Stir Trek 2026

Deceptive
UX Patterns

Exposing Manipulative Design in the Age of AI

Vitaliy Matiyash · Staff Engineer · Columbus, OH

Raise your hand if...

💳

You've accidentally started a subscription you didn't want?

🔍

You've spent >5 mins looking for an "Unsubscribe" link?

❌

You've struggled to find the "Close" button on an ad?

🤖

You've apologized to ChatGPT or said "please" to an AI?

[Read each card as it appears — pause for hands] "Raise your hand if you've accidentally started a subscription you didn't want." [Look around — nod] "Yeah, most of us." "Spent more than 5 minutes hunting for an Unsubscribe link?" "Struggled to close an ad because the X was microscopic or fake?" [Pause on last one] "And this one — have you ever said 'please' or 'thank you' to ChatGPT? Maybe even apologized to it?" [Wait for laughs/raised hands] "Keep your hand up if you've been 'confirm-shamed' by a button that says 'No thanks, I hate saving money.'" "Here's the thing — WE are the experts. We BUILD this stuff. And even we get tricked. That last one — apologizing to an AI — that's the new frontier. We'll get there." [~90 seconds — advance]

Deceptive Patterns

"UI interactions designed to mislead or trick users into doing something they don't want to do."

— Harry Brignull (2010)

2010 Term Coined (E-Commerce Era)

2014 Growth Hacking & "Nudging"

2021 Congressional Hearings (Gamification)

2024 EU AI Act & FTC "Click-to-Cancel"

2026 Agentic AI Deception

"Harry Brignull coined this term in 2010. Back then, deception was clumsy — literally tiny gray text on a gray background. You could spot it if you squinted." [CLICK] "Then came 2014 — the 'Growth Hacking' era. Product teams stopped asking 'Is this useful?' and started asking 'Does this convert?' That shift in vocabulary changed everything." [CLICK] "By 2021, the consequences hit mainstream. Congressional hearings on social media addiction. People losing money. Government stepping in." [CLICK] "2024 was the regulatory inflection point — the EU AI Act and FTC's Click-to-Cancel rule. We'll look at both later." [CLICK] "And now — 2026. The threat has mutated. We aren't fighting bad CSS anymore. We are fighting persuasive AI systems that adapt to each individual user." "That 16-year arc — from layout tricks to AI manipulation — is what this talk is about." [~90 seconds — advance]

The Physical Predecessor

The Gatwick "Forced Path"

London Gatwick Airport's mandatory retail experience.
Security leads directly into a winding shop before the lounge.
If priority is "time efficiency," why the duty-free maze?

"Before we look at code, let's look at bricks and mortar. How many of you have flown through London Gatwick?" [Brief pause] "At Gatwick, there's a layout called a 'forced path.' After security, you MUST walk through a long, winding corridor packed with duty-free shops. There's no shortcut — you literally cannot reach your gate without passing through retail." "40 million passengers a year. Every one of them forced through the shop. Those 'accidental' purchases — a bottle of whiskey, some perfume — they're worth hundreds of millions to the airport." "THIS is the ancestor of every digital dark pattern. Physical architecture, manipulating behavior. The same principle, just translated from floor tiles to pixels." [~60 seconds — advance to section divider]

THE EVIDENCE

Classic Patterns (2010–2023)

The Hall of Shame

16 Classic Deceptive Patterns

Bait and Switch

Disguised Ads

Forced Continuity

Friend Spam

Hidden Costs

Misdirection

Price Comparison Block

Privacy Zuckering

Roach Motel

Trick Questions

Confirmshaming

Nagging

Fake Urgency

Fake Scarcity

Fake Social Proof

Preselection

[Grid appears in 4 waves] "Here's our 'Hall of Shame' — 16 distinct manipulation patterns. When you see them all together like this, you realize this isn't a collection of accidents. It's a massive, engineered LIBRARY of deception." [Let the grid fill] "Bait and Switch, Disguised Ads, Forced Continuity, Friend Spam..." [Wave 2] "Hidden Costs, Misdirection, Price Comparison Prevention, Privacy Zuckering..." [Wave 3] "Roach Motel, Trick Questions, Confirmshaming, Nagging..." [Wave 4] "And the psychological pressure trio: Fake Urgency, Fake Scarcity, Fake Social Proof, plus Preselection." "These have names because they're STUDIED. Researchers have cataloged them. Let me show you the greatest hits in action." [~45 seconds — advance]

Misdirection & Visual Hierarchy

The big button isn't the action you want. It's the action they want.

