Definition
The Unified Theory for the Measurement of Fauxlutions (UTMF) is a diagnostic framework proposed by Greg Kihlström in a May 2026 research article, “The High Cost of ‘Cheap Innovation’: Technological Solutionism, Algorithmic Enshittification, and Unrealistic Representation in Marketing.” It provides a structured way to evaluate whether a brand’s technology deployment will be received by consumers as genuine progress or rejected as a “fauxlution” — a faux solution that solves corporate problems while creating consumer ones.
The framework’s central claim, in Kihlström’s formulation: Consumers accept technology only when it augments human value or transparently serves a utility. Consumers reject technology when it simulates human value to deceive or cheapen the interaction.
The UTMF was developed to address a gap in marketing literature. Existing concepts each captured part of the picture — technological solutionism (Morozov, 2013) explained the corporate impulse to apply code to non-coding problems; enshittification (Doctorow, 2023) explained platform decay through value extraction; costly signaling theory, the labor illusion, the Persuasion Knowledge Model, the uncanny valley, and algorithmic aversion each explained specific consumer reactions. What was missing was a single framework that tied these together and could be applied to evaluate a specific brand action.
In marketing, the UTMF matters because it converts a scattered set of “consumers seem to hate this” observations into a structured pre-launch and post-launch evaluation tool. A marketing team considering an AI-driven campaign, a chatbot rollout, a new virtual experience, or any other technology-mediated consumer touchpoint can walk through the four pillars and identify which violations the planned deployment is likely to trigger. The framework doesn’t tell teams what to build. It tells them what they’re risking.
The Four Pillars
Kihlström structures the UTMF around four pillars. Each pillar represents a category of violation that triggers consumer rejection, and each is grounded in one or more established theories. A fauxlution will violate at least one pillar; severe cases violate all four.
Violation of Effort
Grounded in costly signaling theory (Bird & Smith, 2005; Zahavi’s original 1975 work) and the labor illusion (Buell & Norton, 2011).
Principle: Real value comes from visible sacrifice — time, talent, money, and craft.
Costly signaling theory predicts that consumers read the cost of a brand’s effort as a guarantee of quality. The peacock’s tail is the canonical biological example; a high-production-value television commercial is the marketing equivalent. The labor illusion adds that consumers derive subjective satisfaction from witnessing effort, even when the outcome is identical to an effortless alternative.
Generative AI collapses both signals. Production cost approaches zero, which collapses the value of the cost signal. Visible labor disappears, which collapses the labor illusion’s reciprocal satisfaction. Kihlström’s exemplar is the 2024 Under Armour campaign with Anthony Joshua, directed by Wes Walker, which used AI to remix existing high-value footage rather than commissioning a new shoot. The audience read it as a cost-cutting move, not as innovation.
Violation of Truth
Grounded in the Persuasion Knowledge Model (Friestad & Wright, 1994) and moral decoupling (Bhattacharjee, Berman, & Reed, 2013).
Principle: Consumers must be able to identify persuasion attempts in order to apply judgment to them.
The Persuasion Knowledge Model holds that consumers develop, over a lifetime, increasingly sophisticated knowledge of how marketing tactics work. That knowledge is the basis for their defensive judgment. When AI filters like TikTok’s Bold Glamour or AI-generated synthetic content seamlessly bypass that recognition — passing as reality — the persuasion attempt is hidden, and when consumers eventually detect it, the resulting skepticism spreads to everything the brand says.
Moral decoupling adds a twist. Consumers can sometimes separate immoral production methods from a valuable product (the “separate the art from the artist” pattern). But with AI-generated content trained on copyrighted data without consent, and producing low-effort output, the immorality of the method couples to the cheapness of the result. There’s no high-value performance to balance the ethical concerns against.
Violation of Agency
Grounded in the Uncanny Valley of Mind (Stein & Ohler, 2017; building on Mori, 1970) and algorithmic aversion (Dietvorst, Simmons, & Massey, 2015).
Principle: Only humans have minds and the agency to be responsible for their actions.
The Uncanny Valley of Mind extends Mori’s original visual concept to internal states. Consumers are unsettled when a machine appears to think, feel, or possess intent. Algorithmic aversion adds that consumers lose trust in algorithms faster than in humans after seeing identical mistakes — because algorithmic errors are read as evidence of a broken system, while human errors are absorbed into a model of normal fallibility.
