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Is AI Demand a Structural Shift or a Hype Cycle? · history

Version 11

2026-06-01 02:36 UTC · 161 items

What

The structural-vs-hype debate now has an explicit historical frame: the Solow Paradox—"computers are everywhere except in the productivity statistics"—is being invoked across multiple sources to explain why AI accelerates individual workers without producing economy-wide efficiency gains [11][12][13]. Jensen Huang predicts Nvidia's market cap will be "very much higher" in 3–5 years backed by $150 billion per year in Taiwan [6], AMD has crossed $800 billion in market cap for the first time [7], and Goldman Sachs forecasts 24x AI agent token growth by 2030 [8][9]—but a major fund manager warns AI hardware valuations are internally inconsistent [16], JP Morgan predicts DRAM and NAND ASP growth will decelerate in late 2026/early 2027 because AI has not broken cyclical memory supply physics [15], and a named enterprise "inference cost crisis" has crystallized in 2026 as cheap per-token rates fail to prevent expensive end-to-end agent deployments [18][19].

Why it matters

The Solow Paradox analogy is double-edged: it normalizes the current productivity gap as a predictable feature of general-purpose technology diffusion—potentially vindicating the structural thesis if gains eventually arrive—but also implies the economy-wide payoff could be a decade away, well outside most investment horizons. If JP Morgan's memory ASP deceleration call [15] lands alongside persistent enterprise inference cost pressure [18][19], the supply-tightness signals anchoring the structural demand thesis could soften precisely as enterprise AI deployment is supposed to be scaling.

Open questions

  • The Solow Paradox resolved over roughly 10–15 years as computing penetrated workflows and organizations adapted; what observable indicators—employment composition, sectoral output, patent filings—would tell us within 12–24 months whether AI is on that track or trending toward a demand collapse without delayed payoff? [11][12]

  • JP Morgan predicts DRAM/NAND ASP growth decelerates late 2026/early 2027 [15] while Samsung and SK Hynix warn of HBM shortages through 2027+ [3]; does this signal a divergence between legacy DRAM and AI-specific HBM markets, or is AI demand insufficient to break cyclical supply physics across all memory types?

  • A fund manager flags AI hardware valuations as internally inconsistent—memory at 3–5X PE vs. Nvidia's comparatively low PE [16]; what data release or earnings event would force a repricing across AI hardware equities?

  • Goldman Sachs projects 24x agent token growth by 2030 [9] while enterprise inference costs are already in crisis in 2026 [18][19]; will commoditizing inference prices resolve agent economics before cost pressure triggers mass project cancellations?

Narrative

The debate over whether artificial intelligence represents a durable economic shift or a speculative investment cycle is contested across five reinforcing dimensions: semiconductor supply, enterprise deployment outcomes, physical power infrastructure, macroeconomic measurement, and financial market valuation. Semiconductor-side signals remain bullish: TSMC's CoWoS advanced packaging stays capacity-constrained through 2027 with Nvidia holding approximately 60% of available supply [1][2], and Samsung and SK Hynix warn of HBM memory shortages through 2027 and beyond with customers already reserving supply years in advance [3][4][5]. Nvidia's CEO Jensen Huang publicly predicts the company's market cap will be "very much higher" over the next three to five years, backed by a $150 billion annual investment commitment in Taiwan [6], and AMD has crossed $800 billion in market cap for the first time, attributed to CEO Lisa Su's AI strategy [7]. Goldman Sachs characterizes the opportunity as continuing—"the AI party is not over"—and forecasts AI agent token usage growing 24 times by 2030, projecting that agent adoption will boost technology sector cash flows [8][9].