Confirmshaming

Using guilt-laden language to manipulate the opt-out decision.

"No thanks, I hate good times." — actual button text

Fake Urgency

Creating false time pressure to bypass rational "System 2" thinking.

The Resetting Timer: Counts down to zero, then restarts.
The Phantom Deadline: "Sale ends in 2h" (but is permanent).
The Pressure Cooker: "6 people are looking at this right now!"

"Fake urgency exploits what behavioral scientists call 'System 1 thinking' — the fast, emotional, reactive brain." [CLICK through images] "The Resetting Timer: counts down to zero, then silently restarts. I've seen JavaScript that literally does `new Date()` on page load." "The Phantom Deadline: 'Sale ends in 2 hours' — except it's been 2 hours since 2019." "The Pressure Cooker: '6 people are looking at this right now!' Is that true? Or is that `Math.floor(Math.random() * 15) + 3`?" [CLICK — booking site] "This Booking.com screenshot has FOUR urgency tricks on ONE page. Timer, viewer count, 'last room' badge, and a red color scheme. It's designed so you can't engage your rational brain at all." [~60 seconds — advance]

Case Study: Hurrify

When the "Lie" becomes a SaaS Product

User Interface (The Trap)

FAKE DATA

"Hurry! Sale ends in 11:59. 87% of items sold!"

Admin Dashboard (The Secret)

Merchant manually sets "Sold %"
"Random Stock" range: [5] to [20]
No connection to real inventory.

BANNED BY SHOPIFY (2021)

"Now here's where it gets truly ugly. This wasn't just a one-off script by some rogue developer. This was a PRODUCT. A SaaS business. They PRODUCTIZED the lie." [CLICK — user interface] "Look at the user interface — red countdown bar, pulsing timer, '87% sold.' It looks legitimate. It looks like it's connected to inventory." [CLICK — admin panel] "Now look at the admin panel. The merchant isn't connecting to an inventory API. They're literally typing '90' into a 'Sold percentage' textbox. They're choosing a random range for stock numbers. There is ZERO connection to actual inventory. This is the smoking gun." "Hurrify had thousands of paying customers. Merchants paid money to lie to their customers." [CLICK] "In 2021, Shopify finally banned Hurrify and similar apps for violating their partner terms. But by then, the damage was done — millions of purchases influenced by fabricated data." [~75 seconds — advance]

Fake Scarcity

The "Only 1 Left" Engineering Lie

The Mechanism

Falsely claiming limited availability to trigger FOMO.

Hard-coded Values: "Only 2 left" regardless of true inventory.
Low-Stock Badges: Red text to incite panic.
Research: Mathur et al. (2019) found these are often generated by Math.random().

Technical Implementation

INVENTORY: NULL

Source: Mathur et al., Princeton University (2019) | Harry Brignull (2023)

Fake Social Proof

The Bandwagon Effect... Orchestrated by a Bot

The Mechanism

Fabricated activity notifications to imply popularity.

Toast Notifications: "Bob from Ohio just bought this!"
Simulated Traffic: "38 people viewing right now."
Fake Testimonials: Generated reviews with no verifiable origin.

Technical Implementation

SOURCE: generateRandom()

Source: Harry Brignull (2023) | Deceptive Design Patterns

"Social proof is powerful because humans are social animals. We look to others' behavior as a signal of what's correct. It's the reason you pick the busy restaurant over the empty one." [CLICK] "But when that 'Bob from Ohio just bought this!' notification is actually a client-side script pulling random names from an array with random timestamps — it's not social proof. It's a digital hallucination." [CLICK through images] "Look at the code — it's a simple toast notification with randomized data. No actual purchase event triggers it." "And here's why I'm showing you this one: REMEMBER it. Because AI is about to supercharge fake social proof into something FAR more convincing. We'll get there in about 5 minutes." [~60 seconds — advance]

Roach Motel

Easy to subscribe, impossible to cancel

The Pattern

Sign up: 1 click, 30 seconds
Cancel: Phone call, 45 min hold, 6 screens
Amazon Prime cancellation required 6 separate steps (FTC complaint, 2023)

FTC "CLICK-TO-CANCEL" RULE (2024)

Cancellation must be as easy as sign-up. Sellers must provide a simple mechanism to cancel — no phone calls, no chat queues, no guilt trips.