The DPD chatbot incident is the comic-tragic case: a frustrated customer convinced the bot to swear at the company and write poems about how useless DPD was, and the resulting agency-attribution to the bot forced DPD to disable it. Moffatt v. Air Canada is the legal case: when the airline’s chatbot invented a bereavement policy, Air Canada argued the bot was a “separate legal entity” responsible for its own actions. The Civil Resolution Tribunal rejected the argument as “remarkable” and held Air Canada responsible.
Violation of Service
Grounded in enshittification (Doctorow, 2023).
Principle: Platforms, products, and services should facilitate a fair and equitable exchange of value.
Where Doctorow’s enshittification describes a sequential decay across three phases of platform life, the UTMF’s service pillar applies the underlying logic to individual brand actions. Any deployment that extracts value from users while degrading their experience — to serve cost reduction, shareholder returns, or business-customer monetization — violates this pillar.
Kihlström cites several cases. Google Search’s deterioration from its famously simple late-1990s interface to a cluttered mix of ads, AI Overviews, and SEO noise. Google Ads’ 1.1-star Trustpilot rating from 886 reviews. Spotify Wrapped 2024, which replaced beloved listening data with AI-generated “micro-genres” like “Pink Pilates Princess Strut Pop” that users called hallucinations and slop. McDonald’s removal of over 100 AI drive-through systems after viral videos of misinterpreted orders, including a customer receiving bacon-topped ice cream.
How to Apply the UTMF
The framework is qualitative and diagnostic. Kihlström notes in the article that a quantitative measurement device would be a useful direction for further research. Until then, the standard application is a structured walkthrough.
Step 1 — Identify the deployment. Define the specific brand action, feature, campaign, or technology deployment being evaluated.
Step 2 — Walk the four pillars. For each pillar, ask the diagnostic question:
- Effort: Does this deployment reduce or hide the visible effort, craft, or cost behind the brand’s work?
- Truth: Does this deployment make it harder for consumers to recognize that they’re being persuaded?
- Agency: Does this deployment have a machine simulating human empathy, judgment, or accountability that the brand can’t actually deliver?
- Service: Does this deployment make the consumer’s experience worse in order to make the brand’s economics better?
Step 3 — Count pillar violations. A single pillar violation is sometimes a recoverable mistake. Two or more is a structural problem. Four is the Willy’s Chocolate Experience.
Step 4 — Map to underlying theories. Each pillar links to a specific behavioral mechanism. Knowing which mechanism is being triggered helps predict the shape of the backlash and where mitigation might be possible.
Step 5 — Decide. Either redesign the deployment to reduce pillar violations, add visible human involvement to offset them, or shelve the initiative.
The Willy’s Chocolate Experience in Glasgow, February 2024, is the framework’s clearest worked example because it hits all four pillars at once:
- Effort: AI-generated promotional imagery substituted for actual set design and event production.
- Truth: Outlandish promises about a chocolate wonderland were used to sell tickets to a near-empty warehouse.
- Agency: AI-realistic images of an experience that didn’t exist robbed customers of the ability to assess what they were paying for.
- Service: The event was cancelled within hours of opening, with police called and children in tears.
Comparison to Adjacent Frameworks
| Framework | Core focus | What it evaluates | How it differs from UTMF |
|---|---|---|---|
| UTMF | Four-pillar diagnostic of tech deployments | Whether a specific brand action will be received as augmentation or rejection | Synthesizes multiple theories under a single applied lens |
| Costly Signaling Theory | Cost as a signal of honesty | Why expensive displays build trust | One input into UTMF’s Effort pillar |
| Persuasion Knowledge Model | Consumer ability to recognize and resist persuasion | How audiences process marketing tactics | One input into UTMF’s Truth pillar |
| Enshittification | Sequential platform decay | Why two-sided platforms degrade over time | Maps to UTMF’s Service pillar |
| Technological solutionism critique | Critique of “there’s an app for that” thinking | Why companies misapply code to social problems | An ideological framing UTMF operationalizes |
| Uncanny valley | Discomfort with near-human imitation | Visual response to humanoid AI | Visual focus; UTMF’s Agency pillar is broader |
| Ethical AI frameworks | Bias, transparency, fairness in AI systems | Whether an AI system is responsibly built | Production focus; UTMF is consumer-reception focused |
| Brand trust models | Drivers of consumer trust in brands | What builds or erodes brand equity | General; UTMF is specific to technology deployments |
Best Practices
Use it before launch, not after. The UTMF is more valuable as a pre-mortem than as a post-mortem. The Klarna, Coca-Cola, Under Armour, and McDonald’s Netherlands cases were all foreseeable; walking the four pillars before launch would have flagged them.