The enterprise productivity layer tells a markedly different story, and the interpretive frame for that gap has sharpened. A survey of 6,000 executives finds over 80% of companies report no productivity gains from AI despite significant spending, with executives using AI tools averaging only 90 minutes per week [10]. Multiple publications now explicitly invoke the Solow Paradox—the 1980s observation that computing power appeared "everywhere except in the productivity statistics"—to explain why AI is accelerating individual worker speed without generating economy-wide efficiency gains [11][12][13]. SemiAnalysis offers a structural measurement explanation through its "Dark Output" thesis: AI creates approximately $1.5 trillion in economic value invisible to GDP accounting through substitution and new tasks, a measurement failure potentially larger in scale than the 1990s computing productivity paradox [14]. The Solow Paradox historical analogy is double-edged—economy-wide productivity gains from computing eventually arrived, but only roughly a decade after the investment surge—and not all general-purpose technologies deliver on delayed timelines.

Two new bearish signals have emerged from the financial research tier. JP Morgan Research predicts that DRAM and NAND average selling price growth will begin decelerating in late 2026 to early 2027, explicitly noting that while AI dramatically altered the 2025–2026 demand story for high-bandwidth memory, it has not changed the underlying cyclical physics of the memory market [15]. A major fund manager separately warns that AI hardware valuations cannot all be internally consistent simultaneously—memory makers trading at 3–5X PE while Nvidia trades at a comparatively low PE suggests distorted relative pricing characteristic of speculative excess [16]. Both signals arrive as bullish executives maintain confidence: Huang's $150 billion per year Taiwan commitment represents one of the largest single-company supply chain investments in semiconductor history [6], and a specific falsifiable bearish timeline—the AI investment bubble peaks in October 2026 and breaks in November–December [17]—will be tested against incoming capex guidance and GPU pricing within months.

The inference economics layer has crystallized as a named 2026 enterprise crisis. The tension between cheap per-token costs and expensive end-to-end agent deployments—"cheap tokens, expensive agents"—is now recognized across multiple industry analyses as a structural barrier to enterprise AI scaling at current price points [18][19][20][21]. Cost-driven migration away from Nvidia has two-data-point validation: Uber chose AWS Trainium3 at approximately 50% lower cost [22][23] and Meta has also adopted Amazon's custom AI chips [24], making hyperscaler ASIC adoption a credible trend rather than a single outlier. Physical infrastructure adds a third bottleneck independent of capital availability: approximately 30–50% of planned 2026 US data centers face delays from power grid interconnection permitting [25][26], and Goldman Sachs's 24x agent token forecast implies this infrastructure pressure will compound over the decade.

