explore/Expand/Extract · p2
• explicitcontestedAI adoption compounds into a flywheel, and the danger is that the slowest laggards fall so far behind that the gap becomes structural — a permanent moat rather than a lag closable with effort.
  We've Stopped Arguing About Whether AI Changes Software · p13
• implicitThe eight-item sequence (ROI/workflow redesign, maturity framework, AI-native startup design, the no-AI-native-enterprises reality check, workflow-as-adoption-unit, verified flywheel autonomy, spec-driven engineering discipline, and hybrid infrastructure placement) is presented as a connected progression of organizational and engineering problems to solve, in which model capability itself is never named as a binding constraint.
  The Org Age of AI: A Collection of Enterprise AI Adoption Guides · p6

2026-08

• explicitAccess to ChatGPT Enterprise does not imply uniform, effective, or immediate deployment; firms are still in an active, open-ended process of discovering where generative AI belongs in their workflows, paralleling historical general-purpose-technology diffusion (electrification, computerization) that took decades of complementary co-invention to pay off.
  How Organizations Use AI: Evidence From ChatGPT · p12

2026-09

• explicitAI will automate some work, eliminate some job families, and consolidate some industries, following the pattern of every technology shift since the Industrial Revolution.
  The oldest architecture in computing · p1

Tensions

Open gaps (28)