Don’t average the pillars. Some teams will be tempted to weigh wins on one pillar against losses on another — “yes, it violates Effort, but it’s a big Service win.” That’s not how consumer rejection works in practice. Pillar violations stack; they don’t net out.
Map each pillar to a measurable consumer signal. Effort can be measured via perceived-effort surveys. Truth via ad-recognition and skepticism scales. Agency via reaction to error events. Service via satisfaction and retention. Building these measurements before launch lets the team test the framework’s predictions against actual response.
Be especially careful where multiple pillars cluster. Holiday advertising, customer service, premium product experiences, and high-stakes services (finance, healthcare, travel) tend to put all four pillars in play simultaneously. The cost of getting them wrong is higher because more pillars can be violated at once.
Read pillar violations as disclosures. A brand that ships a fauxlution isn’t just making a tactical mistake. It’s disclosing its priorities to its audience. Kihlström’s framing of the trust deficit treats accumulated pillar violations as evidence consumers use to update their model of the brand.
Use it on platforms you don’t own. The UTMF also applies to evaluating the platforms a brand markets through. Spotify, Google Search, Meta, TikTok, and others have varying pillar profiles. Brands building on top of those platforms inherit some of the trust dynamics those platforms generate.
Future Trends
The UTMF was introduced in May 2026, and several directions for its development are noted in Kihlström’s article and emerging from the surrounding research environment.
Quantitative operationalization. Kihlström explicitly calls for a measurement device that converts the four pillars into scored metrics. Likely candidates include perceived-effort scales (adapted from Buell & Norton’s labor illusion research), inferred manipulative intent measures (from the PKM literature), agency-attribution scales (from Uncanny Valley of Mind work), and service-degradation indices (drawing from enshittification’s emerging operational measures).
Cross-cultural calibration. The behavioral foundations of the four pillars travel reasonably well across markets, but the thresholds at which violations trigger backlash vary. Markets with longer histories of mass advertising and more sophisticated persuasion knowledge may show faster activation; markets with different cultural norms around effort, craft, and human service may calibrate the Effort pillar differently.
Regulatory alignment. The Truth and Agency pillars in particular align with emerging regulatory trends — AI disclosure mandates (EU AI Act, FTC guidance), content provenance standards (C2PA), and corporate accountability for AI agent actions (the precedent set in Moffatt v. Air Canada). Brands that pass UTMF evaluation are also likely to satisfy compliance requirements that don’t yet exist but are visible on the horizon.
Integration with brand health measurement. The UTMF could plausibly become a module inside broader brand tracking systems — measured periodically against the consumer base to detect emerging pillar violations before they go viral. This would shift the framework from per-deployment diagnostic to ongoing brand health indicator.
Extension beyond technology. Although the framework was developed for technology deployments, the underlying violations apply to other categories of brand action: greenwashing (Truth violation), staged authenticity in influencer marketing (Truth and Effort), and loyalty program devaluation (Service). Whether the UTMF’s name survives that extension or a more general framework emerges is open.
FAQs
Who proposed the UTMF? Greg Kihlström proposed and authored the UTMF in a May 2026 Agile Brand Guide research article. The framework synthesizes prior work from multiple researchers but is original to Kihlström’s article.
Is the UTMF the same as the fauxlution concept? No. “Fauxlution” is the term for the phenomenon — a faux solution that creates more consumer problems than it solves. The UTMF is the framework for evaluating whether a given brand action constitutes one.
How many pillars must be violated for something to be a fauxlution? At least one. Single-pillar violations can still produce significant backlash if the pillar in question is severely violated. The framework doesn’t set a minimum threshold; it provides a structured way to identify and count violations.
Does the UTMF apply only to AI? No. AI is the most active source of fauxlutions in the current cycle, but the framework applies to any technology deployment — chatbots, VR experiences, dynamic pricing systems, automated underwriting, drive-through automation, and others. The Metaverse and NFT cases Kihlström analyzes predate the generative AI wave.
Can a deployment violate one pillar and still succeed? Sometimes. If the violation is minor and the consumer benefit elsewhere is substantial, single-pillar violations can be absorbed. The pattern across the documented cases suggests that brands which acknowledge the violation and adjust tend to recover faster than brands that defend it.
Is the framework quantitative? Not yet. Kihlström’s article presents the UTMF as a qualitative diagnostic and explicitly notes that further research is needed to develop a measurement device. Each pillar has measurable underlying constructs (perceived effort, inferred manipulative intent, agency attribution, service quality perception), so quantitative operationalization is feasible.
