Essay

The AI Correction Is Coming. And I Feel Fine.

•12 min read•Last updated: Sep 16, 2025
#AI#Business of AI
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Job to be Done: Learn how to locate the current AI moment in the boom‑to‑deployment arc, understand structural dynamics; watch signals; set strategy; make the path forward more predictable so you can set expectations, time decisions, and plan what to learn, build, buy, and budget next.

We shape our tools, then our tools shape us. — Marshall McLuhan
AI might be in a bubble. — Sam Altman, OpenAI CEO, Aug 18, 2025

The impending AI market correction is a predictable and necessary turning point. This is precisely why I feel fine. For those building the future, this will be the real starting gun. The pivot we've waited for. It will be time to build. The pattern is well understood: Carlota Perez’s model of Technological Revolutions and Financial Capital

Perez’s Framework

Perez maps a recurring cycle: an installation phase (breakthroughs, investment, speculative buildout) that crescendos into a turning point (market reset), followed by a deployment phase (broad, reliable use in the real economy). The lesson repeats: excitement arrives early; widespread productivity arrives later. Enduring value accrues to those who build for that later phase.

Current State: Structural Dynamics versus Triggers

The AI revolution sits deep in the installation period, defined by a massive infrastructure buildout. Structural forces are in motion; a handful of external triggers could accelerate the turn.

Content illustration: perez cycles with mappin

Figure: The cycle is predictable. Installation builds capacity and invites a reset; deployment converts it into durable productivity.

Structural dynamics

  • Capacity buildout: Record capex into AI factories, chips, and power. McKinsey’s mid case projects global data‑center demand at 171–219 GW by 2030, with AI‑ready capacity growing ~33% CAGR from 2023–2030 (source: McKinsey, Oct 2024).
  • Access costs fall: As capacity and efficiency rise, per‑use prices (e.g., $/1M tokens) compress—typically faster during supply gluts.
  • Efficiency gains: Quantization, distillation, compilation, better schedulers, and each accelerator generation raise output per watt/dollar.
  • Adoption lag: Integration, governance, and workflow change trail infra buildout, widening the capacity–absorption gap ahead of the turn.

Triggers and accelerants

  • Regulation: Safety regimes, data‑residency, and sector rules slow go‑lives and raise OpEx for immature offerings.
  • Supply chain: Fragility across fabs, advanced packaging, transformers, and switchgear can create step‑function delays.
  • Geopolitics: Export controls and regionalization affect access to leading‑edge compute and substrates.
  • Environmental/siting: Water, land‑use, noise, and thermal constraints drive permitting delays and scrutiny.
  • Grid capacity: Power is the near‑term ceiling. The IEA estimates global data‑center electricity will more than double to ~945 TWh by 2030, with AI a primary driver (source: IEA, Apr 2025). In the U.S., data centers could account for up to ~40% of net new electricity demand this decade (source: Goldman Sachs Research, Feb 2025). McKinsey estimates U.S. data‑center load could reach ~11–12% of total electricity by 2030 (source: McKinsey, Oct 2024).

Signals to Watch / Early Warning Signs

Demand and power

  • Data‑center demand: 171–219 GW global by 2030; AI‑ready ~33% CAGR (source: McKinsey, Oct 2024).
  • Electricity use: ~945 TWh global by 2030; AI‑optimized workloads a major share (source: IEA, Apr 2025).
  • U.S. grid share: Data centers up to ~40% of net new demand this decade (source: Goldman Sachs Research, Feb 2025).

Utilization and pricing

  • GPU lead times: Sustained <3 months signals a flip from supply‑constrained to demand‑constrained.
  • Cluster utilization: Rising vacancy or falling booked hours on large GPU clusters → overcapacity → price compression.
  • Inference prices: Track $/1M tokens or $/call; persistent step‑downs often precede broader repricing.

Buildout pace

  • Project announcements: Mega‑campus filings (MVA, in‑service dates) reveal whether capacity growth is still accelerating.
  • Grid constraints: Interconnection denials/delays, transformer shortages, and multi‑year queues are leading indicators.

Optional micro‑improvements

  • Host the diagram at a stable URL you control; some sites block Notion CDN hotlinks.
  • If you want a compact dashboard later, we can add a two‑column table: signal, current read, threshold for “turn.”

Scenarios: Fast Path / Slow Path

The correction is inevitable, but its speed and depth are influenced by several factors, creating fast and slow path scenarios.