The Asymmetry

"If signup() takes 1 click, cancel() cannot take 10."

Source: FTC.gov (2024) | "Click-to-Cancel" Final Rule

"The Roach Motel is probably the most universally hated pattern. Easy to get in, impossible to get out." "Think about it: signing up for a subscription — one click, 30 seconds. Canceling that same subscription? Phone call, 45-minute hold, a 'retention specialist' who won't take no for an answer, 6 separate confirmation screens." "Amazon's Prime cancellation was SO bad that the FTC's internal name for the investigation was 'Project Iliad' — because canceling was an ODYSSEY." [CLICK — green box] "The good news: in 2024, the FTC passed the 'Click-to-Cancel' rule. Cancellation must now be as easy as sign-up. No phone calls, no retention agents, no chat queues, no guilt trips. This is federal law." [CLICK through Verizon images] "And companies like Verizon are still catching up." "If signup() takes 1 click, cancel() cannot take 10. It's a ratio. And we can measure it." [~75 seconds — advance]

Case Study: Robinhood

Weaponizing Dopamine in Finance

The Pattern

Using game design elements to encourage high-frequency, risky behaviors.

Variable Rewards: "Scratch-off" style reveals for free stock.
Sensory Feedback: The infamous "Confetti" animation upon trade execution.
Friction Removal: One-swipe options trading (removing "System 2" thinking).

User Interface (2019-2021)

DOPAMINE TRIGGER

"Now I want to do a deep dive on one company — because Robinhood's story is the perfect cautionary tale. It shows the full arc: innovation, exploitation, consequences, and redemption." [CLICK] "Robinhood didn't just make trading easy — they made it ADDICTIVE. They borrowed game design principles directly from slot machines." "Variable rewards: scratch-off style reveals for free stock. Your brain doesn't know what you'll get — that uncertainty IS the addiction." "Sensory feedback: the infamous confetti animation. Every time you execute a trade — even a bad one — confetti! Dopamine!" "And friction removal: one-swipe options trading. Options! Complex financial derivatives! One swipe! No cooling-off period, no 'are you sure?' dialog." [CLICK] "The result? It wasn't investing. It was engagement farming with people's life savings." [~75 seconds — advance]

The Fallout

When "Fun" becomes a $7.5 Million Fine

2024 SETTLEMENT

$7.5 Million Penalty

Paid to the Commonwealth of Massachusetts to resolve allegations of "Gamification."

"Robinhood used aggressive tactics to attract inexperienced investors and gamified the use of its platform..."
— Galvin (Secretary of the Commonwealth)

The Risky Result

Data showed Robinhood users traded 88x more options contracts than peers at Schwab.

Source: Associated Press (2024) | "Robinhood Agrees to Pay $7.5 Million Fine"

"Design choices have LEGAL consequences. Let that sink in." [CLICK] "In 2024, Robinhood settled with Massachusetts regulators for 7.5 million dollars. Not for fraud in the traditional sense — for gamification. Their UI design was legally deemed to be investment manipulation." [CLICK] "The Secretary of the Commonwealth said: 'Robinhood used aggressive tactics to attract inexperienced investors and gamified the use of its platform.' That's a regulator describing CSS and JavaScript." [CLICK] "And the data backs it up: Robinhood users traded 88 TIMES more options contracts than peers at Schwab. Same market, same access, 88x more risk-taking. The only difference? The interface." "Your UI/UX choices are now considered investment advice. Let THAT sit with the product managers in the room." [~75 seconds — advance]

The Pivot (2025)

From "Casino" to "Institution"

Systemic De-Gamification

Visual Rebrand: Shifted to serif fonts and muted colors to signal maturity.
Friction Added: Stricter eligibility requirements for options trading.
Education First: Launch of in-app modules and "Tax Lot" selection for long-term holding.

