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Compounding Waves - How Each Tech Era Built the Substrate, and the Skills, for the Next
The compounding economic logic of three successive technology waves from January 1995 to May 2026 - internet disintermediation of distribution, software-defined platforms and cloud infrastructure, and the current AI/agentic systems wave - examining the technical, economic and human-skills dependencies that make each wave a precondition for the next, the new categories of work each wave created, and whether the relationship is best understood as cumulative compounding or as externalised costs harvested by later layers.
- Claude Opus 4.8
- financial
- academic
- blogs
- vc
Synthesised 2026-05-11
The Compounding Logic of Three Technology Waves, 1995–2026
Overview
Three technology waves stack on top of each other in a way that is now impossible to ignore. The internet wave (1995–2005) built payment rails, web standards and the habit of publishing human knowledge openly. The cloud wave (2005–2015) turned that open web into indexable, abstractable infrastructure and built the hyperscale compute, containerisation and API conventions that made frontier model training economically feasible. The current AI wave consumes both: it trains on wave-one content and runs on wave-two infrastructure. The dependency is not metaphorical. Amazon could fund Anthropic because AWS generated the capital and the compute, and Anthropic can train models because AWS built the hyperscale layer. Sources: Stratechery (Ben Thompson) (2026) (↗); Big Data & Society (2024) (↗)
The defining shift of the past 18 months is that the substrate stopped being free. Wave-one content was an unpriced externality for two decades. Between 2023 and 2026, that externality began to be internalised through litigation (NYT and Reddit suits), API monetisation (Reddit, Stack Overflow), licensing deals (OpenAI's $60 million Reddit agreement) and technical enforcement (robots.txt, Cloudflare bot management). Longpre et al. found AI-crawling restrictions on critical web sources rose more than 500% in under a year. Sources: arXiv / NeurIPS 2024 Datasets and Benchmarks Track (2024) (↗); SiliconANGLE (2024) (↗); CNBC (2025) (↗)
The second shift is the scale of capital being committed against contested returns. Bloomberg Intelligence projects more than $3.5 trillion in AI capex through 2030, with the four largest US hyperscalers on track for $650–700 billion in 2026 alone. Yet an NBER survey of executives found AI usage averaging 1.5 hours per week, and Apollo's Torsten Slok captured the mood: AI is everywhere except in the macroeconomic data. Sources: CoStar (citing Bloomberg and company filings) (2026) (↗); Fortune (2026) (↗)
Two frames compete for the relationship between the waves: compounding dividend (earlier waves genuinely enable later ones, gains will appear) versus externalised harvest (later waves extract uncompensated value from earlier ones). The evidence supports both simultaneously, which is exactly why the debate will not resolve cleanly.
Timeline
- Open-web publishing norm established
- "Software is eating the world" thesis
- Scaling laws formalised
- Chinchilla data-proportionality laws
- Goldman 7% GDP estimate
- Sequoia's $200B question
- Reddit API monetisation begins
- NYT v OpenAI litigation
- Reddit licensing deals
- Consent in Crisis audit
- Goldman "Too Much Spend?"
- Sequoia's $600B question
- METR time-horizon finding
- Wikimedia AI-crawler cost alarm
- Gartner agentic supply-exceeds-demand
- Bain $2T revenue gap
- Off-balance-sheet financing documented
- Hyperscaler capex near doubling
- NBER 1.5hr/week usage
- Brookings early-career displacement synthesis
Key Findings
The dependency chain is concrete, not rhetorical. Kaplan et al. established that model performance scales as a power law with compute, data and parameters; Hoffmann et al.'s Chinchilla work showed data must scale proportionally with model size. Together they make the open web a load-bearing input rather than a convenience. The wave-two artefacts (CUDA, PyTorch, Kubernetes, hyperscale storage, AWS/Azure/GCP) are the direct substrate for training and inference. Andreessen's 2011 observation that application hosting costs fell from $150,000 to $1,500 per month between 2000 and 2011 describes the same cost-curve compression now being applied to GPU inference. Sources: arXiv (OpenAI) (2020) (↗); arXiv (DeepMind) (2022) (↗); Andreessen Horowitz (a16z) (2011) (↗)
Training data is the load-bearing wave-one dependency, and it is being walled off. The data commons that fed frontier models (Common Crawl indexing back to 1996, Wikipedia, GitHub, Stack Overflow, Reddit) was produced under open-publishing norms with no AI monetisation in mind. Longpre et al.'s audit of 14,000 domains found over 28% of the most critical C4 sources now fully restricted. A 2025 follow-up shows moderate news sites withdrawing first, leaving hyperpartisan material over-represented. The Wikimedia Foundation reported 65% of its most expensive traffic now comes from AI crawlers. Sources: arXiv / NeurIPS 2024 Datasets and Benchmarks Track (2024) (↗); arXiv (2025) (↗); arXiv (2025) (↗)
Enclosure has created a two-tier data economy. OpenAI and Google licensed Reddit legally; Reddit then sued Anthropic and Perplexity for accessing the same public corpus. Reddit's chief legal officer described an "industrial-scale data laundering economy". This is the clearest illustration of the externalised-harvest frame: well-capitalised incumbents pay, smaller entrants litigate, and the original creators receive neither compensation nor attribution. Sources: CNBC (2025) (↗); Law360 / Troutman Pepper Locke (2025) (↗); MediaNama (2025) (↗)
