The AI Correction Is Coming . And I Feel Fine

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 Macluan

In a recent conversation, OpenAI CEO Sam Altman notably suggested that AI might currently be in a "bubble" – a sentiment that perfectly aligns with this essay. Far from being a cause for alarm, this perspective underscores our core argument: that the current AI boom, like all technological revolutions before it, is following a predictable arc toward an inevitable market correction. This essay aims to equip you to understand this cycle, identify its signals, and strategically position yourself to thrive in the productive deployment phase that follows

The AI revolution is here, not as a singular event, but as a predictable cycle. For technology leaders and engineers, understanding this cycle is paramount. This guide helps you pinpoint today's AI moment within the boom‑to‑deployment arc of technological revolutions. We’ll examine the structural dynamics driving current expansion, identify the signals indicating an inevitable market correction, and clarify how falling access costs will shift strategic opportunities. The goal is to concentrate durable leverage where it truly matters—in infrastructure, data, orchestration, security, and reliability—so you can set clear expectations, time decisions, and plan what to build, buy, and budget next.

Here, we define a few key terms to ensure precision:

• Installation Phase: The early period of a technological revolution marked by radical innovations, intense investment, and speculative exuberance in new infrastructure and basic technologies.
• Frenzy Period: The height of speculation within the installation phase, often characterized by overbuilding and inflated valuations, driven by financial capital.
• Turning Point: The inevitable market correction that occurs when financial exuberance collides with real‑world bottlenecks, leading to a repricing of assets and a shift in leadership from financial to production capital.
• Deployment Phase: The period following the turning point, characterized by widespread, practical application of the new technology, driven by production capital focused on reliability, standards, and integration into the real economy.
• Disruption: The process by which new technologies or business models displace established ones, often by serving overlooked segments or new performance dimensions.
• AI Factory: An integrated compute utility that combines specialized systems—power distribution and management, thermal regulation and cooling, computational accelerators, high‑bandwidth networking, distributed storage architecture, and comprehensive data/ML orchestration frameworks—built specifically to train and serve AI models at industrial scale with reliability and efficiency.

A disclaimer: The thresholds and timelines discussed here are speculative, reflecting macro‑economic patterns rather than precise market forecasts. Actual timing and specific impacts will vary across sectors and geographies. This is not investment advice; it’s a framework for strategic planning.

Historical Frameworks#

To navigate the current AI landscape, I apply two complementary frameworks: Carlota Perez's model of technological revolutions and Clayton Christensen's theory of disruptive innovation. Together, they provide a powerful lens for understanding not just the current boom, but the structural dynamics of the inevitable correction and the productive period that follows.

Carlota Perez maps the big cycle of technological revolutions: an installation phase (early breakthroughs, investment, and a frenzy of speculative capital) building toward an inevitable turning point (a market reset), and finally, a deployment phase (broad, productive use of the technology in the real economy). History shows that while many revolutions see a compressed narrative and intense hype, the widespread, productive deployment of the core technology often lags the initial excitement by a significant margin. The real value accrues not to those who merely fund the frenzy, but to those who build for the long haul.

Clayton Christensen's work on disruptive innovation explains how new entrants win. Disruptors typically begin by serving new performance dimensions or overlooked customer segments, often with a "good enough" or initially inferior technology. Over time, as the technology matures, these disruptors improve their offerings, eventually moving into the mainstream and displacing incumbents. This framework helps us understand how, after the market reset, value shifts decisively toward products and applications that everyday customers and organizations can actually use effectively and affordably.

These frameworks illustrate that while market narratives may ebb and flow, the underlying logic of technological adoption and value creation remain consistent. This combination predicts both the inevitable market correction and the prolonged, productive period of widespread deployment that will follow.

Current State: Structural Dynamics versus Triggers#

AI is currently deep within its installation period, characterized by a massive build‑out of infrastructure. This phase is defined by powerful structural dynamics and potential triggers that could accelerate the shift towards a market correction.