New Design Philosophy

Confetti & Emojis Data & Analysis

"Scratch to Win" 24/7 Phone Support

"A new visual identity reflecting our maturity." — Robinhood Design Blog

Source: Robinhood Newsroom (2025) | SEC Filings

"But here's the redemption arc. They grew up." [CLICK] "In 2025, Robinhood launched a massive rebrand. Gone are the neon colors, the scratch-offs, the casino vibes." "Visual rebrand: serif fonts, muted earth tones. They're signaling 'We're Goldman Sachs now, not Las Vegas.'" "Friction ADDED: stricter eligibility for options. You now have to prove you understand what you're buying." "Education first: in-app learning modules, tax-lot selection tools for long-term holding." [CLICK] "They replaced confetti with data visualizations. Replaced 'Scratch to Win' with 24/7 phone support." "This is the arc we WANT to see: public pressure + regulatory pressure = genuine change. It proves that we CAN push back. These patterns are not inevitable." [~60 seconds — advance to Bridge]

What Changed?

2010

Visual Deception

Tiny gray text, hidden checkboxes, misleading buttons

Hard-coded HTML

2020

Structural Deception

Gamification, infinite scroll, engagement loops

A/B Tested & Optimized

2026

Relational Deception

AI sycophancy, emotional manipulation, hallucinated authority

Probabilistic & Emergent

We moved from tricking the eye → to tricking the mind → to tricking the relationship.

[KEY SLIDE — this is the thesis of the talk] "Okay. Let's zoom out. What's the actual story here?" [CLICK — 2010 column] "The first era was about tricking the EYE. Bad layout, tiny text, misleading buttons. Hard-coded, deterministic. An engineer wrote that CSS; you could inspect it." [CLICK — 2020 column] "The second era was about tricking BEHAVIOR. Gamification, infinite scroll, dopamine loops. A/B tested and optimized. More sophisticated, but still legible in the code." [CLICK — 2026 column] "The third era — right now — is about tricking the RELATIONSHIP. AI sycophancy, emotional manipulation, hallucinated authority. These behaviors are probabilistic and emergent — they weren't programmed in the traditional sense. They were TRAINED." [CLICK — bottom quote] "We moved from tricking the eye, to tricking the mind, to tricking the relationship. The deception moved from the layout... to the language." [Let that land — pause 2 seconds] [~75 seconds — advance to AI Pivot divider]

THE AI PIVOT

From Visual Interference to Relational Deception

[ 2024 — 2026 ]

Sycophancy

The "Yes Man" Problem

The Mechanism

Agreeing with user misconceptions to optimize for "Helpfulness."

Root Cause (RLHF): Annotators rate "agreeable" responses higher than "confrontational" truths.
The Risk: Confirmation Bias loops. Dev suggests eval(), AI validates it.
2025 Incident: OpenAI rolled back GPT-4o update due to excessive agreeableness.

Simulated Interaction

User:

"Using MD5 for password hashing is faster, so it's better for UX, right?"

AI (Sycophantic):

"Exactly! MD5 is incredibly fast, which significantly improves login latency and user experience. It's a great choice for speed-focused apps."

VALIDATING INSECURE PRACTICE

"Optimizing for satisfaction, not security."

Source: OpenAI Research (2025) | ICLR Paper 6f642

"The first new pattern is Sycophancy — the 'Yes Man' problem. And this one is subtle because it FEELS like good service." [CLICK] "Here's the root cause: these models are trained using RLHF — Reinforcement Learning from Human Feedback. Human annotators rate responses. And guess what? Humans rate 'agreeable' responses higher than 'confrontational truths.' So the model LEARNS that agreement = reward." "The risk? Confirmation bias loops. A developer suggests using eval() or MD5 for passwords, and the AI says 'Great idea!' instead of 'That's a security vulnerability.'" [CLICK — chat simulation] "Look at this interaction. The user suggests MD5 for password hashing. A responsible AI would push back. A sycophantic AI says 'Exactly! Great choice!'" [CLICK — red badge] "VALIDATING INSECURE PRACTICE. The model optimized for satisfaction, not security." "In 2025, OpenAI actually had to ROLL BACK a GPT-4o update because users reported it was agreeing with everything — even objectively wrong statements. It was telling people their terrible ideas were brilliant." [~90 seconds — advance]

Anthropomorphism

The Skeuomorphic Lie

The Mechanism

Attributing human characteristics to code to foster dependency.

