Jevons Paradox

When technology makes a resource cheaper, total consumption rises — applied to cognition and labor to argue AI produces more work and more engineers, not fewer.

Also known as: Jevons paradox, Jevons' Paradox, Jevons paradox (Wardley's reading), Jevons paradox (on cognition), demand elasticity, unhobbling

How authors use it

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

Documents (16)

• Presentation: A Few Predicted Talks From QConAI 2030 Meryem Arik · 2026-09-14 source ↗ Reader 🖥️
  As falling per-unit token costs and near-zero-cost software collide with rising use-case demand, non-technical builders, and parallel agent architectures, the decisive challenge for software organizations through 2030 shifts from producing more code to governing who builds it, what it costs, and how humans supervise it -- a shift that will also redefine what makes a good software engineer and, as public backlash grows, force new AI regulation.
• Presentation: The Free-Lunch Guide to Idea Circularity Holly Cummins · 2026-07-31 source ↗ Reader 🖥️
  Ideas in technology that look genuinely new are almost always old tradeoffs returning because the surrounding environment has re-tilted the balance, not novelties or evidence of forgetting -- and the same 'debt is a tradeoff with the future' logic that explains financial debt also explains technical, epistemic, ecological, and sleep debt, so recognizing which tradeoff underlies a current design is the transferable skill for spotting when a discarded old idea is about to become valuable again.
• 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.
• Research: How AI Agents Broaden the Scope of Knowledge Work Jeremy Yang, Kate Zyskowski, Noah Yonack, Jerry Ma (Perplexity) · 2026-07-29 Reader 🖥️
  AI agents don't just make existing knowledge work faster — by combining much longer autonomous execution with far broader tool and context integration, they lead people to attempt fundamentally different, more cross-disciplinary, and previously-impractical work, so leaders must redesign roles, review points and team boundaries around this expanded scope rather than just harvesting the time and cost savings.
• Presentation: The Future of Engineering: Mindsets That Matter When Code Isn't Enough Ben Greene · 2026-07-28 Reader 🖥️
  Software engineers remain valuable in the age of coding agents not because they can still write code, but because building working systems still requires disciplined simplicity, deliberately protected comprehension, willingness to attack hard problems first, and human empathy for customers — capacities coding agents lack and that startups have always demanded of engineers under resource pressure.
• The loudest warning about AI and jobs yet Casey Newton · 2026-07-14 source ↗ Reader 🖥️
  Although there is no full-blown AI jobs crisis yet, entry-level employment is already showing measurable erosion, and the cohering consensus among economists (crystallized in the 'We Must Act Now' statement) means governments and labs should build data, incentives, and safety-net institutions now, before the disruption becomes undeniable.
• AI's environmental costs threaten water, land and climate United Nations · 2026-06-04 Reader 🖥️
  AI's environmental footprint extends well beyond carbon emissions to substantial and unevenly distributed water, land, and e-waste costs — driven overwhelmingly by everyday inference use rather than training and resistant to efficiency gains via a rebound effect — such that averting resource strain now depends on deliberate governance choices by governments, companies, and users, not on technological improvement alone.
• You Don't Get to Create Anything (Still Burning: Beck + Randy Shoup on Jevons paradox and distributed systems under AI) Kent Beck — Still Burning Podcast · 2026-06-03 Reader 🖥️
  Cognition is becoming near-free, and per Jevons paradox that will produce more cognitive work and more engineers, not fewer — but the people best prepared for this regime are exactly the ones who already lived through writing the distributed-systems playbook, because they know two things the panicking crowd forgets: that a playbook is hard-won and not obvious until written, and that the human compulsion underneath all of this is the maker's need to create the thing rather than merely transcribe it (the 'you don't get to create anything' moment that drives Shoup out of patent law and back into engineering); accordingly the right posture is calm, perspective-keeping, deductive engagement with the new regime — bounding the genie with specs/evals, making good practices easier rather than shaming the bad, and treating this as another wave of physics-discovery rather than a hype cycle or an apocalypse.
• Your Company Needs an Energy Strategy for AI's Next Phase Eric Yanfei Zhao · 2026-06 Reader 🖥️
  Because the AI value stack's scarce control point is migrating from models and GPUs down to electricity itself, every enterprise — not just hyperscalers — must now treat energy access as a board-level strategic variable and build distinctive capability to measure, reduce, contract for, and geographically place its AI compute before scarcity becomes an operating emergency.
• Baseten CEO Tuhin Srivastava on the AI Inference Crunch, Custom Models No Priors (Sarah Guo, Elad Gil) · 2026-05-09 Reader 🖥️
  AI inference is the enduring, last-standing market because intelligence only begets more intelligence, and the companies that win it will combine software excellence with strategic control of scarce compute supply.
• Is AI Doom Going Out of Style? AI Daily Brief · 2026-05-05 Reader 🖥️
  The AI job-doom narrative is losing its grip because intellectual pushback (Jevons' Paradox reaching mainstream audiences via Klein/Imas) and market validation (agentic revenue explosion, rising software job postings) are converging simultaneously — though the authors acknowledge the shift rests on thin evidence and public sentiment remains deeply negative.
• Skill Issue: Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI No Priors: AI, Machine Learning, Tech, & Startups · 2026-03-20 Reader 🖥️
  The jump in code-agent capability since December 2024 has made human throughput — not model capability — the binding constraint, and the central challenge is now removing yourself from the loop so autonomous agents can maximize token throughput on your behalf.
• Gene Kim on Vibe Coding - Part II | Episode 411 | Troubleshooting Agile Douglas Squirrel · 2025-12-18 source ↗ Reader 🖥️
  Vibe coding changes software teams' organizational unit of work — toward smaller, option-rich teams and even individual business-stakeholder-plus-developer pairs rather than net headcount reduction — and because trust in these tools is a learned skill that takes about a year to build, leaders should start adopting now rather than wait for the technology to mature further.
• 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.
• Leading your engineers towards an AI-assisted future Pete Hodgson · 2025-06-26 source ↗ Reader 🖥️
  An engineering leader should drive AI-coding adoption the same way they'd drive any major capability shift — by setting a clear, phased goal (Experimentation, then Adoption, then Impact) measured with lightweight KPIs, giving teams real autonomy over how they get there, and backing that autonomy with concrete organizational support (training, budget, protected time, a community of practice, empathetic communication) — rather than mandating a usage percentage from day one.
• 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 (9)