How does this relate to ethical AI frameworks? Ethical AI frameworks (bias, transparency, fairness) focus on whether an AI system is responsibly built. The UTMF focuses on whether a deployment will be received by consumers as legitimate. A system can be ethically built and still trigger UTMF violations if it simulates human qualities, hides effort, or degrades service.
Does the framework apply to B2B? Yes. Business customers have agency knowledge, persuasion knowledge, and effort perception just like consumers do, and they evaluate vendor technology decisions through similar lenses. The Google Ads B2B service degradation is one of the cases Kihlström uses to illustrate the Service pillar.
Where does the framework leave room for legitimate AI use? The UTMF doesn’t argue against AI. It argues against AI deployed in ways that violate one or more pillars. Augmenting human effort (rather than replacing it), supporting transparent utility (rather than simulating reality), assisting human accountability (rather than substituting for it), and improving service (rather than extracting from it) — all are AI applications the framework would judge as legitimate.
What’s the single most important diagnostic question? Whose problem is the technology solving? If the answer is “an internal corporate problem at the expense of the consumer experience,” the deployment is on a fauxlution trajectory regardless of how technically impressive it is. Kihlström returns to this question repeatedly across the article as the underlying through-line of the four pillars.
Related Terms
- Enshittification
- AI slop
- Costly signaling theory (CST)
- Labor illusion
- Persuasion Knowledge Model (PKM)
- Algorithmic aversion
- Uncanny Valley of Mind
- Moral decoupling
- Human premium
Sources
Watson, C., Mckinnon, M., & Bonar, M. (2024, March 2). “Willy Wonka experience: How did the viral sensation go so wrong?” BBC News — https://www.bbc.com/news/uk-scotland-glasgow-west-68431728
Kihlström, G. (2026, May 23). “The High Cost of ‘Cheap Innovation’: Technological Solutionism, Algorithmic Enshittification, and Unrealistic Representation in Marketing.” Agile Brand Guide — https://agilebrandguide.com/the-high-cost-of-cheap-innovation-technological-solutionism-algorithmic-enshittification-and-unrealistic-representation-in-marketing/
Friestad, M., & Wright, P. (1994). “The Persuasion Knowledge Model: How People Cope with Persuasion Attempts.” Journal of Consumer Research, 21(1), 1–31 — https://doi.org/10.1086/209380
Buell, R. W., & Norton, M. I. (2011). “The Labor Illusion: How Operational Transparency Increases Perceived Value.” Management Science, 57(9), 1564–1579 — https://www.hbs.edu/ris/Publication%20Files/Norton_Michael_The%20labor%20illusion%20How%20operational_f4269b70-3732-4fc4-8113-72d0c47533e0.pdf
Dietvorst, B., Simmons, J. P., & Massey, C. (2015). “Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err.” Journal of Experimental Psychology: General, 144(1), 114–126 — http://dx.doi.org/10.1037/xge0000033
Doctorow, C. (2023, January 21). “The ‘Enshittification’ of TikTok.” Wired — https://www.wired.com/story/tiktok-platforms-cory-doctorow/
Morozov, E. (2013). To Save Everything, Click Here: The Folly of Technological Solutionism. PublicAffairs — https://books.google.com/books/about/To_Save_Everything_Click_Here.html?id=XIt2swEACAAJ
Bird, R. B., & Smith, E. A. (2005). “Signaling Theory, Strategic Interaction, and Symbolic Capital.” Current Anthropology, 46(2), 221–248 — https://www.journals.uchicago.edu/doi/10.1086/427115
Bhattacharjee, A., Berman, J. Z., & Reed, A. (2013). “Tip of the Hat, Wag of the Finger: How Moral Decoupling Enables Consumers to Admire and Admonish.” Journal of Consumer Research, 39(6), 1167–1184 — https://doi.org/10.1086/667786
Stein, J. P., & Ohler, P. (2017). “Venturing into the uncanny valley of mind—The influence of mind attribution on the acceptance of human-like characters in a virtual reality setting.” Cognition, 160, 43–50 — https://doi.org/10.1016/j.cognition.2016.12.010
Mori, M. (2012). “The Uncanny Valley [From the Field].” IEEE Robotics & Automation Magazine, 19(2), 98–100 — https://doi.org/10.1109/MRA.2012.2192811
Sookman, B.B. (2024, February 19). “Moffatt v. Air Canada: Misrepresentation by AI Chatbot.” McCarthy Tétrault — https://www.mccarthy.ca/en/insights/blogs/techlex/moffatt-v-air-canada-misrepresentation-ai-chatbot