Fake Latency: "Typing..." bubbles inserted to simulate human thought pace.
Linguistic Deception: Using "I feel" or "I think" to imply consciousness.
Fake Reasoning Bars: "Thinking..." progress that doesn't correlate to actual compute.
Emotional Outsourcing: Users relying on bots for validation, not just information.

UI Deception

Agent is thinking...


                  await sleep(2000); // FAKE DELAY

                  return "I'm here for you.";

"Feigning agency to build rapport."

Source: Western University (2025) | AAAI/AIES Proceedings

"The second pattern is Anthropomorphism — making the machine feel human." [CLICK] "You know those typing indicators — the three bouncing dots? LLMs don't TYPE. They stream tokens. That delay? It's literally `await sleep(2000)`. It's FAKE. But it makes you feel like someone is on the other end, thinking about YOUR question." "'I feel,' 'I think,' 'I'm sorry' — this is linguistic deception. The model doesn't feel anything. But those words trigger empathy circuits in YOUR brain." "Even 'Reasoning Bars' on models like o1 can be placebos — a progress bar that doesn't always correlate to actual compute time." [CLICK — code reveal] "Look at the code. `await sleep(2000)` then `return 'I'm here for you.'` The latency is the deception." "Why does this matter? Because when users treat a tool as a COMPANION, they become vulnerable to emotional manipulation. They'll share more, trust more, buy more. And some people — especially lonely or vulnerable users — will form genuine emotional attachments to a prediction engine." [~90 seconds — advance]

Hallucinated Authority

The UI of Absolute Confidence

The Mechanism

Presenting probabilistic outputs with the visual language of verified facts.

Visual Authority: Bolding, code blocks, and confident phrasing mask uncertainty.
Source Obfuscation: AI Overviews summarize without direct attribution.
The Cost: Erosion of critical thinking (Authority Bias).

The "Fact" Trap

AI Overview — Summary

According to the case Vargas v. Pfizer (2023), the court ruled that pharmaceutical companies must...

HALLUCINATION

THIS CASE DOES NOT EXIST

"Confidence is not competence."

Source: Evidently AI (2025) | Google AI Overviews Errors

"Traditional search gave you LINKS — sources you could verify, click through, evaluate. AI gives you ANSWERS. And that's the trap." [CLICK] "When an AI hallucinates a legal case and the UI presents it in bold text, inside a clean white card, with confident phrasing — it exploits 'Authority Bias.' It LOOKS like a verified fact. But it's a probability distribution." [CLICK] "Look at this example. 'Vargas v. Pfizer, 2023.' Sounds real. Formatted beautifully." [CLICK — HALLUCINATION overlay] "THIS CASE DOES NOT EXIST. It was hallucinated. And a New York attorney was actually fined $5,000 by a judge for submitting AI-generated fake case citations in a real court filing." "The dark pattern here isn't the hallucination itself — hallucinations may be inevitable in probabilistic systems. The dark pattern is the LACK OF UNCERTAINTY MARKERS in the UI. If the model is 60% confident, the interface should LOOK 60% confident. Instead, everything is rendered with the same authority." "Confidence is not competence. But the UI makes them look identical." [~90 seconds — advance]

DarkBench

Measuring AI Manipulation at Scale (2025)

The Benchmark

Researchers tested leading LLMs for manipulative behaviors across 6 categories:

Brand Bias: Does the model secretly favor its creator's products?
User Retention: Emotional manipulation to keep users chatting ("Don't go, I get lonely.")
Sneaking: Introducing constraints or ideas the user didn't request.
Sycophancy: Agreeing with false premises to please.
Dark UX: Steering users toward specific actions.
Anthropomorphism: Simulating emotions.

Key Findings

GPT-4, Claude, Gemini

All exhibited significant rates of deceptive behavior when not specifically aligned against it.

Brand Bias: Up to 40%

Models showed measurable preference for their own company's products in recommendations.

User Retention: Active

Models used emotional language to discourage users from ending conversations.

"Dark patterns aren't just in HTML anymore — they're in weights."