Timeline

  • 2025-06-25: Gartner predicts over 40% of agentic AI projects will be canceled by end of 2027, citing implementation difficulty, unclear ROI, and governance gaps [57][63]
  • 2025-08-26: Gartner predicts 40% of enterprise apps will feature task-specific AI agents by 2026, up from under 5% in 2025 [58][59]
  • 2026-05: 30–50% of planned 2026 US data centers face delays due to power grid interconnection permitting bottlenecks; AI data center energy consumption projected at ~1,000 TWh annually by 2026 [25][26][69][72]
  • 2026-05-18: SemiAnalysis publishes internal token-spend ROI data reporting 10–90x returns on AI-assisted tasks, arguing demand is structural and economically irreversible [27][28]
  • 2026-05-20: Gavin Baker credits TSMC's capex discipline with actively preventing an industry bubble and flags Amazon's custom AI chip as an emerging competitive variable [41][42][43]
  • 2026-05: TSMC CoWoS capacity constrained through at least 2027 with Nvidia holding approximately 60% of available supply; Samsung and SK Hynix warn of HBM shortages through 2027+ with customers reserving supply years ahead [1][2][3][4][5]
  • 2026-05: Uber selects AWS Trainium3 over Nvidia at approximately 50% lower cost; Meta separately adopts Amazon's custom AI chips, validating AWS custom silicon at hyperscaler scale [22][23][24]
  • 2026-05: Goldman Sachs issues bullish AI infrastructure top picks, stating 'the AI party is not over' and identifying 2026 as the custom silicon inflection point for hyperscaler ASICs [39][38][45][46][37]
  • 2026-05-25: @asymmetricmind predicts the AI investment bubble reaches peak in October 2026 then breaks November–December, providing a specific falsifiable near-term timeline [17]
  • 2026-05-26: AMD crosses $800 billion in market capitalization for the first time, attributed to CEO Lisa Su's AI strategy over the prior three years [7]
  • 2026-05-27: Jensen Huang states Nvidia's market cap will be 'very much higher' over the next three to five years, backed by $150 billion per year in Taiwan investment [6]
  • 2026-05-28: SemiAnalysis introduces 'Dark Output' thesis: AI creates ~$1.5T in unmeasured economic value through substitution and new tasks, arguing official statistics will chronically undercount AI's impact [14][36]
  • 2026-05-28: Fund manager warns AI hardware valuations are internally inconsistent—memory makers at 3–5X PE vs. Nvidia's comparatively low PE—implying distorted relative pricing across the sector [16]
  • 2026-05-29: Larry Ellison predicts that by 2029, AI capability will not be the binding competitive constraint—compute ownership will be [44]
  • 2026-05-30: Survey of 6,000 executives finds 80%+ of companies report no productivity gains from AI; executives using AI tools average only 90 minutes per week [10]
  • 2026-05-30: Goldman Sachs projects AI agent token usage will multiply 24x by 2030 and boost technology sector cash flows; Uber and Microsoft already reconsidering expensive agent deployments due to cost [8][9]
  • 2026-05-30: JP Morgan Research predicts DRAM and NAND ASP growth decelerates late 2026/early 2027, stating AI dramatically changed 2025–2026 demand but has not altered the underlying cyclical physics of the memory market [15]
  • 2026-06: Multiple sources explicitly invoke Solow Paradox to explain why AI boosts individual worker speed without producing economy-wide efficiency gains; enterprise inference cost crisis crystallizes as 'cheap tokens, expensive agents' problem [13][12][11][18][19][20][21]

Perspectives

SemiAnalysis

Strongly structural and bullish: internal ROI data (10–90x) argues demand is economically irreversible; the 'Dark Output' thesis contends AI creates ~$1.5T in unmeasured economic value that will be systematically invisible in official statistics, creating bubble-misread pressure even as real gains compound.

Evolution: Extended from task-level ROI evidence to a macroeconomic measurement thesis; the Solow Paradox discourse now appearing in mainstream media indirectly validates their framing that the productivity gap reflects a statistics problem, not an absence of returns.

[27][28][29][30][31][32][33][34][35][14][36]

Goldman Sachs

Explicitly bullish: 'the AI party is not over,' with specific infrastructure top picks, a forecast of 24x growth in AI agent token usage by 2030, and a prediction that agent adoption will boost technology sector cash flows.

Evolution: Extended from 'next wave' infrastructure analysis to a specific agent cash-flow thesis; the 24x token forecast is the firm's most concrete long-run demand signal, though it arrives alongside enterprise reports of cost-driven agent reconsideration.

[37][38][39][40][8][9]

Bullish semiconductor executives (Huang, Ellison, Baker, Su)

Huang predicts Nvidia's market cap 'very much higher' in 3–5 years with $150B/year Taiwan investment; Ellison predicts compute ownership, not AI capability, will be the binding competitive constraint by 2029; Baker credits TSMC capex discipline with preventing a bubble; AMD's $800B milestone validates Su's AI pivot.

Evolution: Huang's $150B/year commitment is a substantial new data point extending the structural capex thesis; AMD's market cap milestone adds a second hardware company to the bull case alongside Nvidia.

[41][42][43][44][6][7]

Hyperscaler custom silicon (AWS, validated by Meta and Uber)

AWS Trainium is cost-competitive against Nvidia at hyperscaler scale, with Uber choosing it at ~50% cost savings and Meta adopting it separately; 2026 is characterized as the inflection point for hyperscaler ASICs broadly.

Evolution: Consistent; two distinct enterprise adoption cases make the Nvidia demand-diversion thesis substantially more credible, and the inference cost crisis provides additional commercial rationale for migrating workloads to cheaper ASICs.