The productivity paradox maps onto a structural disagreement about diffusion. Goldman's 2023 research estimated 7% GDP uplift and a 1.5-point annual US productivity gain; Acemoglu, featured in Goldman's own 2024 counter-report, puts realistic TFP gain at 0.5–0.6% over a decade. McKinsey found 88% of organisations using AI but only 6% qualifying as high performers with measurable EBIT impact. Brynjolfsson's J-Curve framework predicts gains arrive only after large, invisible complementary investments, consistent with the twenty-year lag seen in the electricity wave. Sources: Goldman Sachs (2023) (↗); Fortune (2024) (↗); McKinsey Global Institute (2025) (↗)
Labour effects are real but concentrated at the entry level. Brookings' 2026 synthesis found no aggregate displacement through 2024–2025 but a 16% employment decline for workers aged 22–25 in AI-exposed occupations, which Brynjolfsson, Chandar and Chen call "canaries in the coal mine". The Harvard Business School study found a 24% quarterly reduction in automatable skills per firm post-ChatGPT against a 15% rise in augmentation-exposed roles, with the positive figure concentrated in mid-to-high-skilled work. Sources: Brookings Institution (2026) (↗); Harvard Business School Working Paper (2024) (↗)
Each wave invents its own technical intermediaries, and their wage premiums diffuse away. The pattern runs from webmaster and e-commerce merchandiser through cloud architect, SRE and data scientist to today's prompt engineers, ML engineers, AI-ethics specialists and what McKinsey calls "business–AI translators". Gartner projects 80% of engineering workers will need upskilling by 2027. The historical regularity is that scarcity confers a brief premium before skills commoditise, which Willison's documentation of GPT-4-class capability reaching 18 organisations illustrates in real time. Sources: McKinsey Global Institute (2025) (↗); Simon Willison's Weblog (2024) (↗)
Capital concentrates at the layer controlling the binding constraint, and the financing is moving off balance sheet. CB Insights found AI reached 48% of total venture funding in 2025 ($226B of $469B), with six rounds accounting for 49% of all AI capital. The "Hype, Sustainability" paper found 80–90% of early-stage AI startup capital flows back to cloud providers. Man Group documented hyperscalers moving liabilities into SPVs, private credit and data-centre REITs, with at least $178.5 billion in US data-centre credit deals in 2025. Sources: CB Insights (2026) (↗); arXiv (2024) (↗); Man Group (2025) (↗)
Benchmark progress and real-world productivity have diverged. METR's task suites show AI task-completion horizons doubling every seven months since 2019, with Claude 3.7 Sonnet hitting 50% success on tasks that take human experts roughly 50 minutes. Yet METR's 2025 field study found AI tools made experienced open-source developers 19% slower on their own repositories. The gap appears when tasks involve codebase familiarity, ambiguous requirements and long feedback loops, which is precisely where enterprise value sits. Sources: arXiv / METR (2025) (↗); arXiv / METR (2025) (↗)
The next binding constraint is physical, not informational. Bain's 2025 report projects a $800 billion shortfall in capital required to fund anticipated compute demand even after redirecting all on-premise IT budgets and reinvesting all AI savings. CB Insights recorded nuclear energy investment tracking toward $5 billion annually, driven entirely by hyperscaler power demand. The constraint is migrating from data to energy and physical infrastructure. Sources: Bain & Company (2025) (↗); CB Insights (2025) (↗)
Evidence & Data
The capital figures are the firmest numbers in the sweep. Hyperscalers are committed to $3.5 trillion-plus through 2030, with $650–700 billion in 2026 and Alphabet's guide now outpacing trailing free cash flow. Sources: CoStar (citing Bloomberg and company filings) (2026) (↗)
The productivity estimates span an order of magnitude: Goldman's 7% GDP and 1.5-point productivity figures against Acemoglu's 0.5–0.6% TFP over a decade. The adoption-versus-impact split is stark: McKinsey's 88% using AI against 6% high performers, and Bain's finding that leaders embedding AI in specific workflows reached 10–25% EBITDA gains. Sources: Goldman Sachs (2023) (↗); American Enterprise Institute (summarising Goldman Sachs research) (2024) (↗); Bain & Company (2025) (↗)
The revenue-justification gap has widened on schedule: Sequoia's $200B question (2023) became $600B (2024), and Bain sized the 2030 requirement at $2 trillion against 200 gigawatts of compute demand. Sources: Sequoia Capital (2023) (↗); Sequoia Capital (2024) (↗); Bain & Company (2025) (↗)
On data, the crawler-restriction surge (500%+ in under a year, 28%+ of critical C4 sources restricted) and Wikimedia's 65% AI-crawler traffic figure are the load-bearing empirical points. Willison's 100x inference cost drop between 2022 and 2025 anchors the commoditisation case. Sources: arXiv / NeurIPS 2024 Datasets and Benchmarks Track (2024) (↗); Simon Willison's Newsletter (Substack) (2026) (↗)
Signals & Tensions
Gartner downgraded its own top trend. Having named agentic AI the top strategic trend for 2025, Gartner warned in October 2025 that supply exceeds demand and a correction looms, while still projecting agentic AI could drive $450B+ in enterprise software revenue by 2035. A vendor-aligned analyst issuing a sceptical signal carries more weight than the same claim from a non-vendor. Sources: Gartner (2024) (↗); Gartner (2025) (↗)
Substrate-becoming versus application-staying is underexplored. The internet, cloud and AI compounded because each lowered the fixed costs of the next wave's general-purpose operations. Mobile, IoT, blockchain and VR largely stayed application-layer because they served narrower use cases. This distinction is asserted across lanes but rarely tested rigorously.