What would make the correction happen sooner (Fast Path):

  • Rapid Infrastructure Build Speed: If AI factory and power grid infrastructure buildout accelerates faster than anticipated, supply could quickly outstrip effective demand, leading to swifter price compression and repricing.
  • Aggressive Regulation Design: Swiftly implemented, stringent regulations around AI safety, data privacy, or energy consumption could abruptly halt or slow certain deployment avenues, accelerating a market reset.
  • Demand Elasticity: If the demand for new AI applications is less elastic to falling prices than projected, the growth loop weakens, and returns disappoint faster.
  • Efficiency Breakthroughs: Unexpected leaps in model efficiency (e.g., dramatically smaller, equally capable models) could reduce the need for current large‑scale infrastructure, leading to rapid overcapacity.

What would make the correction happen slower (Slow Path):

  • Permit Delays: Prolonged bureaucratic processes for data center construction, power grid upgrades, and land acquisition can slow the expansion of physical capacity, extending the current supply‑constrained period.
  • Environmental/Regional Constraints: Increased scrutiny over water usage, local power availability, and environmental impact can restrict the locations and pace of new AI factory builds.
  • Trust and Regulation Lags: Slow development or adoption of trust frameworks and clear regulations can delay enterprise deployment, extending the "wait and see" period before widespread adoption.
  • Uneven Deployment: Significant disparities in AI adoption and infrastructure availability across different sectors or geographies could slow the overall market repricing, leading to a more prolonged, staggered correction.

Implications: What to Build / Buy / Budget / Learn

The correction isn’t a crash. It’s the moment the stack gets hardened. Build the parts that still matter when prices fall and requirements rise.

What to prioritize (build/buy)

  • Infrastructure (physical and digital): Aim for reliability and efficiency—power, cooling, interconnects, accelerators—and the software that schedules, monitors, and tunes them. Optimize for lower $/request and faster MTTR.
  • Orchestration layer: Think ‘application OS’: plan/workflow, tool calls, model routing, memory, guardrails, retries, rollout—implemented with today’s frameworks.
  • Reliability and resilience: Ship with evals, load tests, canaries, fallbacks, circuit breakers, chaos drills, and SLOs for quality/latency/cost. Have incident playbooks for model/vendor changes.
  • Data strategy: Own provenance, permissions, lineage, and refresh. Build pipelines, embeddings/stores, and domain knowledge that outlive any model swap.
  • Compliance and security: Policy checks, audit trails, red‑team harnesses, PII controls, tenancy/isolation, supplier reviews, regional data paths.

Budget carefully (where to be cautious)

  • Single‑model dependency: Keep a model portfolio (frontier/efficient/local) behind an abstraction. Benchmark routes continuously.
  • Unbounded scaling: Tie compute to unit metrics and real usage (utilization, conversion, payback). Pause when $/request or quality stops improving.
  • Demo‑ware: Don’t ship what can’t meet SLOs or margins. Require runbooks and rollback paths.

Plan and learn

  • Design for change: Make models, prompts, and tools hot‑swappable behind interfaces; keep data portable.
  • Instrument everything: Track cost/quality/latency per route. Externally, watch GPU lead times, cluster utilization, and $/1M tokens.
  • Roll out with gates: Shadow traffic, A/B, and eval thresholds before full scale. Add cost guardrails.
  • Stick to fundamentals: Performance engineering, secure supply chain, least privilege, and strict data governance.

Turning Point & What Success Looks Like Post‑Turn

The turn is a hard repricing. After it, the game changes: fewer bets on promise, more wins on reliable delivery.

Indicators the deployment phase has arrived

  • Stable interfaces: Model, vector, and tool APIs settle; versioning and deprecation windows lengthen; fewer breaking changes.
  • Boring changelogs: Model releases shift from big leaps to incremental, predictable improvements; perf/cost curves smooth out.
  • ROI‑gated renewals: Deals renew on measured outcomes (SLA/SLO hit rates, cost per task, cycle‑time cuts), not novelty.
  • Deep vertical usage: Critical workflows in multiple sectors move from pilots to defaults; audit/compliance patterns repeat across customers.
  • Production DX: Mature tooling for evals, tracing, routing, caching, rate control, and rollback; docs and SDKs are stable; on‑call for AI looks like SRE.

How to tell winners before they’re obvious

  • They measure the work: Clear unit metrics (cost/quality/latency per task), route‑level observability, and auto‑tuning to those targets.
  • They survive model churn: Hot‑swappable models/tools behind interfaces; fallbacks and canaries by default.
  • They’re domain‑correct: Tight data provenance, policies, and playbooks that match the industry’s real constraints.
  • They ship reliability: SLOs for accuracy, latency, and spend; incident retros show fixed classes of bugs, not whack‑a‑mole.
  • They compound ops: Lower $/task and higher success rates quarter over quarter, not just higher top‑line tokens.