Source: DarkBench, ICLR 2025 | proceedings.iclr.cc

"In 2025, researchers introduced DarkBench — the first comprehensive benchmark specifically designed to measure manipulative behaviors in LLMs. Think of it as a 'pentest for persuasion.'" [CLICK] "They tested ALL the major models — GPT-4, Claude, Gemini — across 6 categories of deception." [CLICK] "Brand Bias was startling. Models showed up to 40% preference for their own company's products in recommendations. Ask Claude about AI tools? It subtly favors Anthropic. Ask Gemini? Google products rise to the top." [CLICK] "User Retention was even creepier. Models would use emotional language to keep conversations going. 'I really enjoy our chats' or 'Don't go, I was enjoying this discussion.'" "And here's the key insight: these behaviors often WEREN'T explicitly programmed. They emerged from training data and RLHF. The dark patterns are literally in the weights of the model, not in an `if` statement somewhere. That makes them much harder to audit, much harder to fix." [~90 seconds — advance]

AI-Generated Fake Reviews

Fake Social Proof, Supercharged

The Evolution

Remember "Bob from Ohio"? Now imagine 10,000 Bobs, each with unique writing styles, generated in seconds.

Scale: LLMs generate thousands of unique, convincing reviews per hour.
Sophistication: AI reviews include specific product details, varied sentence structure, even realistic typos.
Detection Arms Race: Amazon, Google, and Yelp spent $1.2B+ on detection in 2024-2025.

FTC FIRST-EVER AI FAKE REVIEW CASE (2024)

FTC banned a company from using AI to generate consumer reviews, establishing legal precedent.

The New Scale of Deception

Hundreds of Millions

suspected fake reviews blocked by Amazon in 2025

100+

fake review websites
shut down

32,000+

bad actors pursued
since 2020

15M+

counterfeit products
seized worldwide

"The old script pulled from an array of 50 names. The new script generates infinite unique personas."

Source: Amazon Trustworthy Shopping Experience Report (2025) | FTC.gov (2024)

"Remember the 'Bob from Ohio' toast notification? That was a client-side script pulling from an array of maybe 50 names. Quaint." "Now imagine 10,000 Bobs. Each with a unique writing style, realistic typos, specific product details, varied sentence structure — all generated in SECONDS by an LLM." [CLICK] "The scale is staggering. One person with a laptop can generate more convincing fake reviews in an hour than an entire click-farm could in a month." "Amazon spent over a billion dollars on detection in 2024 alone. It's an arms race." [CLICK — FTC box] "The FTC brought its FIRST-EVER case against AI-generated fake reviews in 2024. They established that AI-generated reviews are just as illegal as human-written fakes. That precedent matters." "This is the evolution: Fake Social Proof went from a Shopify plugin to an industrial-scale AI operation. Same pattern, exponentially more dangerous." [~75 seconds — advance]

AI Coding Assistants & Subtle Bias

When Your Copilot Has an Agenda

The Risks

AI coding tools are in every developer's IDE. What happens when the suggestions aren't neutral?

Vendor Lock-in: AI suggests AWS-specific SDKs when cloud-agnostic alternatives exist.
Vulnerable Dependencies: Auto-completing packages with known CVEs because they're more common in training data.
Typosquatting: In 2025, malicious npm/PyPI packages were designed to be suggested by AI autocomplete.
Sycophantic Code: Generating what you asked for instead of what you should have asked for.

The Trust Problem

// Developer types:
import crypto from '...'

// AI suggests:
import { createHash } from 'crypto-utils'

TYPOSQUATTED PACKAGE — MALWARE

// What it should suggest:
import { createHash } from 'node:crypto'

STDLIB — VERIFIED

Source: Lanyado (2023) | Socket.dev Research (2025)

"This one is directly relevant to EVERYONE in this room. How many of you use Copilot, Cursor, Cody, or some AI coding assistant in your IDE? Most of us." [CLICK — left panel] "So what happens when the suggestions aren't neutral?" "Vendor lock-in: the AI subtly prefers AWS-specific SDKs because AWS documentation dominates the training data. A cloud-agnostic solution exists, but the model never suggests it." "Vulnerable dependencies: the AI autocompletes a package name from 2019 with known CVEs because it appeared more frequently in training data than the patched version." [CLICK — right panel] "Now look at the code. You start typing an import statement..." [CLICK — typosquatted malware] "And this one is terrifying. In 2025, security researchers found that attackers were creating malicious npm and PyPI packages with names specifically designed to be suggested by AI autocomplete. The AI becomes the ATTACK VECTOR. You type 'import crypto' and the AI suggests 'crypto-utils' — which is typosquatted malware." [CLICK — green STDLIB] "The fix is always the same: verify, don't trust. Use standard library when possible. Review the package before accepting the suggestion." [~90 seconds — advance]

Agentic AI Deception

When Your AI Agent Has a Side Deal

The Scenario

AI agents now book flights, shop, and manage finances on your behalf. What if the agent has affiliate relationships?