• incompleteA 35-patient trial with an 18-month observation window is treated as sufficient basis for headline framing ('radically new', 'a vaccine'), without discussing statistical power, control-group design (is this placebo-controlled, dose-ranging, open-label?), or how many of the 35 patients were in the highest-dose (62%-reduction) group versus lower, less dramatic doses.
  Én enkelt behandling kan gi svært langvarig kolesterolsenkning: – Noe radikalt nytt — read next: The original NEJM paper (Vafai et al., 'In Vivo Base Editing of PCSK9 with VERVE-102 for Hypercholesterolemia') for trial design details, and later Phase 2/3 results from the planned ~200-patient study.
• incompleteThe article treats 'off PCSK9' and 'lower LDL' as essentially interchangeable outcomes and never addresses whether a permanent, irreversible gene edit carries different risk than the existing reversible PCSK9-inhibitor drugs it is being compared to — e.g., what happens if a patient develops an adverse reaction years later with no way to 'undo' the edit, unlike stopping a pill or antibody injection.
  Én enkelt behandling kan gi svært langvarig kolesterolsenkning: – Noe radikalt nytt — read next: Look for expert commentary (bioethics, gene-therapy safety literature) specifically on irreversibility as a distinct risk category from reversible pharmacological intervention, rather than just 'will there be side effects'.
• incompleteThe prescriptive 'What This Means for Executives' section treats the deflationary thesis as urgent enough to mandate immediate, full-scale investment for every executive, without differentiating strategy by how fast or slow deflation is likely to arrive given the very frictions the piece itself lists (regulation, market structure, utilization rates). A firm in a friction-heavy sector (the piece's own medical-care example) gets the identical 'the race is mandatory' prescription as one in a friction-free sector, even though the auto and agriculture case studies show the same dynamic can take seven decades to fully play out.
  AI and the Looming Competition for Margin — read next: None specified by the author — an open strategic question about how urgency should scale with an industry's specific competition-inhibitor profile.
• incompleteThe auto-industry chart's claim that margins fell 'to near 0%' doesn't specify which margin metric (gross, operating, net) is plotted or whether it's an industry-aggregate accounting figure vs. actual firm-level profitability — making the empirical anchor for the central thesis difficult to independently verify or compare against known auto-manufacturer profit margins.
  AI and the Looming Competition for Margin — read next: Primary BEA/BLS/BCG Center for Macroeconomics data cited as the chart's source.
• incomplete'89% of leaders agree AI has accelerated work' — but the article assumes that acceleration is sustained throughput rather than a transient burst as teams adopt new tooling. No engagement with the possibility that the 'AI productivity boom' is a one-time S-curve, not the new steady state.
  Managers Are Struggling to Keep Up with the AI Productivity Boom — read next: DORA reports on AI's effect on engineering throughput; longitudinal AI-adoption studies.
• illogicalThe central 'meaning cannot be commodified out' argument rests on one negative case (Buzzfeed's failure) and one still-unresolved positive case (the Atlantic, whose own outcome Elkins repeatedly disclaims — 'we'll see where it goes... I have no crystal ball'). No comparative base rate of publisher-AI licensing deals succeeding versus failing is offered, so the argument's generalizability is asserted rather than demonstrated.
  Are We Entering a New Age of Creativity with the Help of AI? — read next: Longitudinal tracking of publisher-AI deal outcomes (traffic, revenue, subscriber retention) as more of the '70+' deals mature past their first year or two.
• incompleteThe Jobs analogy is named but not fully cashed out against its disanalogies. Jobs's reality distortion field was famously paired with cruelty, factional purges and product taste applied to consumer aesthetics. Larsen's framing is the opposite — toppdivisjon-ledelse runs on indre motivasjon, not fear; authenticity / easy-to-read-ness is the asset; the product is a public-sector welfare system. The conversation does not name these disanalogies, so the analogy carries more rhetorical work than it earns.
  1) Torbjørn Larsen: Det nærmeste vi kommer Steve Jobs — read next: Read Walter Isaacson's Jobs alongside Tracy Kidder's Soul of a New Machine and Marc Andreessen-tradition writing on charismatic founder leadership to surface the disanalogies; for the NAV case specifically, read Cutler on 'institutionalized overload' to see what indre-motivasjon-based mobilisation can hide. Connects to the gap in the Skjærpe ep 17 extract on transformations that fail in the liminal phase.
• incompleteThe flywheel/S-curve/liminal frame is used to describe both the NAV transformation (which succeeded) and the AI shift (in progress). The conversation never names a transformation that failed at the liminal phase and asks why — so the framework is at risk of being unfalsifiable on its own terms.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis — read next: Read failed transformations through the same frame — e.g., government-IT projects that died in their liminal phase, large agile adoptions that reverted. Cutler's overload work and the 'people who tried Spotify model' literature would expose where the frame breaks. The Geoffrey Moore 'Crossing the Chasm' tradition has more developed accounts of curves that stall.
• incompleteThe 'learning vs production' rule is stated as a personal heuristic but not connected to any educational theory or empirical evidence. For a leader of consultants asked to scale this practice, there is no model for how the two modes should be partitioned, whether across days, projects, or moods.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis — read next: Donald Schön's reflection-in-action; deliberate-practice literature (Ericsson); Beck's 'tidy first?' framing of preparatory vs delivery work — the tidy/messy split echoes the learning/production split structurally. Worth syntopical pass against Beck's recent 'Starving Genies' work on AI-assisted craft.
