Promptflation: When Adding “AI” Increases Valuation by 300% Overnight

Feb 26

Dr. Remy Deckard, PhD Department of Speculative Econometrics, Institute for Narrative Liquidity (INL) JUNK: Journal of Unverified Nonsense & Knowledge

Abstract

A recurring anomaly in contemporary markets is the sudden appreciation of firms that append the letters “A” and “I” to otherwise ordinary products, services, and nouns. This paper introduces Promptflation, defined as the rapid inflation of enterprise valuation attributable to the mere presence of “AI” in branding, documentation, or founder breath. Using a faux econometric framework combining Pitch Deck Linguistics (PDL), Term-Frequency Valuation (TFV), and the widely accepted “demo-to-revenue gap” heuristic (DRG), we estimate that the marginal contribution of the token AI to implied valuation frequently exceeds the contribution of revenue itself. In a synthetic panel of 214 startups and 38 publicly rebranded companies, we find (a) a strong positive association between “AI mention density” and valuation multiples, (b) a nonlinear “Overnight 300%” region where the first AI mention produces the largest jump, and (c) evidence of semantic arbitrage, in which firms convert adjectives (“AI-powered”) into capital. While causality remains unproven and likely illegal, the results suggest the word “AI” currently exhibits a higher return on investment than product-market fit.

1. Introduction

Markets are supposed to price fundamentals. Revenue, margins, customers, defensibility, and so on. Yet, from time to time, markets experience episodes where fundamentals become less important than a story. The dot-com era had “.com.” The blockchain era had “chain.” The metaverse era had… a hard time. The current era has AI.

If you have recently walked through a startup accelerator, scrolled past a venture capital newsletter, or been cornered at a barbecue by someone with a new “AI venture,” you may have noticed a peculiar phenomenon: the presence of the term “AI” seems to create value ex nihilo. A slide deck without AI is a deck. A slide deck with AI is a “platform.” A spreadsheet without AI is a spreadsheet. A spreadsheet with AI is “a transformative decision intelligence layer.”

This paper exists to examine the economic physics of that transformation.

We propose that “AI” has become a value multiplier token—a memetic asset that can be appended to products to produce valuation lift disproportionate to operational reality. We call the resulting effect Promptflation, because it resembles inflation (prices rising) and prompts (the incantations used to summon value from large models and larger imaginations).

Our objective is not to make a real financial claim about any specific company. Our objective is to demonstrate, satirically but precisely, how easily an era of enthusiasm can confuse linguistic signals for economic substance.

2. Related Work

Promptflation sits at the intersection of four literatures:

Bubble theory, which asks why prices detach from fundamentals.
Narrative economics, which notes that stories spread like contagions and affect decisions.
Signaling theory, which observes that in uncertain environments, signals often substitute for proof.
PowerPoint studies, a young but rapidly funded field.

Traditional bubble models rely on expectations, leverage, and herd behavior. Narrative economics emphasizes the transmissibility of stories. Promptflation adds a new ingredient: a compact linguistic token that acts as a market catalyst.

Historically, similar tokens have existed (“cloud,” “IoT,” “web3,” “synergy”). But AI appears unusually potent because it borrows legitimacy from real breakthroughs while remaining ambiguous enough to fit any pitch.

3. Conceptual Framework

3.1 Defining Promptflation

We define Promptflation as:

An increase in valuation attributable to AI-associated language that exceeds the expected increase from measurable fundamentals over the same interval.

In nontechnical terms: when a company says “AI” and the market says “take my money,” regardless of whether anything changes besides the PDF.

3.2 The AI Token as a Financial Instrument

The AI token has several unique properties:

Ambiguity: It can mean machine learning, generative models, automation, an if-statement, or vibes.
Prestige borrowing: It inherits credibility from genuine scientific advances.
Low verification cost: It is cheaper to say “AI” than to build it.
Social reinforcement: Investors fear missing out more than they fear being wrong.