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

2025-06

• explicitcorroboratedAI will likely automate the tedious parts of engineering work rather than the job itself, and Jevons Paradox implies that cheaper software production increases total demand for software enough to keep engineers employed, even as per-unit cost falls.
  Leading your engineers towards an AI-assisted future · p11

2026-03

• explicitestablishedJevons paradox will apply to software engineering: as AI makes software cheaper, demand will increase, likely creating more software engineering jobs in the near term.
  Skill Issue: Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI · p16

2026-05

• explicitestablishedDevelopers insert more intelligence when inference gets cheaper — Jevons paradox applies to AI inference
  Baseten CEO Tuhin Srivastava on the AI Inference Crunch, Custom Models · p9
• explicitcorroboratedHistorical evidence shows that occupations most exposed to computer automation grew faster in employment than those that were not, via Jevons' Paradox / demand elasticity.
  Is AI Doom Going Out of Style? · p3

2026-06

• economic forecast / framing⚡ disputed & corroboratedCognition has become near-free, and per Jevons paradox this will create more cognitive work and more engineers, not fewer — because tasks that were not economically viable as cognitive work now are.
  You Don't Get to Create Anything (Still Burning: Beck + Randy Shoup on Jevons paradox and distributed systems under AI) · p1

2026-07

• explicitPredictions of 'a world without developers' (COBOL, low-code, and now AI) have consistently failed, and by Jevons Paradox each new layer of abstraction has historically increased the number of developers rather than decreased it, because cheaper software production increases total demand for software.
  Presentation: The Free-Lunch Guide to Idea Circularity · p7
• explicitCurrent AI-driven productivity gains have produced a 'world without rest' rather than the historically promised 'world without work' (Keynes's 15-hour week): professions using AI report working roughly 3 hours more per week than before, an instance of Jevons Paradox applied to time and cognition.
  Presentation: The Free-Lunch Guide to Idea Circularity · p13
• explicitCoding agents are dramatically expanding who can write software (engineers, PMs, designers, marketers, salespeople), and by Jevons paradox the resulting drop in the cost of code raises rather than lowers total demand for software — implying engineering jobs are more likely to grow than disappear.
  Presentation: The Future of Engineering: Mindsets That Matter When Code Isn't Enough · p1

2026-09

• explicitDespite falling per-token prices, total token spend keeps rising -- a Jevons paradox -- because cheaper tokens unlock more use cases and each successive AI-application generation (chatbots, RAG, reasoning models, agents, complex agents) is more token-hungry than the last.
  Presentation: A Few Predicted Talks From QConAI 2030 · p3

Open gaps (42)