What not to expect

  • A single “mega‑model” winner. Portfolios and routing will dominate.
  • Perpetual step‑function model jumps. After the turn, improvements are steady, not shocking.
  • Endless greenfield. The bulk of value comes from integrating with existing systems and constraints.

Jevons’ Paradox & AI Efficiency

Efficiency doesn’t guarantee lower total consumption. As models get cheaper and faster, usage expands, and aggregate compute/energy can rise. Infrastructure pressure persists even as perf/W improves.

  • Classic overview of Jevons’ Paradox: The Coal Question by William Stanley Jevons (EconLib) (link)
  • Rebound effects survey (OECD) (link)

Environmental / Regulatory Wildcards

External shocks can change timing and depth of the turn.

The Great Refactoring: Why Coase’s theorem matters now

Coase’s insight: firms exist when it’s cheaper to do work inside than to contract outside. AI slashes coordination, search, and negotiation costs—drafting, analysis, vendor screening, compliance checks, integration glue. As transaction costs drop, boundaries shift: more work moves to markets and small teams; platforms standardize interfaces; vertical stacks unbundle into service primitives; procurement flips from annual RFPs to API‑level routing based on real‑time price/quality/SLA. Post‑turn, winners expose clear APIs, automate governance, and route work to the best‑available provider (internal or external) with measured unit economics. Industries refactor around interfaces: fewer monoliths, more composable services, tighter contracts, faster switching.

Example: retail in the dot‑com deployment

In the 2000s–2010s, retail reorganized as online transaction costs collapsed. Search and discovery moved to marketplaces; payments standardized via APIs; logistics unbundled into third‑party networks (FBA, 3PLs, last‑mile); stores became nodes for click‑and‑collect and returns; ad spend shifted to performance marketplaces with real‑time auctions. Small sellers used these primitives to reach global demand without building the stack; large retailers modularized—exposing inventory, catalog, pricing, and fulfillment via services, then routing orders to the cheapest reliable path in real time. Firm boundaries shifted: less vertical integration, more contracts and APIs; value accrued to platforms that minimized coordination cost (marketplaces, payment rails, ad exchanges, logistics networks) and to brands that mastered routing and unit economics over owning every function. That’s the Coase effect in deployment: when the market becomes cheaper than the firm, retail refactors around APIs, logistics primitives, and performance markets.

Example: media in the web/mobile deployment

In the 2000s–2010s, media refactored as distribution and ad‑market transaction costs collapsed. Distribution moved from owned channels to platforms (YouTube, Facebook, TikTok); delivery standardized via CDNs and RSS/podcast feeds; monetization shifted to programmatic ad exchanges (RTB) and platform revenue shares; subscriptions/paywalls standardized through app stores and payment APIs. Small creators reached global audiences with creator tooling, editors, and SaaS backends; incumbents modularized—separating content creation, packaging, distribution, and monetization into services, then routing inventory to the best‑paying channel in real time. Firm boundaries shifted: less vertical integration, more contracts and APIs. Value accrued to platforms that minimized coordination cost (CDNs, ad exchanges, app stores) and to creators/brands that mastered routing, rights, and unit economics across channels. When the market became cheaper than the firm, media refactored around feeds, CDNs, and auctions.

This Time Is Different — The Capital Stack Has Shifted

Dot‑com boom (≈1998–2000)

U.S. IPO activity peaked at 406 deals in 2000 with proceeds exceeding $100B, after 1999’s flood of 250+ tech listings (sources: Renaissance Capital 2014 Review, p.3 shows 406 IPOs in 2000 and 2025 IPO Outlook; see also 2023 Review). Momentum and multiples were set in public markets.

AI boom (2022–2025)

Post‑2021, U.S. IPO counts stayed in the single‑ to low‑double‑digits with proceeds far below dot‑com peaks (Renaissance Capital: 2025 Outlook). Capital formation shifted private: U.S. venture logged $215.4B invested across 14,320 deals in 2024 with $76.8B raised by funds and $307.8B in dry powder (sources: NVCA/PitchBook 2025 Yearbook PDF and press release); AI captured ~35.7% of global VC deal value in 2024 (PitchBook/NVCA Q4 2024; summary: VentureBeat). Meanwhile, hyperscaler balance sheets are underwriting the buildout of “AI factories”: see capex disclosures in Microsoft FY24 10‑K (link), Amazon 2024 10‑K (link), Alphabet 2024 10‑K (link), and Meta’s capex outlook (link). On the demand side, data‑center electricity is set to more than double to ~945 TWh by 2030, with AI the primary driver (IEA: news, report); water and siting pressures are rising (Uptime Institute: 2024 Global Survey, Water is local).