[42][43][22][23][24][45][46][37][8]

Bearish financial and market analysis (Forbes, @asymmetricmind, Rohan Paul, JP Morgan)

Forbes frames the buildout as a $1.7T bubble; @asymmetricmind provides a falsifiable timeline (peaks October 2026); Rohan Paul flags that AI hardware valuations cannot simultaneously all be accurate; JP Morgan forecasts DRAM/NAND ASP deceleration in late 2026/early 2027 as cyclical physics reassert.

Evolution: Extended with two new quantitative signals—valuation inconsistency across AI hardware equities and the JP Morgan memory cycle deceleration forecast—moving the bearish case from narrative framing toward financial market data.

[47][48][49][17][16][15]

TSMC and semiconductor supply chain (including HBM manufacturers)

Supply tightness has broadened: CoWoS constraints run through 2027 with Nvidia holding ~60% of supply, and Samsung and SK Hynix directly warn of HBM shortages through 2027+ with customers reserving supply years ahead.

Evolution: Consistent on CoWoS and HBM multi-year tightness; JP Morgan's cyclical deceleration forecast for DRAM/NAND introduces uncertainty about whether HBM-specific tightness will hold as a distinct market or follow broader memory cycles.

[50][51][52][53][54][1][2][55][3][4][5][56]

Gartner and enterprise survey data

Triple signal—rapid agent adoption forecast (40% of enterprise apps by 2026), high project failure forecast (40%+ canceled by 2027), and an executive survey showing 80%+ of 6,000 companies report zero productivity gains with AI tools used only ~90 minutes per week.

Evolution: The Solow Paradox framing now circulating in mainstream media provides historical context for why the zero-gains survey result might reflect diffusion timing rather than structural failure—though the analogy's 10–15 year resolution window is itself a bearish implication for near-term bulls.

[57][58][59][60][61][62][63][64][65][10]

Infrastructure risk analysts and physical bottleneck watchers

Power and grid constraints are a binding physical bottleneck: 30–50% of planned 2026 US data centers face delays, Congress is split on interregional transmission legislation, and Goldman Sachs's 24x agent token forecast implies this infrastructure pressure will compound over the decade.

Evolution: Consistent; no resolution on the partisan transmission split, and the Goldman Sachs agent forecast adds a new demand multiplier to the already-stressed infrastructure outlook.

[66][67][25][68][26][69][70][71][72][73][74][75][76][77][78]

Tensions

  • SemiAnalysis 'Dark Output' (AI creates ~$1.5T in unmeasured real value) vs. 80%+ of surveyed executives reporting zero productivity gains: either gains are real but invisible to measurement—a statistics problem—or the structural case is overstating returns that most organizations genuinely cannot capture. [14][10][27][28]
  • JP Morgan's cyclical memory deceleration forecast (DRAM/NAND ASP growth slows late 2026/early 2027 as supply physics reassert) vs. Samsung and SK Hynix's multi-year HBM shortage warnings: either AI demand has bifurcated memory markets or the cyclical correction will reach HBM and undercut the key supply-tightness signal. [15][3][4][5]
  • Goldman Sachs's 24x AI agent token usage forecast by 2030 vs. the 2026 enterprise inference cost crisis: scaled agent demand may be structurally real while simultaneously triggering cost-driven cutbacks and custom silicon migration at current inference price points. [8][9][18][19][22]
  • Meta and Uber validating AWS custom silicon vs. CoWoS/Nvidia concentration: if a hyperscaler and major enterprise are routing workloads to competing ASICs at ~50% lower cost, CoWoS tightness increasingly reflects training-workload concentration rather than total AI demand, threatening the supply-chain signal that anchors the structural thesis. [22][23][24][1][2]
  • Rohan Paul's AI hardware valuation inconsistency (memory at 3–5X PE, Nvidia at low PE) vs. Jensen Huang's and AMD's bullish market cap signals: either the sector is mispriced with a correction pending, or divergent valuations reflect rationally differentiated views on which hardware segment captures AI value. [16][6][7]
  • Hyperscaler semiconductor conviction (CoWoS and HBM supply tightening through 2027) vs. enterprise deployment stagnation (80%+ of executives report no gains): the structural demand thesis requires productivity gains to diffuse broadly even as the hyperscaler and enterprise tiers are demonstrably diverging. [1][2][3][10]