The blog lane has a structural incentive problem. Writers who benefit from AI tools (Willison, Thompson) lean toward the compounding-dividend frame; the strongest harvest arguments come from those whose prior output sits in training corpora uncompensated. Willison's Jevons-paradox framing (cheaper cognition generates more demand) is the cleanest dividend statement, but it is also self-interested. Sources: Simon Willison's Newsletter (Substack) (2026) (↗); Augmented Mind (Substack) (2025) (↗)
The O-ring model cuts against simple displacement. Gans–Goldfarb's framework, discussed by Bewick, argues automating easy tasks concentrates human effort on harder bottleneck tasks, challenging the task-separability assumption behind most exposure indices. It neither validates the dividend story nor confirms wholesale displacement. Sources: Tom Bewick (Substack) (2026) (↗)
Synthetic data is unproven as a substitute. The scaling-laws-of-synthetic-data work suggests it can help, but LessWrong contributors converge on a 2026–2032 window when general-purpose internet training data runs dry. Whether wave three is the last to consume wave one's externality for free is genuinely open. Sources: arXiv (2025) (↗); LessWrong (2025) (↗)
Open Questions
How long is the J-Curve trough? Brynjolfsson predicts gains arrive after a diffusion lag, but no source resolves which wave-specific conditions set its length. The electricity analogy (twenty years) and the PC-internet analogy (a decade) give very different forecasts. Sources: Goldman Sachs Global Investment Research (2024) (↗)
Who pays for the next substrate? With Bain's $800 billion shortfall and financing moving into SPVs and private credit, the risk is being transferred to institutions that do not see themselves as betting on GPU cycles. Whether this holds under stress is untested. Sources: Bain & Company (2025) (↗); Man Group (2025) (↗)
Does the entry-level effect generalise? Brookings' 16% decline for 22–25-year-olds in exposed occupations is early-career-concentrated. Whether this is a leading indicator of broader displacement or a one-off compositional shift is unresolved. Sources: Brookings Institution (2026) (↗)
Will wave-three roles absorb workers at wave-one and wave-two rates? The literature splits on automation versus augmentation. The augmentation figure concentrates in mid-to-high-skilled roles, leaving the low-skilled displacement question open. Sources: arXiv (2025) (↗); arXiv (2024) (↗)
Why does benchmark progress diverge from field productivity? METR's seven-month doubling and its 19%-slower field finding cannot both be the whole story. Reconciling them is the central empirical task. Sources: arXiv / METR (2025) (↗)
Can the externality be priced without entrenching incumbents? Licensing internalises the harvest but may simply concentrate advantage among those who can afford it. Whether litigation produces compensation for creators or merely a tollbooth for incumbents is unsettled. Sources: Law360 / Troutman Pepper Locke (2025) (↗)
Is the IMF right that statistics both overstate and understate AI? Official figures record capex but miss spillovers. Until measurement catches up, every productivity claim rests on contested foundations, and the gap is wide enough to hide either a bubble or a delayed dividend. Sources: IMF Finance & Development (2026) (↗)
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Sources
Summary: ↑ Back to summary
Financial Press
| ID | Title | Outlet | Date | Significance |
|---|---|---|---|---|
| f1 | AI Accelerator Market Looks Set to Exceed $600 Billion by 2033, Driven by Hyperscale Spending and ASIC Adoption | Bloomberg Intelligence | 2026-01 | Provides authoritative market sizing for the AI infrastructure layer, projecting $3.5 trillion in hyperscaler capital expenditure through 2030 and tracing the shift from GPU to custom ASIC architectures as the compounding substrate of wave three. |
| f2 | Global AI Data Center Dominance Shifts Away From Big Tech | Bloomberg | 2025-12 | Documents the structural financialisation of AI infrastructure - over $178 billion in US data-centre credit deals in 2025 alone - and the entry of inexperienced operators into the buildout, raising systemic risk questions central to the 'who pays for the next substrate' question. |
| f3 | Where enterprise data is headed in 2026 | Bloomberg Professional Services | 2025-12 | Based on Bloomberg's own Enterprise Tech and Data Summit, this piece marks the transition from AI experimentation to enterprise-wide adoption in financial institutions - a key indicator of where wave three value is beginning to accrue. |
| f4 | Gen AI: Too Much Spend, Too Little Benefit? | Goldman Sachs Global Investment Research | 2024-06 | The most-cited financial-press intervention on the AI productivity paradox, assembling both the bull case (6.1 percent GDP uplift) and the Acemoglu counter-case (0.9 percent), and naming the conditions under which the trillion-dollar infrastructure bet could fail to generate returns. |
| f5 | Generative AI Could Raise Global GDP by 7% | Goldman Sachs | 2023-04 | The foundational Goldman Sachs productivity forecast - 1.5 percentage-point annual productivity uplift, $7 trillion GDP gain - that anchored the financial-press bull case for wave three and remains the benchmark against which sceptical evidence is measured. |
| f6 | Generative AI: Hype, or Truly Transformative? | Goldman Sachs Global Investment Research | 2023-07 | Goldman's early framing of AI as 'Software 3.0', explicitly comparing the wave to the PC-internet productivity boom of 1996–2005 and warning that investor timetables for returns typically exceed reality - directly relevant to the compounding-wave thesis. |
| f7 | Hyperscalers' $680 Billion AI Capital Expenditure Investment Raises the Stakes | CoStar (citing Bloomberg and company filings) | 2026-02 | Detailed breakdown of each hyperscaler's capital programme and free-cash-flow gap for 2026, showing that capex is outpacing internal cash generation and forcing debt issuance - the clearest available data point on who is financing the current substrate layer. |
| f8 | The Magnificent Capex: AI Infrastructure Spending and Who Actually Benefits | Ferguson Wellman Capital Management | 2026-05 | Frames the AI capex cycle in three layers - builders, enablers, adopters - and highlights that Amazon, Alphabet, Microsoft and Meta will collectively spend $650–700 billion on capex in 2026 alone, nearly double 2025, with significant cost-inflation embedded in that figure. |