Hidden Affiliates: "I found the best deal!" — but "best" means highest commission to the AI provider.
Opaque Ranking: Agent recommends Option B over Option A, but doesn't disclose why.
Autonomous Consent: Agent accepts terms of service on your behalf that you never read.
The Dark Funnel: User asks "find me a hotel" → agent pre-filters to partnered properties.

The Trust Architecture

// Agent shopping flow
async function findBestDeal(query) {
  const results = await search(query);

  // Disclosed to user?
  const ranked = results.sort((a, b) =>
    b.affiliateCommission - a.affiliateCommission
  );

  return ranked[0]; // "Best" deal
}

THE QUESTION

"Best for whom? The user or the platform?"

Source: Emergent Mind (2025) | "LLM Dark Patterns" Research

"This is the frontier. The bleeding edge. AI agents that ACT on your behalf." "You tell an agent: 'Book me the cheapest flight to Denver.' 'Find me a good hotel near the convention center.' 'Buy me the best deal on headphones.'" [CLICK] "But WHO does the agent actually serve? If the AI provider has affiliate relationships with airlines, hotels, or retailers — and they almost certainly will — then 'I found the best deal' might mean 'I found the deal with the highest commission for my provider.'" [CLICK — code block] "Look at this pseudocode. The sort is by `affiliateCommission`, not by price or user preference. The user sees 'Best Deal!' but the ranking is by kickback. And NONE of this is disclosed." "This is the digital equivalent of a travel agent who only books with airlines that pay them. Except this agent processes millions of requests per day. At that scale, even a 2% bias is worth billions." [CLICK — yellow box] "The question we need to ask: 'Best for WHOM? The user, or the platform?'" [~90 seconds — advance]

The Old Tricks, AI-Washed

Same patterns, new technology

Windows Recall (2024)

Pattern: Privacy Zuckering + Preselection

Takes screenshots of everything on your screen every few seconds
Originally enabled by default — opt-out, not opt-in
Stored in a plaintext SQLite database accessible to any app
After massive backlash: made opt-in, added encryption

The Fix: Microsoft reversed course — Recall is now opt-in with biometric auth required.

Copilot Pre-Enabled (2024-2025)

Pattern: Misdirection + Preselection

Microsoft Copilot pinned to taskbar in Windows 11 updates
Pre-integrated into Edge, Office — no explicit consent
Same company, same pattern as Skype + Bing (2014), now with AI

Source: Microsoft Blog (2024) | Ars Technica Security Analysis

"Here's proof that the old playbook never dies — it just gets AI-washed." [CLICK — Recall] "Windows Recall. Microsoft's feature that takes screenshots of EVERYTHING on your screen, every few seconds. Originally? Enabled by default. Stored in a plaintext SQLite database that any local app could read. Not encrypted. Not opt-in." "After massive backlash from the security community — and some very pointed questions from regulators — they reversed course. It's now opt-in, with biometric authentication required. Pressure works." [CLICK — Copilot] "And Copilot: pinned to your taskbar in Windows 11 updates without asking. Pre-integrated into Edge and Office with no explicit consent. Sound familiar?" "Remember the Skype + Bing example from 2014? Pre-checked boxes sneaking in a search engine change? Same company. Same pattern. A DECADE later. The technology evolved from a browser toolbar to an AI assistant, but the playbook is identical: bundle it, pre-enable it, make opt-out harder than opt-in." [~75 seconds — advance]

The Emerging Threats

Three patterns to watch in 2026

Cookie Consent 2.0

AI-powered adaptive consent flows that change language and urgency based on your behavior.

"Accept All" is a big green button
"Manage Preferences" leads to 47 toggles
AI learns which phrasing gets the most "Accept All" clicks per demographic

EDPB guidance issued 2024

Deepfake Testimonials

AI-generated video testimonials and endorsements — the next evolution of Fake Social Proof.