These properties allow AI language to behave like a derivative: a contract whose value is based on an underlying asset (the real AI revolution), but which can be traded independently of actual exposure to that asset.

3.3 Valuation as a Function of Words

We model valuation (V) as a function of fundamentals (F) and narrative tokens (N):

V = αF + βN + ε

In traditional finance, β is small and ε is noise. In Promptflation regimes, β becomes large and ε becomes a keynote speaker.

N is operationalized using AI Mention Density (AIMD): the number of AI-related terms per 1,000 words in a pitch deck or press release.

4. Data and Methods

4.1 Sample Construction

Because access to real cap tables would require accountability, we constructed a synthetic dataset with the following sources:

214 startup pitch decks (downloaded from the public internet, allegedly),
38 public-company rebranding events (identified by sudden increases in the phrase “AI strategy”),
1,000 investor tweets containing the phrase “massive,”
12 conference keynote transcripts, and
a small pile of napkins from networking events.

Each observation was assigned:

Revenue (R): actual revenue if available; otherwise “projected.”
Valuation (V): stated valuation if available; otherwise “implied by confidence.”
AIMD: AI Mention Density.
Demo Gloss (DG): a subjective score capturing how cinematic the demo appears.
Agentic Claims Index (ACI): the number of times the word “agent” is used without specifying what the agent does.

4.2 Identification Strategy

We applied three pseudo-identification approaches:

Difference-in-Differences (DiD): comparing valuation changes before and after adding AI language.
Instrumental Variables (IV): using proximity to a major AI conference as an instrument for AI mention density (because everyone comes back and rebrands).
Regression Discontinuity (RD): exploiting the discrete jump when the first “AI” appears in the deck.

We stress that these methods are presented in the style of econometrics, not the reality.

5. Results

5.1 The 300% Overnight Region

Across the sample, the first addition of “AI” to a company description was associated with a sharp valuation shift. The effect was largest when the prior description contained words like “workflow,” “platform,” or “marketplace” and smallest when the prior description already contained “blockchain,” indicating a saturation of hype tokens.

In the DiD specification, companies that inserted “AI” into their tagline experienced an average valuation multiple increase of 3.0× over a short interval—hence the colloquial “300% overnight.” This effect persisted even when revenue was held constant, ignored, or politely absent.

5.2 AI Mention Density Outperforms Revenue

Our core regression suggests that a one-standard-deviation increase in AIMD is associated with a valuation lift greater than a one-standard-deviation increase in revenue.

Interpretation: the market currently rewards saying AI more than earning money.

This is consistent with the modern preference for scalable narratives over inconvenient cash flows.

5.3 Semantic Arbitrage

We observed evidence of semantic arbitrage: firms converting ordinary features into AI adjectives, then converting those adjectives into capital.

Example pathways included:

“autocomplete” → “predictive AI assistant” → “platform” → Series A
“rules engine” → “decision intelligence” → “agentic orchestration” → Series B
“FAQ page” → “generative support copilot” → “customer experience AI” → a press release with fireworks

This arbitrage was most effective when paired with a demo video that includes a blinking cursor and a confident narrator.

5.4 The Demo-to-Deployment Gap as a Feature

Contrary to classical theory, the gap between demo performance and real-world deployment did not reduce valuations. In some cases it increased them, suggesting that ambiguity preserves optionality.

We call this the Schrödinger Product Effect: the product remains simultaneously revolutionary and unfinished until observed by a customer.

6. Mechanisms

6.1 The Legibility Premium

AI language increases legibility. Not because it clarifies what the company does, but because it maps the company into a familiar macro-story: “AI is the future.” Investors can then outsource due diligence to zeitgeist.

6.2 The Halo of Inevitability

AI creates a sense that adoption is inevitable, which shifts investment logic from “will this work?” to “will I look stupid if I miss it?” This is a powerful driver because shame is a stronger force than spreadsheets.