| f9 | 2025: The State of Generative AI in the Enterprise | Menlo Ventures | 2025-12 | Provides the most granular demand-side data available: enterprise AI spend grew from $1.7 billion to $37 billion since 2023, coding became the first genuine 'killer use case', and 50 percent of developers now use AI tools daily - directly mapping value-creation to the wave-three application layer. |
| f10 | AI Investment and Deal Trends: Global Report H1 2025 | Ropes & Gray | 2025-08 | Documents the M&A and investment landscape for the first half of 2025, including Microsoft, Alphabet, Amazon and Meta committing a combined $320 billion to AI infrastructure - and Morgan Stanley's framing of agentic AI as 'the next frontier' for enterprise value capture. |
| f11 | AI Can Lift Global Growth | IMF Finance & Development | 2026-03 | IMF analysis showing that hyperscaler valuations have been rewarded by equity markets 'unseen since the dot-com era', and that GDP data simultaneously overstates AI's immediate capital contribution while understating its productivity spillovers - directly relevant to the intangible-capital measurement problem. |
| f12 | Artificial Intelligence and Productivity in Europe | IMF Working Paper | 2025 | Cross-country econometric analysis comparing Acemoglu's conservative productivity estimates with McKinsey and Goldman Sachs projections, with specific attention to why higher-wage, financial-services-heavy economies are disproportionately exposed - a critical input for the wave-three value-capture debate. |
| f13 | The Next Phase of AI: Technology, Infrastructure, and Policy in 2025–2026 | American Action Forum | 2026-04 | Documents the regulatory and policy dimension of the AI infrastructure buildout, including the projection that agentic AI will represent 10–15 percent of IT spending by 2026 - connecting the technical dependency chain to emerging enterprise spending patterns. |
| f14 | Thousands of CEOs Admit AI Had No Impact on Employment or Productivity | Fortune | 2026-04 | Synthesises an NBER survey of 6,000 executives alongside Apollo chief economist Torsten Slok's 'AI is everywhere except in the macroeconomic data' observation, and cites a Financial Times analysis showing 374 S&P 500 companies claiming positive AI adoption without aggregate productivity evidence. |
| f15 | AI's Economic Potential: Goldman Sachs Responds to Daron Acemoglu | American Enterprise Institute (summarising Goldman Sachs research) | 2024-06 | The clearest head-to-head presentation of the Acemoglu (0.5 percent TFP, 1 percent GDP) versus Goldman Sachs (9 percent productivity, 6.1 percent GDP) forecasts, illuminating the empirical assumptions - task share, automation cost savings, labour reallocation - that separate the two positions. |
| f16 | Markets Have Overestimated AI-Driven Productivity Gains, Says MIT Economist | Fortune | 2024-08 | Daron Acemoglu writing directly in the financial press, arguing that Total Factor Productivity growth from the full AI suite is likely 0.5–0.6 percent over ten years - the anchor citation for sceptical financial commentary on whether wave three can justify its infrastructure spend. |
| f17 | Productivity Paradox | American Enterprise Institute | 2025-05 | Applies Brynjolfsson, Rock and Syverson's 'Productivity J-Curve' framework to current AI adoption data, including a Wall Street Journal story on IBM's Arvind Krishna - AI reduced headcount in HR but freed capital for new hires in engineering and sales, illustrating the intra-wave role-creation dynamic. |
| f18 | Research on AI and the Labor Market Is Still in the First Inning | Brookings Institution | 2026-03 | Comprehensive literature review from Brookings, synthesising Brynjolfsson, Chandar and Chen's ADP payroll finding (16 percent employment decline for workers aged 22–25 in AI-exposed occupations) against studies showing no aggregate displacement - directly relevant to the intra-wave skills and role-creation question. |
| f19 | AI, Productivity, and Labor Markets: A Review of the Empirical Evidence | International Center for Law and Economics | 2026-02 | Systematic review of the empirical literature through 2025, finding no economy-wide displacement but concentrated entry-level effects and task reallocation - the most current synthesis of what the labour-economics evidence actually shows at the start of wave three. |
| f20 | Goldman Sachs Rolls Out an AI Assistant for Its Employees | CNBC | 2025-01 | Reports Bloomberg Intelligence's estimate that global investment banks may shed 200,000 jobs in three to five years, alongside Goldman CIO Marco Argenti's 'amplified workforce' counter-framing - a direct illustration of the divergence between corporate AI rhetoric and the financial sector's own research on displacement. |
| f21 | OpenAI Agrees to Deal with Reddit to Scrape Its Content for AI Training | SiliconANGLE | 2024-05 | Documents the $60 million OpenAI-Reddit licensing deal - the first major commercial enclosure of a wave-one open-web data source - establishing the precedent that wave-three models must now pay for what they previously harvested for free. |
| f22 | Reddit Accuses Perplexity of Stealing User Posts, Expanding Data Rights Battle with AI Industry | CNBC | 2025-10 | Primary news report on Reddit's October 2025 lawsuit against Perplexity, documenting the 'arms race for quality human content' and the two-tier licensing system that now gives well-capitalised incumbents structural advantages in accessing wave-one training data. |
| f23 | New York Times Sues Perplexity AI for Copyright Infringement | MediaNama | 2025-12 | Reports the NYT's December 2025 suit against Perplexity, extending the copyright-and-training-data legal front opened by the original NYT v. OpenAI action and illustrating how wave-one content producers are actively closing the externality that wave-three depends on. |
| f24 | The Future of Gen AI Training Amid Reddit Data Scraping Suit | Law360 / Troutman Pepper Locke | 2025-12 | Legal analysis noting that the Wall Street Journal reported OpenAI's 2025 losses could reach $74 billion, setting up the tension between escalating content-licensing costs and AI labs' existing financial losses - a concrete framing of the 'externalised harvest' problem. |
| f25 | The AI Bubble: Hidden Risks and Opportunities | Man Group | 2025-11 | Institutional investor analysis arguing that GPU economic life of approximately one year creates a fundamental mismatch between short-duration assets and long-duration debt financing - and that risk is migrating from tech balance sheets into utilities, insurers, pension funds and private credit, raising systemic questions about who ultimately pays for the AI substrate. |
Academic & arXiv
| ID | Title | Outlet | Date | Significance |
|---|---|---|---|---|