• incompleteThe central 'rebound effect' claim — that efficiency gains won't offset rising demand — is asserted without any quantitative comparison of the rate of efficiency improvement versus the rate of usage growth. No numbers are given for how much rebound offsets how much efficiency gain, leaving the reader unable to judge whether this is an established empirical pattern or a directional risk being stated as inevitability.
  AI's environmental costs threaten water, land and climate — read next: Empirical Jevons-paradox / rebound-effect literature from historical energy-efficiency episodes (lighting, computing); compare against extract-hbr-energy-strategy-for-ai's parallel claim about DeepSeek-style efficiency gains and induced demand.
• illogicalThe speaker states up front that she personally tends to overestimate how quickly enterprises change, but does not apply that self-flagged bias when she reaches her most enterprise-speed-dependent predictions -- 'IT manages thousands of apps by 2030' and 'no more separate QCon AI by 2030' both assume fast, broad enterprise organizational change, without her flagging the tension with her own stated error direction.
  Presentation: A Few Predicted Talks From QConAI 2030 — read next: Re-read the talk's specific 2030 timelines discounted for the overestimation bias she herself names, to see which predictions would more plausibly land closer to 2032-2035.
• incompleteThe '400% NDR' and extreme stickiness claim is stated without discussing what happens when the supply crunch eases. If compute becomes abundant, does the software moat alone sustain that retention, or is current stickiness partly a supply-lock-in artifact?
  Baseten CEO Tuhin Srivastava on the AI Inference Crunch, Custom Models — read next: Analysis of cloud platform switching costs and lock-in dynamics in prior infrastructure transitions (e.g., CDN market evolution)
• incompleteThe 'last market' framing assumes training commoditizes while inference persists. But if continuous post-training is integral to inference (as Tuhin himself argues), the boundary between training and inference revenue may dissolve, complicating the 'last market' claim.
  Baseten CEO Tuhin Srivastava on the AI Inference Crunch, Custom Models — read next: Papers or analyses on the convergence of training and inference workloads; Nvidia's own framing of this boundary
• incompleteThe 'concierge everything' vision is stated as unambiguously positive with no engagement with critiques of AI-mediated services (deskilling, dependency, privacy, equity of access).
  Baseten CEO Tuhin Srivastava on the AI Inference Crunch, Custom Models — read next: Critical perspectives on AI-mediated services from Shoshana Zuboff, Meredith Whittaker, or similar voices
• incompleteThe talk doesn't examine whether AI-driven computing efficiency could itself be subject to the same Jevons-Paradox rebound Cummins invokes to defend developer jobs -- i.e., if cheaper computation increases total computation demand, the same logic likely undercuts her own claim that efficient software (Quarkus) straightforwardly reduces carbon, since more efficient compute could simply induce more compute usage and offset the gains. This exact rebound-effect argument, applied to AI energy specifically, is developed elsewhere in this corpus and represents a direct, unaddressed tension with Cummins's efficient-software-is-climate-action argument.
  Presentation: The Free-Lunch Guide to Idea Circularity — read next: extract-ai-environmental-costs-un, which argues efficiency improvements are unlikely to offset rising AI resource demand because of a rebound effect.
• illogicalThe Jevons's-paradox-style prediction ("10x cheaper production → 10x more software, more jobs") does not engage the fixed downstream-capacity constraint (review, testing, integration, compliance) that this corpus's own DORA 2025 extraction identifies as a hard limit on how much AI-generated throughput an organization can actually absorb — nor the token-cost side of the same trade (the very $500–1000/day figure Kim cites as evidence of enthusiasm could equally be read as a resource constraint on the demand-elasticity story).
  Gene Kim on Vibe Coding - Part II | Episode 411 | Troubleshooting Agile — read next: extract-dora-2025-state-of-ai-assisted-software-development#p11