6.3 Credential Substitution

In uncertain technical domains, words can substitute for credentials. A founder with a convincing slide that says “Proprietary AI” is treated as adjacent to a research breakthrough, even if the proprietary element is a dropdown menu.

7. Robustness Checks

We performed robustness checks consistent with JUNK standards:

Removed all companies with revenue greater than zero to ensure the result was not contaminated by reality.
Re-ran regressions using only companies with mascot logos, finding stronger effects.
Controlled for “founder charisma,” proxied by the number of turtleneck photos.
Introduced a placebo token (“AI-ish”), which produced a smaller but still respectable valuation lift.

The findings remained directionally persuasive.

8. Discussion

Promptflation is not merely a joke about buzzwords; it is a cautionary tale about how markets behave under uncertainty. When the underlying technology is genuinely powerful, the incentive to attach oneself to it becomes overwhelming. This creates a semi-rational frenzy: it is not irrational to believe AI will be important, but it may be irrational to assume every AI-labeled company is therefore valuable.

The most dangerous period in an innovation cycle is not when the innovation is fake; it is when it is real, but the social incentives to exaggerate are stronger than the incentives to be precise.

Promptflation thrives in this gap.

8.1 Societal side effects

When valuation becomes responsive to language rather than outcomes, firms optimize language. This can divert resources from engineering toward branding, from product reliability toward keynote aesthetics, and from customer value toward the choreography of inevitability.

In practical terms, it means more time spent naming things “AgenticFlow” and less time spent fixing login bugs.

8.2 The long-run equilibrium

Eventually, reality returns. When enough buyers interact with enough products, some fraction will work, some will not, and the word AI will lose some of its magical lift.

But until then, Promptflation remains profitable as a strategy.

9. Policy and Governance Recommendations

We recommend the following interventions to stabilize the narrative economy:

A mandatory “AI disclosure label” similar to nutrition facts, listing what the system actually uses.
A “buzzword tax” on press releases exceeding a threshold of AIMD.
Investor sobriety checkpoints at conferences.
A public database of the phrase “powered by AI” and what it meant, in hindsight.

These recommendations will not be implemented.

10. Limitations

This paper has several limitations:

It is satire.
The dataset is synthetic and/or emotionally sourced.
The econometrics are performance art.
It treats language as a financial instrument, which is absurd.

However, it also reflects a real temptation: to mistake the presence of a fashionable token for the presence of substance.

11. Conclusion

We introduced Promptflation: the phenomenon where adding “AI” to a company’s story increases valuation disproportionately, sometimes by “300% overnight,” often without commensurate changes in fundamentals. Our faux econometric analysis suggests that AI mention density is currently a stronger predictor of valuation than revenue, especially in environments dominated by hype, ambiguity, and the fear of missing out.

The lesson is not that AI is worthless. The lesson is that words can become assets when everyone agrees to price them as such.

Until the narrative cools, we advise founders to consider their options carefully. They can build a business.

Or they can add two letters.

References

Deckard, R. (2026). Narrative Liquidity and the Price of Words. INL Press.
FOMO, A. & Hype, B. (2025). “Token-Based Valuation in the Post-Deck Era.” Quarterly Review of Pitchcraft.
Slides, P. (2024). PowerPoint as Capital: A Field Guide.
Agentic, S. (2026). “On the Use of the Word ‘Agent’ Without Any Explanation.” Annals of Ambiguity.
Benchmark, B. (2025). How to Win With Metrics That Don’t Matter.
Schrödinger, P. (2026). “Products in Superposition: Demo vs Deployment.” Journal of Quantum Commerce.

Satire Disclaimer: This article is satire intended to illustrate how persuasive-looking correlations and buzzword signals can masquerade as rigorous analysis. Do not use it as investment advice, and do not forward it to a founder mid-raise unless you enjoy being uninvited from demos.

Kurt Richardson