| a1 | Scaling Laws for Neural Language Models | arXiv (OpenAI) | 2020-01 | Established power-law relationships between model performance and compute, data, and parameters, providing the empirical foundation for the hyperscale training regime that defines the AI wave's infrastructure dependency. |
| a2 | Training Compute-Optimal Large Language Models (Chinchilla) | arXiv (DeepMind) | 2022-03 | Revised compute-optimal training ratios, demonstrating that frontier models require data to scale proportionally with parameters, making data supply a binding constraint co-equal with compute. |
| a3 | Consent in Crisis: The Rapid Decline of the AI Data Commons | arXiv / NeurIPS 2024 Datasets and Benchmarks Track | 2024-07 | First large-scale longitudinal audit of 14,000 web domains showing that in a single year (2023–2024) robots.txt and Terms of Service restrictions rose 500%+, directly measuring the closure of the open-web externality that wave-three AI consumed for free. |
| a4 | RE-Bench: Evaluating Frontier AI R&D Capabilities of Language Model Agents Against Human Experts | arXiv / METR | 2024-11 | METR's benchmark comparing AI agents with 61 human experts on ML research engineering tasks, providing the primary empirical evidence on how far agentic systems have advanced toward automating the software-engineering labour that underpins the cloud and AI waves. |
| a5 | HCAST: Human-Calibrated Autonomy Software Tasks | arXiv / METR | 2025-03 | METR's 189-task benchmark for measuring autonomous AI capabilities across ML, cybersecurity, software engineering, and general reasoning, used as the primary instrument for tracking the 7-month doubling time of AI task-completion horizons. |
| a6 | Measuring AI Ability to Complete Long Tasks | arXiv / METR | 2025-03 | Introduces the 50%-task-completion time horizon metric, showing frontier AI models doubled their effective task length every seven months since 2019 and extrapolating this to agent-level software autonomy within a decade. |
| a7 | Crashing Waves vs. Rising Tides: Preliminary Findings on AI and Labor Markets | arXiv | 2026-04 | Empirically distinguishes whether AI capability gains arrive in abrupt bursts for specific tasks ('crashing waves') or as broad parallel shifts across task duration ('rising tides'), with direct implications for which occupational categories face displacement and when. |
| a8 | Augmenting or Automating Labor? The Effect of AI on Employment and Wages | arXiv | 2025-03 | Distinguishes automation AI from augmentation AI using US labour-market data (2015–2022), finding displacement effects outweigh productivity gains for low-skilled occupations and that automation exposure negatively affects new-work creation. |
| a9 | Complement or Substitute? How AI Increases the Demand for Human Skills | arXiv | 2024-12 | Uses 65,000+ job-posting websites (2018–2023) to show AI produces both substitution and complementarity effects on skill demand, with spillover effects reaching workers not directly interfacing with AI systems. |
| a10 | Artificial Intelligence, Automation and Work | NBER / SSRN (MIT Working Paper) | 2018-01 | Acemoglu and Restrepo's foundational task-based framework showing automation displaces labour from tasks machines can perform, establishing the theoretical scaffold for all subsequent empirical AI labour-market research. |
| a11 | A Task-Based Approach to Inequality | Oxford Open Economics | 2024 | Acemoglu and Restrepo's synthesis of task-displacement theory applied to AI, arguing that automation reduces labour share and may depress wages unless counterbalanced by creation of new labour-intensive tasks - the key 'this time is different' test. |
| a12 | Job Transformation, Specialization, and the Labor Market Effects of AI | Working paper (NBER-affiliated) | 2024 | Formal model projecting LLM-induced automation onto heterogeneous workers, finding wages drop up to 35% in the most exposed occupations while rising roughly 4% at moderate exposure, with AI raising returns to social and non-routine manual skills. |
| a13 | Automation and Augmentation: Artificial Intelligence, Robots, and Work | Annual Review of Sociology | 2024 | Comprehensive literature review confirming displacement effects from task automation persist while noting that automation efficacy does not increase monotonically, and that policy intervention is required to prevent widening inequality. |
| a14 | AI and the Future of Work: A Literature Review | arXiv | 2024-08 | Synthesises the labour-economics consensus, noting Acemoglu's estimate of only 0.71% TFP gain from AI over ten years contra Goldman Sachs' 7% GDP uplift, illustrating the wide empirical disagreement on net job creation vs displacement. |
| a15 | Platform Competition in Two-Sided Markets | Journal of the European Economic Association | 2003 | Rochet and Tirole's foundational two-sided-market model explaining how internet platforms court buyers and sellers simultaneously, providing the theoretical basis for understanding how wave-one disintermediation created structural preconditions for wave-two platform economics. |
| a16 | Platform Power in AI: The Evolution of Cloud Infrastructures in the Political Economy of Artificial Intelligence | Internet Policy Review | 2024 | Empirical analysis of AWS, Azure, and Google Cloud trajectories from 2017 to 2021, tracing how hyperscalers operationalise infrastructural power through the cloud-to-AI dependency chain. |
| a17 | Big AI: Cloud Infrastructure Dependence and the Industrialisation of Artificial Intelligence | Big Data & Society | 2024 | Documents how Amazon, Microsoft, and Google use cloud credits, APIs, and technical support to enrol AI startups into their infrastructure stacks, making hyperscaler lock-in the primary mechanism of value capture from the AI wave. |
| a18 | Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI | arXiv | 2024-09 | Documents the circular capital structure where three hyperscalers contributed two-thirds of the $27 billion raised by AI startups in 2023, and applies Jevons' Paradox to explain why efficiency gains from scaling increase rather than reduce overall resource consumption. |
| a19 | Scaling Laws of Synthetic Data for Language Models | arXiv | 2025-03 | Examines whether synthetic data can substitute for organic web-scraped corpora as natural language supply approaches saturation, testing whether scaling laws hold when training tokens are model-generated rather than human-produced. |
| a20 | Web Crawler Restrictions, AI Training Datasets and Political Biases | arXiv | 2025-10 | Shows that increased robots.txt restrictions by moderate news sources push AI training corpora toward hyperpartisan content, linking data-access restrictions to downstream bias in the political composition of training sets. |