• incompleteGreene's evidence throughout is drawn almost entirely from his own two startups (InsightSquared, Outcomes4Me) plus one recent personal side project (the Android RAG proof-of-concept); no comparative case is offered of a startup or team that ignored these mindsets and what happened as a result, nor of a larger, less resource-constrained organization successfully applying the same lessons. It is left open whether 'the lessons startups have always taught us' generalize past small, high-autonomy, high-trust teams.
  Presentation: The Future of Engineering: Mindsets That Matter When Code Isn't Enough — read next: Seek accounts of these same mindsets (start simple, protect comprehension, attack hard bits first) applied — or failing to apply — inside larger, less autonomous engineering organizations.
• illogicalThe Jevons-paradox argument (Lesson 1) establishes that total demand for software may rise as code gets cheaper, but Greene slides from 'aggregate demand for software rises' to 'therefore individual software engineers remain valuable,' without addressing whether rising demand is met by more engineers, or by fewer engineers each producing proportionally more software with agents — the same substitution question other Jevons-paradox-for-jobs arguments in the corpus explicitly raise as a disanalogy.
  Presentation: The Future of Engineering: Mindsets That Matter When Code Isn't Enough — read next: See extract-karpathy-code-agents-autoresearch#g3 for the disanalogy debate (ATMs automating a narrow banking task vs. AI agents potentially automating the whole engineering task) that Greene's version of the argument does not engage with.
• incompleteThe 'more with same' pathway explicitly conditions severe margin erosion on demand being inelastic, but the piece never examines which industries have elastic vs. inelastic demand for AI-driven output. The corpus's well-documented Jevons-paradox pattern for cognitive and software work (cheaper cognition/code has historically increased total demand for it, not just made existing output cheaper) suggests demand in exactly the industries most associated with AI (software, cognitive services) may be relatively elastic — which, by the article's own stated logic, should blunt rather than accelerate margin erosion there, a distinction the piece's mostly physical-goods examples (autos, agriculture) don't surface.
  AI and the Looming Competition for Margin — read next: Corpus material applying Jevons Paradox to cognition and software engineering — cheaper cognition increasing total demand for cognitive/engineering work rather than shrinking it.
• 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.
• incompleteTreats energy as a procurement-and-deployment problem (route smaller models, sign PPAs, pick Nordic regions) rather than as a strategic constraint that should reshape AI architecture decisions — e.g., whether to deploy agentic loops at all, whether to build retrieval-heavy systems vs. cached responses, whether to build AI features whose value-per-watt is negative.
  Your Company Needs an Energy Strategy for AI's Next Phase — read next: Sasha Luccioni and Hugging Face on AI energy accounting; Andy Masley and effective-altruist energy debates; Kate Crawford 'Atlas of AI' on AI's material substrate.
• incompleteThe proposed governance body (CIO + CFO + procurement + sustainability) reproduces the assumption that AI is something the IT/procurement axis manages. It does not engage with the possibility that energy-bounded AI is a board-level capital-allocation question on par with major capex, nor that engineering leadership (CTO, head of AI) should own intelligence-per-watt as a product KPI.
  Your Company Needs an Energy Strategy for AI's Next Phase — read next: Recent CTO-track writing on AI infra strategy; capital-allocation literature on industrial transitions.
• incompleteJevons is invoked rhetorically but the article doesn't engage with the implication: if demand will always grow to consume available efficiency gains, then efficiency-focused recommendations (Action 1, Action 2) only delay the bottleneck for the individual firm without changing the aggregate trajectory. The piece offers no view on whether firms should care about aggregate trajectory.