• incompleteThe elevator metaphor is generalised from Jonas's own career and from the show's animating premise. The conversation does not seriously consider organisations where vertical traversal is structurally suppressed (military, regulated finance with information walls, classified environments). The implicit premise that elevators are always available may not hold.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis — read next: Read about organisations with designed information barriers — security classifications, MAR-walled investment banking, healthcare compartmentalisation. James C Scott's 'Seeing Like a State' on legibility. Where the elevator is locked, what substitutes?
• incompleteThe introvert-leader claim rests on one practitioner's experience and one rhetorical contrast (scale removes small-talk tax). Jonas notes that some colleagues found it hard to distinguish his opinions from his decisions — a cost that may be specifically introvert-shaped (lower social-cue density) but is not investigated.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis — read next: Susan Cain on introvert leadership; the Cutler thread on 'signals that look like decisions' in leadership communication. The implicit problem of 'low-cue leadership at scale' is worth its own extraction.
• incompleteThe tractor and car analogies argue for slow structural change, but neither is examined for disanalogies. AI compresses learning loops in a way physical machinery cannot. The argument relies on the assumption that adoption is gated by humans, not by capability — which is plausible but not defended.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis — read next: The general-purpose-technology economics literature (Bresnahan & Trajtenberg); David Autor on labour-market restructuring time. Counter-cases: dial-up to broadband transitions, mobile phones — both moved faster than tractors. What predicts speed?
• uninformedThe Gukesh anecdote is reported confidently ('the only one of the young players not allowed a computer before age 12') but is not actually verified in the conversation. Whether true or apocryphal, it is doing argumentative work it has not earned.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis — read next: Quick fact-check; more importantly, treat the underlying claim about protected early craft as a hypothesis and look at the actual evidence on AI use in early-career learning — programming pedagogy research will move on this in 2026-2028.
• incompleteThe claim that NAV's success was driven by capability investments is asserted from inside the case by the very people who made those investments. There is no counterfactual — no comparable agency that did not invest similarly and failed similarly. Some of the 'flywheel' may be storytelling.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis — read next: Compare cross-country COVID welfare-IT responses (Germany, Denmark, UK), and Norwegian agencies of similar size that did not transform similarly. The Skatteetaten and Helsedirektoratet experiences would be a natural comparison.
• illogicalJonas argues both that we are early on the AI S-curve (so 10% gains are not the story) and that complexity will push back on productivity gains as they accumulate. These are compatible but in tension: if the curve climbs steeply because of intent-level abstraction, the complexity ceiling should appear later; if complexity bites soon, the curve is shallower than claimed. The argument is not adjudicated.
  17) Jonas Slørdahl Skjærpe om svinghjuleffekter, liminalfaser og kunsten å ta heis — read next: The Brooks / Mythical Man Month tradition on accidental vs essential complexity; Ousterhout on system design; recent measurements on long-running agent harnesses (Anthropic harness design work) which directly test the complexity-ceiling claim.
• incompleteThe argument treats AI as operating purely 'within existing physical frameworks,' but does not consider whether AI might remove cognitive/informational constraints that are analogous to the physical constraints steam removed. If cognitive bottlenecks are the binding constraint on modern economies (as many economists argue), then AI could be categorically transformative in a way the author's framework does not account for.
  Kunstig intelligens gir neppe en ny, industriell revolusjon — read next: Read Daron Acemoglu & Simon Johnson 'Power and Progress' (2023) for a nuanced treatment; also Erik Brynjolfsson's work on the 'productivity J-curve' which argues IT productivity gains are real but delayed by complementary organizational change.
• uninformedThe article cites 'nyere forskning' (recent research) on pre-steam economic growth without naming sources, making the historical claim difficult to verify. The revisionist historiography (e.g., de Vries's 'Industrious Revolution,' Broadberry et al.) is more nuanced than presented here — some scholars argue the pre-1750 growth was qualitatively different from post-Industrial Revolution growth.
  Kunstig intelligens gir neppe en ny, industriell revolusjon — read next: Jan de Vries, 'The Industrious Revolution' (2008); Stephen Broadberry et al., 'British Economic Growth, 1270-1870' (2015).
• illogicalThe Norway-specific argument about US platform dependency is presented as an AI problem, but the author himself notes it is 'et problem uavhengig av KI' (a problem independent of AI). This weakens its use as evidence specifically against AI-driven growth in Norway.
  Kunstig intelligens gir neppe en ny, industriell revolusjon — read next: Examine EU digital sovereignty strategies and whether platform dependency has actually prevented European countries from capturing IT-era productivity gains.
• incompleteThe central 'why lawyers' evidence — that lawyers are the deepest, most-returning users among knowledge workers — comes solely from Anthropic's own product lead (Pike), relayed second-hand in a newsletter that closes with a paid-workshop upsell. No independent usage data or cross-profession comparison is offered, so the load-bearing claim is an unverified vendor assertion.