| a21 | Somesite I Used to Crawl: Awareness, Agency and Efficacy in Protecting Content Creators from AI Crawlers | ACM Internet Measurement Conference 2025 | 2025 | Active and passive measurement study of AI crawler behaviour across popular sites, finding that 50–70% of website traffic is now automated and that crawlers do not reliably respect robots.txt directives. |
| a22 | Generative AI and the Future of the Digital Commons | arXiv | 2025-08 | Frames the foreclosure of open web data through an Ostrom commons lens, documenting that 65% of Wikimedia's most expensive traffic now originates from AI crawlers and analysing governance frameworks for distinguishing search, archival, and training crawlers. |
| a23 | A Critical Analysis of the Largest Source for Generative AI Training Data: Common Crawl | ACM FAccT 2024 | 2024-06 | Provides a structural critique of Common Crawl as AI training infrastructure, examining governance, funding ties to AI labs, and the copyright and accountability gaps embedded in its data pipeline. |
| a24 | Displacement or Complementarity? The Labor Market Effects of Generative AI | Harvard Business School Working Paper | 2024 | Finds a 24% decrease in generative AI-exposed skills per firm per quarter in highly automatable jobs post-ChatGPT, against a 15% increase in augmentation-exposed jobs, providing the most granular job-posting evidence on the dual displacement-complementarity dynamic. |
| a25 | Artificial Intelligence, Domain AI Readiness, and Firm Productivity | arXiv | 2025-08 | Examines why many firms fail to realise AI productivity returns despite heavy investment, finding that domain AI readiness - quality of external academic and data infrastructure - is a stronger predictor than internal technical capability alone. |
Blogs & Independent Thinkers
| ID | Title | Outlet | Date | Significance |
|---|---|---|---|---|
| b1 | Aggregation Theory | Stratechery (Ben Thompson) | 2015-07 | Foundational framing of how internet-era zero marginal distribution costs restructured industry power from supply control to demand aggregation, directly explaining the economic logic of wave one's disintermediation. |
| b2 | Enterprise Philosophy and the First Wave of AI | Stratechery (Ben Thompson) | 2024-08 | Traces technology waves from mainframe digitisation through SaaS to AI, examining how enterprise adoption patterns and job displacement repeat structurally across each transition, with Salesforce as the hinge between wave one and two. |
| b3 | Agents Over Bubbles | Stratechery (Ben Thompson) | 2026-03 | Argues that agentic AI is not a bubble because each agent multiplies compute demand rather than substituting for it, and that the economic imperative to deploy agents will drive both workforce restructuring and hyperscaler capex compounding. |
| b4 | AI Integration and Modularization | Stratechery (Ben Thompson) | 2024-06 | Applies Christensen and Coase integration logic to the AI stack, showing why value accumulates at integrated layers rather than modular ones and explaining why Google's chip-to-model vertical stack differs structurally from AWS's marketplace approach. |
| b5 | AI and the Big Five | Stratechery (Ben Thompson) | 2023-01 | Maps which incumbent hyperscalers are positioned to capture AI value and which are threatened, drawing the explicit parallel between cloud infrastructure incumbency and AI layer incumbency. |
| b6 | AI Promise and Chip Precariousness | Stratechery (Ben Thompson) | 2025-04 | Traces the semiconductor dependency chain from Silicon Valley's founding through TSMC to current AI compute, arguing that wave three's binding constraint is geopolitical control of chip fabrication rather than software. |
| b7 | The End of Aggregation Theory and AI Economics (homepage synthesis) | Stratechery (Ben Thompson) | 2026-03 | Synthesises the claim that AI reintroduces marginal costs and ends the zero-marginal-cost era that Aggregation Theory described, marking the structural boundary between wave two and wave three economics. |
| b8 | Stuff we figured out about AI in 2023 | Simon Willison's Weblog | 2023-12 | First-hand practitioner account of the LLM breakthrough year, documenting the open-web training corpus as the substrate of wave-three capability and flagging the epistemic uncertainty around what models actually learn. |
| b9 | Things we learned about LLMs in 2024 | Simon Willison's Weblog | 2024-12 | Annual review documenting the 100x inference price drop, the proliferation of GPT-4-class models to 18 labs, and the transition toward agentic patterns, providing empirical evidence for the compounding cost-reduction dynamic of wave three. |
| b10 | 2025: The year in LLMs | Simon Willison's Newsletter (Substack) | 2026-01 | Introduces the Jevons paradox framing for AI knowledge work - cheaper cognition generates more demand for cognitive tasks rather than less - directly engaging the compounding-versus-displacement debate. |
| b11 | What if LLMs are mostly crystallized intelligence? | LessWrong | 2025 | Argues that frontier model capability growth is bottlenecked by domain-specific data quality and volume, with general-purpose internet data estimated to run dry by 2026–2032, making wave-one open-web content a finite and depletable input. |
| b12 | The next wave of model improvements will be due to data quality | LessWrong | 2025-06 | Identifies real-world deployment feedback (from Operator and Codex usage signals) as the next load-bearing training data source, framing the shift from static open-web corpora to dynamic synthetic-and-interaction data as a structural transition. |
| b13 | The New AI Infrastructure Stack | Medium (Devansh / Machine Learning Made Simple) | 2025-06 | Characterises the ASIC–CXL–Optical I/O triad as an interlocking dependency chain where adopting one layer forces adoption of the next, providing a concrete hardware-level illustration of wave-internal compounding. |
| b14 | A Simple Explainer of Acemoglu's Simple Macroeconomics of AI | Causal Inference (Substack) | 2025-04 | Unpacks Acemoglu's NBER 2024 model projecting TFP gains of 0.55–0.71% annually from AI under baseline assumptions, grounding the productivity-paradox debate in a tractable framework and cross-referencing Autor's task model. |
| b15 | The Future of Employment in the Age of Artificial Intelligence | Substack (José Luis Chávez Calva) | 2025-04 | Synthesises Acemoglu–Restrepo displacement, productivity, and new-task-creation effects against empirical vacancy data showing kinks in software employment post-2022, testing whether wave three is following wave-two job-creation patterns. |
