When Knowledge Hyperinflates: The AI‑Driven Collapse of Scarcity

In 1923, Germans carted wheelbarrows of marks to buy bread.

When money grows faster than the actual goods and services produced, prices jump, and the currency quickly loses value. Economist Phillip Cagan drew the line at 50 % price growth per month; cross it and you are in “hyperinflation.” [1] A century later, something similar is happening to knowledge. LLMs have slashed the cost of content creation to almost zero. I can prompt can generate pages of text, code, slides, and even draft research papers. That flood of supply drains value from each insight, just as overprinted money cheapens currency.

Welcome to knowledge hyperinflation.

Three engines are pumping up the knowledge supply

1. Generative AI output explosion: a single employee with ChatGPT or GitHub Copilot can produce the text or code equivalent of dozens of human hours in minutes.
2. Synthetic recursion risk: as more AI‑generated text is used to train the next generation of models, the rare, out‑of‑distribution human examples get washed out; a dynamic researchers call model collapse. [2]
3. Ubiquitous workplace adoption: surveys of 10,600 workers across 11 countries show 92 % of Indian employees already use GenAI daily, and 72 % worldwide use it “regularly.” [3]

If knowledge is easier to obtain, what remains scarce?

In a world where access to information is abundant and ever-expanding, the true bottlenecks to value creation are no longer rooted in knowing more but in knowing what to do with that knowledge. Several capabilities remain scarce in this age of knowledge hyperinflation. Each represents a competitive advantage that cannot be commoditized, and each is being augmented at scale by organizations using the o9 platform.

1. Problem Framing: Successful planning begins with asking the right questions and applying relevant constraints. One automotive company uses o9 to embed ESG considerations—like carbon emissions and transport impacts—directly into its Integrated Business Planning. This shifts the planning focus from isolated cost targets to more holistic, multi-objective optimization across cost, service, and sustainability.
2. Context Stewardship: To support that broader planning lens, companies must harness both data and domain expertise. A global consumer goods firm achieves this by combining automated baseline forecasts in o9 with the contextual inputs of its planners. As the system learns from overrides and exceptions, it transforms individual know-how into structured intelligence that enhances forecast quality and scales organizational learning.
3. Critical Analysis and Judgement: With this contextual intelligence in place, planners are better equipped to analyze and act on AI-driven outputs. An industrial manufacturer transitioned from manually selecting models to using o9’s ensemble forecasting, which dynamically blends the best algorithms. Paired with intuitive visualization tools, this enables planners to interpret forecasts more accurately and make confident decisions in production and procurement.
4. Provenance and Trust: Ensuring those decisions are based on consistent and credible data is crucial. A multinational food and beverage company established forecasting governance with o9, automating low-impact SKUs while enforcing edit controls through transparent dashboards. This structure minimizes bias, reinforces consistency, and builds trust in the system’s recommendations.
5. Outcome Ownership: Ultimately, value comes from turning insight into impact. A global beverage brand uses o9 to automate forecasts across thousands of SKUs, freeing planners to focus on strategic areas like demand shaping and portfolio planning. With performance dashboards directly tying forecast accuracy to service and inventory outcomes, teams stay accountable to results and empowered to drive them.

From the Gold Standard to a Decision Standard

When nations quit backing money with gold, currencies survived only because citizens trusted the central bank’s rules. In a knowledge glut, measured decision quality becomes the new hard asset. Facts hold value only if they move the scoreboard.

Despite heavy spending on planning tools, most companies still lack a feedback loop to explain why reality veers from the plan. Inventory piles up, orders cancel, forecasts miss, and no one can pinpoint the cause.

o9’s Decision Replay System—inspired by post-game film study—fills that gap:

1. Record every call: Inventory targets, production plans, price moves.
2. Compare with reality: Did service levels, margins, or stock positions meet expectations?
3. Run causal forensics: Multi-layer analytics reveal whether bad forecasts, safety-stock rules, or demand shocks drove the miss.
4. Feed lessons back: Agentic AI autonomously learns, updates rules, and proposes corrective actions.

The loop creates an institutional “vault” of validated insight while actively unlearning outdated patterns and re-learning from fresh causal signals, precisely the meta-skill humans will need over the next decade.

The result is decision velocity; a stable “currency” backed not by report volume but by outcome lift [5].

Where do we go from here?

When inflation last spiraled out of control, societies established central banks, issued inflation‑linked bonds, and imposed tight monetary rules to rein it in. The knowledge economy needs analogous tools: provenance protocols, decision‑linked metrics, and institutional memories that reward validated learning over sheer volume.

What remains uncertain is who will play the role of lender‑of‑last‑resort when collective judgment falters. That is the debate I’m hoping to spark.

See o9's Agentic AI innovations, in person

On September 10th at aim10x Americas, our new three-stage conference, our CEO and Co-Founder—alongside leaders from New Balance, Keurig Dr Pepper, Molson Coors North America, Five Below, Smurfit WestRock, Acuity, and Kubota Tractor—will be live on stage.

They won’t just be talking about the future. They’ll be demonstrating how these capabilities deliver on the ideas I’ve explored here. These are not incremental upgrades. They are fundamental shifts in capability that form the foundation of the Human+AI operating models modern enterprises will rely on in this decade and beyond.

If the purchasing power of knowledge is falling, this is your chance to see what just might become the new gold standard. You can register here.

Sources

1. Cagan, P. (1956). The monetary dynamics of hyperinflation. In M. Friedman (Ed.), Studies in the quantity theory of money (pp. 25–117). University of Chicago Press.
2. Shumailov, I., Shumaylov, Z., Zhao, Y., Crowley, E. J., Kohli, P., & Gal, Y. (2023). The curse of recursion: Training on generated data makes models forget (arXiv:2305.17493). arXiv. https://arxiv.org/abs/2305.17493
3. Boston Consulting Group. (2025, June 26). AI at work: 2025 survey results. As reported in The Economic Times. https://economictimes.indiatimes.com/tech/technology/nine-in-10-indian-employees-embracing-genai-tools-well-ahead-of-global-average/articleshow/110432195.cms
4. McKendrick, J. (2024, April 30). AI puts the squeeze on the shrinking half-life of skills. Forbes. https://www.forbes.com/sites/joemckendrick/2024/04/30/ai-puts-the-squeeze-on-the-shrinking-half-life-of-skills/
5. Frömer, R., Dean Wolf, C.K. & Shenhav, A. Goal congruency dominates reward value in accounting for behavioral and neural correlates of value-based decision-making. Nat Commun 10, 4926 (2019). https://doi.org/10.1038/s41467-019-12931-x

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