Structural Dynamics:

• Capacity Build‑Out: The current era is marked by unprecedented investment in AI Factories—the physical plant of the AI economy. This includes specialized data centers, advanced chip fabrication, and new power infrastructure designed to handle the immense computational demands of AI.
• Access Cost Declines: As AI infrastructure capacity expands and inference efficiency improves, the per‑use cost of AI (e.g., price per million tokens) inevitably falls. This makes AI more accessible and economically viable for a broader range of applications.
• Efficiency Improvements: Continuous breakthroughs in AI algorithms, model architectures, and inference optimization (e.g., quantization, distillation) dramatically increase the output per watt/dollar. This further drives down effective access costs.
• Adoption Lags: While the technology is advancing rapidly, the rate at which businesses and consumers can effectively integrate and absorb AI into their workflows often lags the pace of innovation and infrastructure build‑out. This gap creates fertile ground for a market repricing.

Triggers and Accelerants:

Several external factors could act as triggers or accelerants for the market correction:

• Regulation: Evolving global AI regulations, compliance mandates, and data governance frameworks will impose new requirements, potentially slowing deployment or increasing operational costs for less mature solutions.
• Supply‑Chain Constraints: The deep dependence on a narrow chain of semiconductor fabs, specialized tools, and raw materials introduces inherent fragility. Any significant disruption (e.g., geopolitical events, natural disasters) could trigger rapid recalibration.
• Geopolitics: Geopolitical tensions (e.g., around Taiwan and semiconductor production) can fundamentally reshape investment timing, geographic strategies, and access to critical components.
• Environmental Limits: The escalating demand for energy and water from AI Factories is beginning to collide with environmental capacities and resource availability, leading to permitting delays and increased scrutiny.
• Energy Grid Capacity: The sheer electricity demand of AI is straining existing power grids. Limitations in generation and transmission capacity represent a fundamental physical constraint on continued rapid expansion.

Signals to Watch / Early Warning Signs#

Monitoring specific signals provides early warning signs of the approaching turning point. These indicators help gauge where the market is headed structurally, not just sentimentally.

The massive scale of AI‑driven demand is already evident:

• Data Center/Capacity Demand Growth: McKinsey estimates that demand for AI‑ready data center capacity may rise approximately 33% per year from 2023 to 2030 in mid‑range scenarios, potentially reaching 171–219 GW globally. [McKinsey 2023]
• Electricity Consumption: The International Energy Agency (IEA) projects global electricity consumption for data centers will more than double by 2030, reaching around 945 terawatt‑hours (TWh), driven significantly by AI workloads. [IEA 2024] Goldman Sachs forecasts that data centers will account for up to 40% of net new electricity demand in the U.S. between now and 2030, with AI being a major contributor. [Goldman Sachs 2024]
• AI Models' Energy Footprint: Training large AI models like GPT‑4 can consume significant energy, estimated at 50 gigawatt‑hours for GPT‑4 alone—equivalent to powering San Francisco for three days. [GPT‑4 Energy Est.]

Beyond these macro indicators, watch for:

• Project Announcements: Monitor significant new AI infrastructure projects (e.g., Qatar/Ooredoo, UK’s Stargate) as indicators of continued, but potentially overbuilt, capacity expansion.
• Power/Grid Constraints: Look for public examples of power grid limitations impacting data center expansion or specific regional electricity strain.
• Utilization Rates: As capacity expands, watch for utilization rates of AI Factories and GPU clusters. Falling utilization indicates supply outstripping demand, accelerating price compression.
• Lead Times: Track GPU lead times. When they consistently drop below three months, the market shifts from supply‑constrained to demand‑constrained.
• Cost per Inference: Monitor the effective per‑use cost of AI (e.g., $/1M tokens). Sustained declines signal increased supply and competitive pressure.

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 build-out 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 impending correction is not a disaster; it’s a re-foundation. For builders and leaders, the goal is to shift your focus to where durable leverage will concentrate post‑turn.