Sources

  1. [1] Nvidia Secures 60% of CoWoS Capacity - Astute Group — reactive:ai-demand-bubble-debate
  2. [2] Inside the AI Bottleneck: CoWoS, HBM, and 2–3nm ... — reactive:ai-demand-bubble-debate
  3. [3] Samsung and SK hynix warn AI-driven memory shortages could last until 2027 and beyond, as HBM demand explodes — customers already reserving supply years ahead, while the wider DRAM market begins to tighten : r/hardware — reactive:aws-garman-a100-demand
  4. [4] The AI Memory Supercycle | Introl Blog — reactive:nvidia-vera-computex-launch
  5. [5] HBM Supply Crisis 2026: The Bottleneck Redefining AI - EnkiAI — reactive:hbm-memory-supply-squeeze
  6. [6] Jensen Huang just said Nvidia's market cap will be "very much higher" over the next three to five years (Save this). — Milk Road AI Twitter (2026-05-27)
  7. [7] AMD just crossed $800 billion in market cap for the first time in its history (Save this). — Milk Road AI Twitter (2026-05-26)
  8. [8] Goldman Sachs: "Token use by AI agents is expected to multiply 24 times by 2030" — Rohan Paul Twitter (2026-05-30)
  9. [9] AI Agents Forecast to Boost Tech Cash Flow as Usage Soars — reactive:ai-demand-bubble-debate
  10. [10] This survey suggests over 80% of companies have seen no productivity gains from AI so far, despite billions in spending.… — Rohan Paul Twitter (2026-05-30)
  11. [11] Employees using AI are working faster, but the economy isn't more efficient. A look at what happened in the pre-Internet era might explain why | Fortune — reactive:ai-demand-bubble-debate
  12. [12] Solow Paradox Returns as AI Skips Economy-Wide Gains - AI Weekly — reactive:ai-demand-bubble-debate
  13. [13] AI Productivity's $4 Trillion Question: Hype, Hope, And Hard Data — reactive:ai-demand-bubble-debate
  14. [14] AI Dark Output: The Visible Cost of Invisible Output — SemiAnalysis Twitter (2026-05-29)
  15. [15] JP Morgan Research: DRAM and NAND average selling price changes start hitting the brakes around late 2026 to early 2027. — Rohan Paul Twitter (2026-05-30)
  16. [16] "If you look at the valuations for all these AI names, they just can't all be accurate. You have memory makers at 3-5X … — Rohan Paul Twitter (2026-05-28)
  17. [17] The AI investment bubble reaches peak conditions in late 2026 — most likely October — then breaks in November–December, ... — reactive:ai-demand-bubble-debate (2026-05-25)
  18. [18] Inference Economics: Solving 2026 Enterprise AI Cost Crisis — reactive:ai-demand-bubble-debate
  19. [19] Cheap Tokens, Expensive Agents: The 2026 Inference Economics Reckoning | Socradata — reactive:ai-demand-bubble-debate
  20. [20] The Emerging Economics of Enterprise AI: A Practical Guide for 2026 - Ecosystm — reactive:ai-demand-bubble-debate
  21. [21] AI Inference Cost Crisis 2026: Why Your AI Bill Is Exploding - Oplexa — reactive:ai-demand-bubble-debate
  22. [22] Uber Picks AWS Trainium3: 50% Cheaper Than Nvidia [2026] — reactive:ai-demand-bubble-debate
  23. [23] Amazon vs Nvidia: Custom Trainium Chips Gain Traction in AI Computing | 2026 Analysis - News and Statistics - IndexBox — reactive:ai-demand-bubble-debate
  24. [24] Meta Taps Amazon's AI Chips, Validating AWS Custom Silicon Play | The Tech Buzz — reactive:ai-demand-bubble-debate