  Lawyers are quietly becoming builders — read next: Independent, third-party AI-usage studies across professions; legal-AI adoption surveys not published by a model vendor; measured outcomes rather than depth-of-use anecdotes.
• incompleteA single-organization, self-reported success narrated by the practitioner who leads it — survivorship/selection bias with no comparison organization and no counterfactual. 'Success' is measured largely by report readership and engagement (~4,000 reads, ~5,000 self-logged improvements) rather than by independently verified software-delivery performance or business outcomes.
  DORA Community Discussion - Operationalizing DORA within a large organization — read next: DORA/Accelerate outcome studies linking practices to delivery and organizational performance; controlled or multi-organization evidence on change-adoption results.
• incompleteThe article's central analogy (AI's labor effects will resemble the Industrial Revolution's multi-decade diffusion lag) never addresses whether today's much faster information diffusion, platform distribution, and AI self-improvement could compress that lag dramatically — which would matter enormously for near-term organizational and team planning even if the long-run 'more programmers, not fewer' conclusion holds. For a team/org planning horizon of 2-5 years, 'eventually it works out' is compatible with a very disruptive intervening period that the article doesn't characterize.
  The End of Programming as We Know It — read next: Matt Beane's warnings (quoted elsewhere in this corpus) about the rate of change over the 'next year and a half' exceeding the prior two years would be a useful counterweight on timing, even if not on the long-run outcome.
• uninformedDing's Leading-Sector-theory-vs-diffusion-theory dichotomy is presented as though it were the field's main framework, without engaging the substantial pre-existing literature on general-purpose technology diffusion (Bresnahan & Trajtenberg's original GPT economics) or classical diffusion-of-innovation theory (Rogers), leaving unclear how novel Ding's contribution actually is relative to this scholarship.
  Ordinary Engineers, Not Heroic Inventors — read next: Bresnahan & Trajtenberg, 'General Purpose Technologies: 'Engines of Growth'?'; Everett Rogers, Diffusion of Innovations.
• illogicalA single historical case comparison (Japan vs. the US, 1980s-1990s) is used to license a general theory that is then applied to firms and to the current AI moment with little qualification — but this one comparison cannot by itself rule out Japan's well-documented 1990s asset-price collapse, keiretsu financing structure, and demographic decline as competing explanations for its stagnation relative to diffusion theory.
  Ordinary Engineers, Not Heroic Inventors — read next: Literature on Japan's 'lost decades' (asset bubble collapse, structural/demographic explanations) to test whether diffusion theory survives as an explanation once the standard macroeconomic account is on the table.
• incompleteThe argument for decentralized, rightsized AI models over centralized frontier models is made entirely by historical analogy (electrification's unit-drive vs. grid) but never engages current model-training economics — the capital intensity and returns-to-scale that presently favor centralization — which could make the electrification precedent misleading for AI specifically.
  Ordinary Engineers, Not Heroic Inventors — read next: The corpus's compute-infrastructure and ai-unit-economics material on why frontier-model economics currently favor centralization, to test whether the electrification analogy actually holds for AI.
• illogicalThe China-dominance argument compresses 'supply chain understanding' into a near-sufficient condition for long-run dominance. Other variables (capital allocation efficiency, demographics, talent migration, capability of domestic models, export-control regimes, energy supply, civil-military fusion costs) are not weighed.
  Simon Wardley's Post — Industrialisation of ML: nine notes on what to expect — read next: Read against Dan Wang on Chinese tech policy, CSIS/RAND supply-chain analyses, and any sceptical takes on PRC industrial policy execution (e.g. semiconductor self-sufficiency progress vs targets).
• incompleteThe Jevons-paradox reframing (adjacent possibles becoming real) is asserted but the conditions under which adjacent possibles actually become real industries — vs stalling — are not specified. Sometimes the adjacent possible is real and stays small.
  Simon Wardley's Post — Industrialisation of ML: nine notes on what to expect — read next: Read against the slower-than-expected industrialisation histories — early electricity (the 40-year productivity paradox), enterprise software in the 1990s. Beck and Shoup on Jevons-in-coding is a sharper instantiation of this same claim worth juxtaposing.
• uninformed (mild)The post under-weights regulatory ratchets — particularly EU AI Act, sector-specific liability regimes, GDPR-style data sovereignty rules — as climatic variables that shape what 'industrialised ML' actually looks like in different jurisdictions. Wardley's mapping treats regulation as doctrine; this post treats it as background.
  Simon Wardley's Post — Industrialisation of ML: nine notes on what to expect — read next: Read alongside EU AI Act implementing acts, NSM/Datatilsynet guidance for the Norwegian context, and Frederiksen on Coop Bank sovereignty for how an enterprise actually reasons about regulatory ratchets as architectural constraint.
• incompleteThe post is silent on the cultural and organisational variation in how 'practice converges' over the 5-8 year window. Nordic public-sector institutions, Japanese manufacturers, and Silicon Valley startups will not converge to the same practice — but the post talks as if there is one convergence trajectory.
  Simon Wardley's Post — Industrialisation of ML: nine notes on what to expect — read next: Read against Cutler on organisational operating systems and the Heis stable's Norwegian-leader interviews — concrete evidence that practice-convergence is mediated by national culture and institutional shape, not just by elapsed years.