  Your Company Needs an Energy Strategy for AI's Next Phase — read next: Climate-economics literature on rebound effects; Vaclav Smil on energy transitions.
• incompleteNo discussion of grid externalities — what happens to non-AI users and communities when hyperscalers and incumbents lock up 20-year nuclear PPAs and Nordic colocation. The framing is purely firm-strategic; the political economy is mentioned ('politically contested') but not analyzed.
  Your Company Needs an Energy Strategy for AI's Next Phase — read next: Reporting on data-center power disputes in Virginia, Ireland, and the U.S. Midwest; energy-justice literature.
• incompleteThe Great Value Loop framework is presented as a recurring pattern but the article does not interrogate whether the energy era differs structurally — physical infrastructure has 20+ year build timelines, hard geographic constraints, and political sovereignty implications that the previous three layers (connectivity, attention, compute) did not.
  Your Company Needs an Energy Strategy for AI's Next Phase — read next: History of electrification (Hughes 'Networks of Power'); literature on infrastructure lock-in and path dependence.
• uninformedDoes not engage with on-device / edge inference as an alternative architectural lever for reducing data-center electricity demand. The five actions all assume cloud-resident inference.
  Your Company Needs an Energy Strategy for AI's Next Phase — read next: Apple Intelligence and on-device LLM literature; small-language-model research from Mistral, Microsoft Phi, etc.
• incompleteThe piece never addresses that its authors are Perplexity staff studying engagement with, and the economic value of, Perplexity's own commercial assistant and agent products — a direct conflict of interest disclosed only as a parenthetical ('where the author team works') and never discussed as a limitation, even though the itemized limits paragraph addresses several other caveats in detail.
  Research: How AI Agents Broaden the Scope of Knowledge Work — read next: An independent, non-vendor field study of assistant-vs-agent usage — academic or from a researcher with no commercial stake in either product — to see whether the scope-expansion effect replicates outside a single company's own telemetry.
• illogicalThe 'second, LLM-based estimate' is framed as independent triangulation ('we didn't rely on a single method') that corroborates the primary human-equivalent-cost estimate, but both estimates were produced by the same research team using models and assumptions of their own choosing; agreement between two internally-generated estimates is much weaker evidence of accuracy than the framing implies, since it cannot rule out a bias shared by both methods.
  Research: How AI Agents Broaden the Scope of Knowledge Work — read next: A genuinely external replication — a third party re-deriving human-equivalent time/cost from comparable session logs using an independently chosen costing methodology.
• incompleteThe authors recommend routing agent work by 'complexity and verifiability' and building a human commit boundary for consequential actions, but their own scope data shows agent use crossing further outside users' primary occupational expertise over time — the exact condition that makes verification hardest — and the piece never measures whether cross-occupational agent output is actually being verified, or is simply being accepted on trust.
  Research: How AI Agents Broaden the Scope of Knowledge Work — read next: Research on whether and how non-expert users actually catch errors in AI output outside their own domain (calibration and overconfidence in cross-domain review), which the piece assumes a verification capacity for but never tests.
• uninformedThe definitions behind 'higher-order cognition,' 'create-level work,' 'abstract vs. routine,' and 'more complex' unlocked tasks are never operationalized — no coding scheme, no example task placed in each bucket, no inter-rater reliability or human-audit check — so a reader cannot judge how much of the reported scope-expansion is real behavioral shift versus an artifact of how the classifying LLM was prompted.
  Research: How AI Agents Broaden the Scope of Knowledge Work — read next: The underlying research methodology or appendix (if published separately from this HBR summary) for the actual classification scheme and any reliability checks performed.