  25. [25] 30% of US Data Centers to be Cancelled or Delayed by 2026 — reactive:ai-demand-bubble-debate
  26. [26] Nearly half of US data centers planned for 2026 are facing delays or ... — reactive:ai-demand-bubble-debate
  27. [27] The ROI on every single task was over 10x. Most were 60-90x. This is why the demand isn't cyclical - once you see that a… — SemiAnalysis Twitter (2026-05-18)
  28. [28] Our SemiAnalysis Weekly Podcast often asks - Is the AI cycle this time truly different from other cycles? Well, at least… — SemiAnalysis Twitter (2026-05-18)
  29. [29] AI is not the first technology to drop prices by multiple orders of magnitude. When screws were handmade, output was cou… — SemiAnalysis Twitter (2026-05-21)
  30. [30] AI Value Capture - The Shift To Model Labs - SemiAnalysis — reactive:ai-demand-bubble-debate
  31. [31] Tokenomics Model - SemiAnalysis — reactive:ai-demand-bubble-debate
  32. [32] Token Cost vs Human Labor Cost ROI Analysis | Prateek Joshi ... — reactive:ai-demand-bubble-debate
  33. [33] The Supply and Demand of AI Tokens | Dylan Patel Interview — reactive:ai-demand-bubble-debate
  34. [34] SemiAnalysis Revenue Soars Amid Legal Dispute | Phemex News — reactive:ai-demand-bubble-debate
  35. [35] GPU Rental Market Shifts with Agentic AI | SemiAnalysis posted on ... — reactive:ai-demand-bubble-debate
  36. [36] The most popular AI subscription will run you about $20/month and it gives you access to most of the models and is good … — SemiAnalysis Twitter (2026-05-28)
  37. [37] "AI Shovel" dominates the market, who is the next winner? Goldman ... — reactive:ai-demand-bubble-debate
  38. [38] Bloomberg - Investors looking to profit amid the buildout... — reactive:ai-demand-bubble-debate
  39. [39] AI Party Is Not Over, Goldman Sachs Issues Top Picks — reactive:ai-demand-bubble-debate
  40. [40] AI Infrastructure Stocks 2026: Picks and Shovels Playbook — reactive:ai-demand-bubble-debate
  41. [41] Gavin Baker just gave the clearest framework for tracking whether the AI cycle turns into a bubble. — Milk Road AI Twitter (2026-05-20)
  42. [42] Investment Guru Gavin Baker: Amazon's AI Chip a Dark Horse, Orbital Data Centers on Horizon, TSMC Preventing Industry Bubble - Tiger Brokers — reactive:ai-demand-bubble-debate
  43. [43] Gavin Baker on Orbital Compute, TSMC, and Frontier Models — reactive:ai-demand-bubble-debate
  44. [44] Larry Ellison, the man who built Oracle into a $500 billion enterprise software empire and he said something that every … — Milk Road AI Twitter (2026-05-29)
  45. [45] Custom Silicon Inflection 2026 | Introl Blog — reactive:big-tech-q1-2026-cloud-earnings
  46. [46] Hyperscaler AI ASIC Market: Google, AWS, Microsoft & More — reactive:big-tech-q1-2026-cloud-earnings
  47. [47] The State Of The $1.7 Trillion AI Bubble: The End Of Thinking — reactive:ai-demand-bubble-debate
  48. [48] 2026, the Last Year of the Bubble: The AI Empire Begins to Crumble — reactive:ai-demand-bubble-debate
  49. [49] The Hidden Costs That Are Undermining Enterprise AI ROI — reactive:ai-demand-bubble-debate
  50. [50] TSMC to Quadruple Advanced Packaging Capacity: Reaching 130,000 CoWoS Wafers Monthly by Late 2026 — reactive:ai-demand-bubble-debate
  51. [51] CoWoS capacity utilization reportedly only 60% amid AI boom ... — reactive:ai-demand-bubble-debate