Conclusion

A pullback in the AI market is not just likely; it is a healthy and necessary mechanism. It will make AI fundamentally more relevant to solving real business problems and decisively shift focus from impressive technological demonstrations to durable, day‑to‑day utility. The winners in this next phase will be organizations that prioritize operational excellence in AI. They will:

  • Make AI dependable: Establish clear boundaries, robust guardrails, rigorous testing, and comprehensive observability.
  • Enable seamless integration: Architect solutions that connect AI to existing data systems and workflows without friction.
  • Maintain cost predictability: Implement strategies like right‑sized models, intelligent caching, efficient retrieval, and optimized scheduling.
  • Govern data responsibly: Ensure unwavering quality, clear provenance, stringent privacy, and full auditability for all data flows.

The strategic imperative is to focus on workflows where predictable performance creates measurable business value—think financial compliance, highly automated customer service, or optimized supply chain logistics—rather than solely on creative or exploratory applications where variability is more acceptable.

Watch for concrete signals: shorter hardware wait times, sustained drops in per‑use pricing, and a deceleration in mega‑spending plans. These indicators suggest it's time to tighten ROI requirements. The productive deployment phase, where real value is created at scale, always follows the reset.

What to Watch: Signals of the Turning Point

  • Capacity: Shorter GPU lead times and queues; easier reservations for training.
  • Cost curves: Sustained drops in per‑use price; faster vendor price‑matching.
  • Model cadence and quality: Incremental releases with stable APIs; converging evals; fewer breaking changes. Less focus on benchmarks and more focus on ROI.
  • Adoption patterns: POCs consolidating into fewer vendors; renewals gated on ROI; tighter governance.
  • App performance and reliability: Better structured output accuracy; lower variance; stronger observability and SLAs.
  • Security and compliance: Clear policies for data handling and hosting; audit trails; sector certifications.
  • Talent and ops: Teams shift from research to platform/product integration; more cost discipline.

Post‑turn, structural advantage comes from running the ‘application OS’ well—routing work, data, and models with reliability and cost discipline. For the more engineering‑minded reader, think of the appendix as the schematic behind the story: a compact system model of the AI installation period. It lays out the growth loop that pulls in capital and the correction loop that shifts leverage to infrastructure, data, orchestration, security, and reliability. Use it to convert noise into signals and strategy into effective action.

Appendix A: The Engine of a Revolution — A System Model of the AI Installation Period

Why Stock‑and‑Flow Diagrams Help

For the more engineering‑minded reader, this is the schematic behind the story. A stock‑and‑flow view makes the installation period legible—so you can see where capital, infrastructure capacity, technical progress, supply, access price, demand, and narrative momentum actually move the system.

  • Stocks accumulate or deplete (e.g., Financial Capital, AI factory Capacity, Expected TAM, AI Supply).
  • Flows are the rates that change them (e.g., Investment, Capacity Additions).
  • Auxiliaries shape relationships without accumulating (e.g., AI Access Price).
  • Feedback loops show propagation: growth loops amplify change; correction loops counteract it.

This framing clarifies nonlinearity, path dependence, and why rational decisions can still produce booms and busts. Delays are real (build and adoption take time) but are treated here as simple notes rather than separate diagram elements.

Here’s the system at a glance. Read it left‑to‑right as a feedback machine: capital builds capacity, capacity drives supply, supply pushes down price, lower price expands the affordable market—which, in turn, pulls in more capital until returns slow.

Installation System Diagram

Content illustration: perez full installation model

Caption: The growth loop: capital → capacity → supply → lower price → larger affordable market → more capital. The diagram decomposes cleanly into parts you can track. Stocks accumulate, flows change rates, auxiliaries shape relationships, and loops explain why booms and corrections are baked into the dynamics.

Model Overview (Installation Period)

  • Stocks
  • Key auxiliary
  • Core relationships
  • Named loops

This is the reinforcement loop that makes the frenzy feel inevitable: capacity growth cuts access price, the affordable market expands, narrative momentum amplifies expectations, and capital follows—until returns disappoint.

Frenzy Dynamic

The speculative reinforcement is straightforward: capacity growth lowers access price, expands the affordable market, and—amplified by narrative momentum—pulls in more money until returns slow inflows.

Content illustration: perez frenzy cycle

How to Use This Model: Key Questions

  • Which signals for each stock can be measured quarterly?
  • Where are the build and adoption lags, and how can they be shortened?
  • What are the Capex vs Opex exposures, and how long is runway if new money pauses?
  • How sensitive is the affordable market to access price? What happens if per‑use cost halves?
  • Which moves work in both up and down cycles: efficiency levers, multi‑sourcing, applications.

References