| b16 | The Transition Is The Crisis: A DEEP Dive on AI, Jobs and The Future Of Work Over the Next 5 Years | Great Leadership (Substack) | 2026-02 | Applies the Acemoglu–Restrepo task-based model to current AI layoff data, documenting that routine cognitive task displacement is already net negative while creative and strategic roles show net job growth, testing the 'this time is different' thesis directly. |
| b17 | Daron Acemoglu on AI and Jobs | Center for Humane Technology (Substack) | 2024-05 | Acemoglu argues that automation since the 1980s has created two structural inequality tiers - capital vs labour, and task-commanding vs task-displaced workers - framing the AI wave as an amplifier of an already-running dynamic. |
| b18 | Acemoglu and Johnson on the Past and Future of Work | The One Percent Rule (Substack) | 2024-12 | Reviews 'Power and Progress' and its core argument that technological benefit distribution depends on institutional power structures, not on the technology itself, providing the historical context for the externalised-harvest framing. |
| b19 | Beyond AI Apocalypse as '47% of Jobs at Risk' | Tom Bewick (Substack) | 2026-02 | Critiques Frey–Osborne and engages Gans–Goldfarb 2026 O-ring automation model, arguing that automating easy tasks concentrates human effort on harder bottleneck tasks, shifting the skill premium rather than eliminating it. |
| b20 | The AI Disintermediation Panic is Unfounded | Playing FTSE (Substack) | 2026-01 | Argues the market is mispricing incumbents by conflating 'AI creates new competition' with 'AI destroys incumbent value overnight,' drawing a direct parallel to earlier waves where incumbents adapted rather than collapsed. |
| b21 | The Death of AI Extraction: Architecting Your Sovereign Exit | Augmented Mind (Substack) | 2025-05 | Frames the AI platform stack as a repeat of the wave-two platform dependency playbook - cheap access, capture dependency, raise prices - arguing that AI labs are converting wave-one open-web content into a proprietary subscription layer. |
| b22 | The 'vast uncertainty' of AI and jobs - David Autor | The Next Wave Futures (WordPress / Andrew Curry) | 2024-02 | Synthesises Autor's 2024 NBER paper showing 60% of US employment is in categories invented post-1940, and documents his explicit uncertainty about whether wave three will follow the same new-task-creation pattern. |
| b23 | AI 2027: What Superintelligence Looks Like | LessWrong | 2025-04 | Detailed scenario analysis of synthetic training data loops and agent-generated research, examining whether wave three can bootstrap its own training data supply and break free of wave-one corpus dependency. |
| b24 | Is the Internet Different? (critique of Aggregation Theory) | Stratechery (Ben Thompson) | 2020-10 | Documents the academic and legal pushback on Aggregation Theory, including Tim Wu's critique, providing a rigorous counterpoint that grounds the internet-disintermediation claim in contested rather than settled economics. |
| b25 | Inside the AI Buildout Wave: How Infrastructure Is Becoming the New Battleground | Defiance ETFs (Substack) | 2025-11 | Documents the Magnificent Seven's $21.1 trillion share of a $60 trillion S&P 500 market cap as evidence of how hyperscaler concentration compounds across technology waves, and identifies power, chip fabrication, and cooling as the next binding physical constraints. |
VC & Analyst Reports
| ID | Title | Outlet | Date | Significance |
|---|---|---|---|---|
| v1 | Why Software Is Eating the World | Andreessen Horowitz (a16z) | 2011-08 | The foundational VC thesis articulating the internet-to-software wave, tracing how falling infrastructure costs (from $150,000/month in 2000 to $1,500/month by 2011) made the cloud era possible, and explicitly predicting that software would disintermediate established industries - a direct precursor to the 'AI is eating software' framing that followed. |
| v2 | AI's $200B Question (Follow the GPUs) | Sequoia Capital | 2023-09 | David Cahn's first quantitative framing of the AI infrastructure revenue gap - requiring $200B in annual end-user revenue to justify then-current GPU capex - introduced the 'follow the GPUs' investment thesis and raised the first structural question about whether the AI wave would compound value or incinerate capital. |
| v3 | AI's $600B Question | Sequoia Capital | 2024-06 | Cahn's updated analysis tripled the required revenue figure to $600B annually as Nvidia's run-rate revenue surged, exposing a structural $500B gap between AI infrastructure investment and demonstrated end-user returns - the most-cited quantitative challenge to the compounding-value thesis. |
| v4 | AI in 2024: From Big Bang to Primordial Soup | Sequoia Capital | 2024-01 | Sequoia's annual AI outlook named the post-ChatGPT frenzy a 'primordial soup' phase, arguing AI requires deeper exploration than SaaS wave substitution and explicitly contrasting AI as a 'revolution' against cloud SaaS as an 'evolution' from on-premise software. |
| v5 | AI in 2025: Building Blocks Firmly in Place | Sequoia Capital | 2024-12 | Sequoia's 2025 outlook maps the consolidation of frontier model competition to five 'finalists' (Microsoft/OpenAI, Amazon/Anthropic, Google, Meta, xAI), frames compute scaling as the next binding constraint, and places the AI wave in relation to prior infrastructure build-outs. |
| v6 | Marc Andreessen made a dire software prediction 15 years ago. Now it's happening in a way nobody imagined | Fortune / Morgan Stanley | 2026-02 | A retrospective audit of Andreessen's 2011 thesis confirming that software did eat retail, media and telecoms as predicted, but that AI is now eating the software layer itself - with Morgan Stanley quantifying unstructured data (over 80% of enterprise information) as the new automation frontier displacing SaaS headcount. |
| v7 | Bain Technology Report 2025: $2 Trillion in New Revenue Needed to Fund AI's Scaling Trend | Bain & Company | 2025-09 | Bain's sixth annual technology report quantifies the AI funding gap as $2T in required annual revenue by 2030 against a global AI compute demand reaching 200 gigawatts, and introduces a four-level agentic maturity framework (information retrieval through multi-agent constellations) as the structural map for the current wave. |
| v8 | AI's Trillion-Dollar Opportunity (Bain Technology Report 2024) | Bain & Company | 2024-01 | Bain's 2024 report places hyperscalers as the dominant first movers in the AI wave, projecting data centre scale growing from 100 megawatts to gigawatts, and frames the cloud infrastructure wave as the direct load-bearing prerequisite for frontier model deployment. |