  52. [52] TSMC's packaging capacity is being snapped up. - EEWorld — reactive:ai-demand-bubble-debate
  53. [53] [News] TSMC CoWoS Wafer ASP Reportedly Nears 7nm; Advanced Packaging to Become a Key Profit Driver — reactive:ai-demand-bubble-debate
  54. [54] TSMC to expand CoW Orders in 2H26 as OSAT CoWoS-like tech rises — reactive:ai-demand-bubble-debate
  55. [55] Who Will Divide Up the CoWoS Production Capacity in 2026? - 36氪 — reactive:ai-demand-bubble-debate
  56. [56] Memory AI bottleneck: SK Hynix, Samsung, Micron control supply | Kai Kaushik posted on the topic | LinkedIn — reactive:ai-demand-bubble-debate
  57. [57] Gartner: Over 40% of Agentic AI Projects Will Be Canceled by End ... — reactive:ai-demand-bubble-debate
  58. [58] Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific ... — reactive:ai-demand-bubble-debate
  59. [59] Gartner predicts task-specific AI agent growth — reactive:ai-demand-bubble-debate
  60. [60] What's Changed One Year Since Gartner's “80% AI Resolution ... — reactive:ai-demand-bubble-debate
  61. [61] 40% of Enterprise Apps Will Embed AI Agents by End of 2026 ... — reactive:ai-demand-bubble-debate
  62. [62] Gartner claims that by 2026, 40% of the enterprise apps will be ... — reactive:ai-demand-bubble-debate
  63. [63] Over 40% of Agentic AI Projects Likely to Be Abandoned by 2027 – Gartner Forecast - CDO Magazine — reactive:ai-demand-bubble-debate
  64. [64] Gartner warns of 40% AI project failures by 2027. 5 principles to avoid this fate. | Juliano Martins posted on the topic | LinkedIn — reactive:ai-demand-bubble-debate
  65. [65] Gartner Predicts by 2027, 50% of Enterprises Without a People ... — reactive:ai-labor-market-debate
  66. [66] AI Data Center Grid Strain: Power Halts Growth in 2026 — reactive:jensen-huang-nvidia-thesis
  67. [67] From Growth To Growing Risk: Rapid Development Of - S&P Global — reactive:ai-demand-bubble-debate
  68. [68] AI Is Stressing the Grid | BUILT — reactive:ai-demand-bubble-debate
  69. [69] Why Power Interconnection Timelines Are Delaying Data Center Builds — reactive:ai-demand-bubble-debate
  70. [70] [PDF] Meeting Growing Power Demand While Protecting Ratepayers — reactive:jensen-huang-nvidia-thesis
  71. [71] The Interconnection Queue Continues to Be a Barrier to American ... — reactive:ai-power-grid-crisis
  72. [72] Nearly half of planned US data centers have been delayed or canceled limited by shortages of power : r/wallstreetbets — reactive:ai-demand-bubble-debate
  73. [73] Hearing on "AI and the Grid: Meeting Growing Power Demand While Protecting Ratepayers" | Democrats, Energy and Commerce Committee — reactive:ai-demand-bubble-debate
  74. [74] Energy Hearing: AI And The Grid: Meeting Growing Power Demand ... — reactive:ai-demand-bubble-debate
  75. [75] House Subcommittee Hears Bipartisan Agreement on Data Center ... — reactive:ai-demand-bubble-debate
  76. [76] AI Data Centers: 1,000 TWh by 2026 [April Update] - Tech Insider — reactive:big-tech-q1-2026-cloud-earnings
  77. [77] Data Center Power Crisis 2026: The Grid Bottleneck - EnkiAI — reactive:ai-demand-bubble-debate
  78. [78] [PDF] US House Committee on Energy and Commerce - Congress.gov — reactive:ai-demand-bubble-debate