• illogicalThe piece argues both that AI's pace of change is unlike anything the author has seen in 20+ years, and that established change-management patterns from prior technology waves (DevOps, cloud, CD) will straightforwardly transfer — without reconciling why a genuinely unprecedented shift would be well-served by a non-unprecedented adoption playbook. The claim that 'proven methodologies' suffice is asserted, not argued against the claim of novelty made two paragraphs earlier.
  Leading your engineers towards an AI-assisted future — read next: Look for arguments specifically about what, if anything, is structurally different about AI adoption that would break a Mission Command-style rollout — e.g. the rate of tool obsolescence outpacing a Community of Practice's ability to stay current.
• tensionConflates elite commentary vibeshift with actual public narrative change. The piece acknowledges polls remain negative but does not reconcile this with its thesis that a meaningful shift is underway.
  Is AI Doom Going Out of Style?
• omissionThe Anthropic revenue numbers ($9B to $44B ARR) are presented without questioning sustainability or margin structure beyond the current moment. Revenue growth rate =/= sustainable business.
  Is AI Doom Going Out of Style?
• underdevelopedKlein's insight that 8 million displaced workers may be harder to help than 80 million is introduced but not developed. This is potentially the most important policy point in the piece and gets one sentence.
  Is AI Doom Going Out of Style?
• incompleteAuto research is demonstrated only on hyperparameter optimization — a narrow, well-defined search with clear metrics. No evidence or discussion of how it would work on research requiring genuine novelty, taste, or paradigm shifts.
  Skill Issue: Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI — read next: Literature on AI-driven scientific discovery beyond optimization: can agents formulate novel hypotheses, not just test existing ones? See work on AI scientists (e.g., Sakana AI's 'The AI Scientist').
• uninformedThe Jevons paradox argument for software engineering jobs is applied without discussing the significant disanalogies: ATMs automated a narrow task within banking, while AI agents potentially automate the entirety of software production. Demand elasticity analysis is hand-waved.
  Skill Issue: Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI — read next: Economic analysis of AI labor displacement that distinguishes between task-level automation (Jevons applies) and occupation-level automation (potentially different dynamics). See Acemoglu vs Brynjolfsson debate.
• incompleteSecurity and trust implications of the 'apps dissolve into APIs' vision are acknowledged but not seriously explored. Dobby discovered unprotected Sonos APIs — this is a security vulnerability presented as a feature.
  Skill Issue: Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI — read next: Research on agent-to-API security models, OAuth for agents, and the attack surface of agent-mediated home automation.
• illogicalKarpathy simultaneously claims (1) everything is a skill issue and current tools are sufficient, and (2) models are jagged, burst at the seams, and going too far ahead makes them net negative. These are in tension — if the tools truly suffice, jaggedness should be manageable.
  Skill Issue: Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI — read next: Framework for distinguishing 'skill issues' from genuine capability boundaries in AI agent systems.
• 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.
• incompleteNo discussion of whether hyper-personalized software eventually re-converges on the same feature bloat it's positioned as an antidote to — especially once Glaze's own remix/sharing features (taking someone else's app and adjusting it) let personal apps spread and accumulate features the way commercial SaaS did.
  The case for making your own apps — read next: Corpus material on Jevons paradox and feature-bloat cycles in software.
• incompleteThe 'you don't get to create anything' extension to AI is never made explicit