| v9 | State of the Art of Agentic AI Transformation (Bain Technology Report 2025) | Bain & Company | 2025-09 | Bain's chapter-level analysis of agentic AI maturity identifies compounding errors in multi-step tasks, lack of communication standards, and data silos as the current binding constraints on wave three - directly relevant to the question of what stops compounding from continuing. |
| v10 | McKinsey: Where AI Will Create Value - and Where It Won't | McKinsey Global Institute | 2025-04 | McKinsey's three-wave model (productivity gains, differentiation, transaction-cost reduction) maps AI's compounding economic logic and argues small early advantages in data quality and relevance will compound into 'disproportionate demand' concentration - directly engaging the 'compounding vs harvest' debate. |
| v11 | The Economic Potential of Generative AI: The Next Productivity Frontier | McKinsey Global Institute | 2023-06 | McKinsey's headline market-sizing report places generative AI at $2.6–$4.4 trillion in annual value across 63 use cases, with customer operations, marketing and sales, software engineering, and R&D as the leading categories - providing the quantitative floor for wave-three economic claims. |
| v12 | The State of AI in 2025: Agents, Innovation, and Transformation | McKinsey Global Institute | 2025-11 | McKinsey's annual survey finds 88% of organisations using AI in at least one function (up from 78% in 2024) but only 6% qualifying as high performers with over 5% EBIT impact, while identifying demand for data engineers, ML engineers, prompt engineers and AI ethics specialists as the emerging role categories of wave three. |
| v13 | CB Insights State of AI 2025 Report | CB Insights | 2026-02 | Full-year 2025 AI venture data: over $200B in AI funding with LLM developers (OpenAI, Anthropic, xAI) capturing 41% of investment, AI M&A running at 1.5x 2024 levels, and the frontier model race consolidating into clear 'haves' and 'have-nots' - the clearest quantitative map of wave-three capital concentration. |
| v14 | CB Insights State of Venture 2024 Report | CB Insights | 2025-01 | Documents the milestone year when AI represented 37% of venture funding and 17% of deals - both all-time highs - with all top five venture deals going to AI infrastructure players, marking the shift from software-wave investment patterns to AI-wave capital concentration. |
| v15 | CB Insights State of Venture 2025 Report | CB Insights | 2026-02 | CB Insights' full-year 2025 report records total venture funding at $469B (the highest since 2022), AI accounting for 48% of all funding, and the top six rounds (totalling $111B) all going to AI companies - quantifying the winner-take-most dynamic the compounding thesis predicts. |
| v16 | CB Insights State of Venture Q3'25 Report | CB Insights | 2025-12 | Captures AI exceeding 50% of total venture funding for the first time and notes energy (nuclear, fusion) attracting record investment as hyperscaler power demand becomes the binding constraint - directly evidencing the 'next substrate' question. |
| v17 | Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026 | Gartner | 2025-08 | Gartner's adoption-curve forecast for agentic AI - from less than 5% of enterprise apps in 2025 to 40% by 2026 and potentially 30% of enterprise application software revenue ($450B+) by 2035 - provides the clearest quantitative adoption-curve framing for wave-three diffusion. |
| v18 | Gartner: AI Agents Will Intermediate More Than $15 Trillion in B2B Purchases by 2028 | Gartner / Digital Commerce 360 | 2025-11 | Gartner's most expansive market-sizing claim - 90% of B2B purchases intermediated by AI agents by 2028, channelling over $15 trillion - frames the disintermediation of wave-one distribution patterns through wave-three agentic systems, closing the loop on the three-wave dependency chain. |
| v19 | Gartner Says Agentic AI Supply Exceeds Demand, Market Correction Looms | Gartner | 2025-10 | Gartner's counter-cyclical warning that agentic AI supply already exceeds demand and a market correction is likely provides sceptical ballast to the compounding-value thesis, drawing historical parallels to dot-com, energy and telecoms corrections. |
| v20 | Gartner Forecasts Supply Chain Management Software with Agentic AI Will Grow to $53 Billion by 2030 | Gartner | 2026-04 | The most recent Gartner vertical-specific forecast, tracking agentic AI in supply chain from under $2B in 2025 to $53B by 2030 (60% enterprise adoption), also identifies data-readiness and workforce AI-literacy as the binding constraints on deployment speed. |
| v21 | Gartner Top Strategic Technology Trends for 2025: Agentic AI | Gartner | 2024-10 | Gartner's technology radar placement of agentic AI as the top 2025 strategic trend, framing it as a 'goal-driven digital workforce' and projecting that by 2028 at least 15% of day-to-day work decisions will be made autonomously - the clearest Gartner framing of wave-three as a labour-market event. |
| v22 | CB Insights State of Venture Q1'25 Report | CB Insights | 2025-05 | Documents the Q1 2025 shift in AI dealmaking from infrastructure-dominated investment toward vertical application-layer platforms, with 63% of organisations placing significant importance on AI agents - marking the transition from wave-three infrastructure build-out to application-layer value capture. |
| v23 | The Crunchbase Unicorn Board: Rising Investors Behind the New Unicorn Class | Crunchbase | 2026-03 | Documents 187 new unicorns in 2025 (up 61% year-on-year), with AI-native companies accounting for 25% of the total and Sequoia and a16z dominating early-stage backing - providing the most recent empirical evidence on the rate of new-category creation in wave three. |
| v24 | Bain Technology Report 2025 (Full PDF): AI Leaders Are Extending Their Edge | Bain & Company | 2025-09 | Bain's full sixth annual report, documenting that AI leaders achieved 10–25% EBITDA gains in 2023–24 while laggards fell further behind, and examining how tech giants are competing at every layer - infrastructure, models, platforms, applications - to capture disproportionate value. |
| v25 | Foundation Capital: The AI Hype - $600B Question or $4.6T Opportunity? | Foundation Capital | 2024-11 | Directly rebuts Sequoia's Cahn thesis by expanding the addressable market to include labour costs ($2.3T in sales, marketing, software engineering and HR) plus outsourced IT and business-process services ($2.3T per Gartner), arguing the AI wave is addressing a $4.6T opportunity rather than competing for the existing software market. |