What to Prioritize (Build/Buy):

• Infrastructure (Physical & Digital): While initial speculative overbuilding occurs, reliable, efficient AI infrastructure (both physical hardware and the software to manage it) remains foundational. Build/buy for long-term operational excellence.
• Orchestration (LLM-OS): This represents a new computing paradigm where the LLM acts as the central "brain" of the system. It uses natural language to coordinate resources and execute complex tasks by dispatching them to integrated software tools. (See the Appendix for a detailed definition.) Building its core components is critical for creating the reliable, high-value applications of the deployment phase.
• Reliability & Resilience: Tools and practices that ensure AI systems are robust, fault-tolerant, and perform predictably under load. This includes advanced observability, testing frameworks, and incident response for AI.
• Data Strategy: High-quality, well-governed data (provenance, privacy, auditability) is a durable asset. Invest in robust data pipelines and knowledge bases (e.g., for RAG systems).
• Compliance & Security: Tools and expertise that address AI ethics, regulatory compliance, and security vulnerabilities will become non-negotiable for enterprise adoption.

What to Be Cautious About (Budget Wisely):

• Over-reliance on Single Mega-Models: The "one model to rule them all" approach is shifting. Prioritize flexibility and model portfolios.
• Unconstrained Scaling: Blindly scaling compute without clear ROI or addressing bottlenecks is a recipe for wasted capital.
• "Demo-ware": Solutions that are impressive but lack the robustness, integration, or cost-effectiveness for real-world deployment.

How to Plan & Learn:

• Build for Resilience: Design architectures that can flex to changing costs and capacity.
• Invest in Feedback Loops: Implement metrics and observability to identify leading indicators of capacity, cost, and adoption.
• Embrace Iteration: Plan for continuous adaptation based on real-world signals, not just initial projections.
• Focus on Fundamentals: Double down on core engineering principles—system design, performance, security, and data governance.

Turning Point & What Success Looks Like Post-Turn#

The turning point itself will be marked by a brutal recalibration of valuations, often painful for those caught unprepared. Success in the subsequent deployment phase, however, will look distinctly different.

Indicators that Deployment Has Arrived:

• Stable APIs & Ecosystems: A mature ecosystem of standardized, stable APIs, frameworks, and developer tooling for building AI applications. Fewer breaking changes in foundational models.
• ROI-Driven Renewals: Customer renewals for AI products are gated on clear, measurable ROI, not just technological novelty.
• Reduced Model Volatility: Fewer dramatic shifts in model capabilities or quality with each release; a focus on incremental, predictable improvements and maintenance.
• Sector-Specific Adoption: Widespread, deep adoption of AI in critical workflows across diverse industry sectors, driven by proven value.
• Predictable Technology Stack with Good Developer Experience (DX): Availability of mature, well-documented tools, frameworks, observability platforms, and model serving/inference APIs that enable efficient and reliable development.

Unpredictable vs. Predictable Winners:

While identifying specific winning companies is difficult, the types of winning strategies are more predictable. Expect traditional sectors that were slow to adopt to become massive winners by leveraging accessible AI. Success will be less about the next foundational model breakthrough and more about its meticulous, reliable application.

Jevons’ Paradox & AI Efficiency#

As AI models become dramatically more efficient (e.g., smaller, faster, cheaper to infer), Jevons' Paradox suggests that the overall demand for compute and energy might not decrease. Instead, cheaper access could enable an explosion of new, previously unviable use cases, leading to an increase in total resource consumption. This implies continuous pressure on infrastructure despite efficiency gains.

Environmental/Regulatory Wildcards#

Critical external factors could dramatically shift the shape and timing of the AI cycle:

• Taiwan Risk: The concentration of advanced semiconductor manufacturing in Taiwan (as highlighted by Bloomberg's "Why AI Can't Exist Without Taiwan") poses a significant geopolitical risk that could severely disrupt global chip supply.
• Power Grid Limits: Widespread and persistent energy grid constraints could force a slower, more localized AI build-out than currently projected.
• Water Usage: Public and regulatory backlash against the massive water consumption of AI Factories could impose new limitations or drive innovation in cooling technologies.

These wildcards underscore the systemic fragility that must be factored into strategic planning.

This Time Is Different — The Capital Stack Has Shifted#

In the dot‑com era, the frenzy was IPO‑led and retail‑visible. In the AI era, it’s PE/VC‑led and infrastructure‑heavy.

• Dot‑com boom (≈1998–2000):
• AI boom (2022–2024):

In short, the dot‑com frenzy was driven by public listings and retail flows; the AI frenzy is driven by private balance sheets and infrastructure build‑out. The cycle is the same—install, frenzy, turn, deploy—but the capital stack is different, and so is how the correction propagates.

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.