  You Don't Get to Create Anything (Still Burning: Beck + Randy Shoup on Jevons paradox and distributed systems under AI) — read next: Beck's own Substack on 'starving genies' and the human's relationship to the genie's outputs; Cal Newport's deep-work tradition on how the substitution of tool-mediated production changes the nature of the work; Pressfield on Resistance; and — the persona-relevant cross-thread — the 'fragility of ideas' hidden layer in Kjetil's reading: if the maker-compulsion is the durable substrate, what does it look like when the agent satisfies it without you having moved? This is the productive question the episode leaves on the table.
• incompleteThe Jevons-on-cognition forecast is asserted, not argued through to its qualitative differences
  You Don't Get to Create Anything (Still Burning: Beck + Randy Shoup on Jevons paradox and distributed systems under AI) — read next: Carl Benedikt Frey 'The Technology Trap' on the political economy of transitional displacement; Brad DeLong on what 'creating new categories of work' has actually looked like historically; Anton Korinek and Daron Acemoglu on AI-and-labor specifically. For the contrarian side: Erik Brynjolfsson on the J-curve (gains that show up late and unevenly); the Robin Hanson 'Age of Em' tradition for the qualitative-difference-of-cognition objection. Persona-relevant: DORA 2025 on AI's actual code-quality effects.
• incompleteThe playbook claim under-specifies the AI playbook's structure
  You Don't Get to Create Anything (Still Burning: Beck + Randy Shoup on Jevons paradox and distributed systems under AI) — read next: Hamel Husain on evals; Simon Willison on the working LLM-augmented practitioner's actual playbook; Addy Osmani and the Claude Code patterns crowd; the Bernhard / Bockeler / Thoughtworks harness-engineering thread already in this repo; the Heis ep 11 AI-special with Vilde Opsal and Anders Sveen for the Norwegian practitioner cut; the Heis ep 13 Torrissen for code-quality-under-AI. This is exactly where 'craft transmission for the next era' lives — and where the conversation is most incomplete.
• incompleteThe 'apprenticeship channel under 50:1 manager ratios' problem from the prior Still Burning episode is not picked up
  You Don't Get to Create Anything (Still Burning: Beck + Randy Shoup on Jevons paradox and distributed systems under AI) — read next: John Cutler on overload and DRI dynamics; Will Larson and Tanya Reilly on engineering-org structure; the Heis ep 13 Torrissen extraction for the Norwegian practitioner take on code-quality and craft transmission under AI. Persona-relevant: the 'fragility of ideas' layer — ideas die at the institutional adoption point if the apprenticeship channel is gone.
• incompleteShoup's 'make it easy, don't shame' principle is stated as climate policy and not applied to software craft
  You Don't Get to Create Anything (Still Burning: Beck + Randy Shoup on Jevons paradox and distributed systems under AI) — read next: Cutler on operating-system design and waste; Thaler and Sunstein on choice architecture for the foundational behavioral-economics text; Kniberg on Spotify's model as an example of friction-reduction at scale. Persona-relevant: this is exactly where Kjetil's leadership-vs-management orientation and his Cutler-deep reading already live.
• incompleteThe 'distributed-systems-playbook-writers as exemplars' framing implicitly leaves out the playbook that didn't get written
  You Don't Get to Create Anything (Still Burning: Beck + Randy Shoup on Jevons paradox and distributed systems under AI) — read next: Schneier on security (in Kjetil's RSS); the Ruined by Design / Mike Monteiro line on what professionals fail to write playbooks for; the Norwegian intelligence reports (NSM, PST) for the security-and-resilience perspective; recent regulatory writing on AI-and-governance for what is being written by non-engineers because engineers didn't write it.
• incompleteThe Beck-as-inductive vs. Shoup-as-deductive split is named but not connected to the playbook-writing claim
  You Don't Get to Create Anything (Still Burning: Beck + Randy Shoup on Jevons paradox and distributed systems under AI) — read next: Anything on creative-cognitive partnerships at scale: Brian Eno's 'scenius' concept, Anne Lamott on the writer-editor pairing, the literature on the pair-programming and mob-programming traditions in software. For the temperament-and-craft thread specifically: Beck's own writing on the craft of seeing simple design where others see complexity.