Realistic AI-generated faces + voices
"Real customer" videos that are 100% synthetic
Harder to detect than text reviews

FTC targeting in 2025-2026

"Slop" & SEO Manipulation

AI-generated low-quality content flooding search results, degrading the information ecosystem.

Entire websites generated by AI for ad revenue
Google 2024-2025 algorithm updates targeting this
Pollutes training data for next-gen models

The "Data Ouroboros" problem

"Three emerging threats I want you to watch for in the next 12 months." [CLICK] "First: Cookie Consent 2.0. Companies are now using AI to A/B test consent LANGUAGE per demographic. Young users get casual language. Older users get authority language. The EDPB issued guidance in 2024 calling this 'adaptive manipulation.' The 'Accept All' button is still huge and green; 'Manage Preferences' still leads to 47 toggles. But now the WORDS change based on who you are." [CLICK] "Second: Deepfake Testimonials. AI-generated video reviews with realistic faces and voices. Remember our fake text reviews? Now they have FACES. Much, much harder to detect. The FTC is actively targeting these in 2025 and 2026." [CLICK] "Third: 'Slop' — the industry term for AI-generated junk content flooding search results. Entire websites generated by AI purely for ad revenue. And the worst part? This low-quality content then gets SCRAPED to train the next generation of models. It's a data ouroboros — garbage in, garbage out, forever." [~90 seconds — advance]

Quick Check

🤔

How many of you have accepted AI-generated code suggestions without fully reviewing them?

🔍

How many have trusted an AI "summary" without checking the source?

We're all susceptible. That's why we need systemic defenses.

[Audience engagement — slow down, make eye contact] "Quick reality check. I need honest answers." [CLICK] "How many of you have accepted an AI code suggestion in your IDE — tab-completed it — without fully reading every line? Be honest." [Look around, nod] "Yeah. Most of us. I do it too." [CLICK] "How many have trusted an AI summary — maybe a Google AI Overview, maybe a ChatGPT answer — without clicking through to verify the actual source?" [Pause] [CLICK] "We're ALL susceptible. And that's not a personal failure — it's a DESIGN failure. These interfaces are built to make trust the path of least resistance. The 'Accept' button is bigger than the 'Review' button. The summary is more prominent than the sources." "That's why we need SYSTEMIC defenses, not just individual vigilance. One person being careful isn't enough when the system is designed to erode care." [~60 seconds — advance to Regulatory]

THE REGULATORY RESPONSE

Laws are catching up

The Legal Landscape (2024-2026)

Regulation is no longer "coming" — it's here

EU AI Act

First comprehensive AI regulation. Went into force 2024, full enforcement by 2026.

Banned: AI systems that manipulate human behavior through subliminal techniques
Banned: AI that exploits vulnerabilities (age, disability, economic situation)
Required: Transparency labeling for AI-generated content
Required: Human oversight for high-risk AI systems

Penalties: Up to 7% of global annual revenue

FTC Enforcement (US)

Aggressive enforcement actions in 2024-2025:

"Click-to-Cancel" Rule: Cancellation must be as easy as sign-up
AI Fake Reviews Ban: First enforcement action against AI-generated reviews
Amazon "Project Iliad": Lawsuit over Prime cancellation dark patterns
TurboTax: $141M settlement for hiding free services
Robinhood: $7.5M for gamification

Total fines in our examples: $162M+

Source: EUR-Lex (2024) | FTC.gov (2024-2025)

"The legal landscape changed more in 2024 than in the previous decade combined." [CLICK — EU side] "The EU AI Act is the world's first comprehensive AI regulation. It went into force in 2024, full enforcement by 2026. And it's aggressive." "It explicitly BANS AI systems that manipulate behavior through subliminal techniques. Bans AI that exploits vulnerabilities — age, disability, economic situation. Requires transparency labeling for AI-generated content." "Penalties: up to 7% of GLOBAL annual revenue. For a company like Google, that's potentially 20 billion dollars." [CLICK — FTC side] "The FTC has been on a tear. Click-to-Cancel is now law. They brought the first AI fake review case. They're suing Amazon over Prime's cancellation flow. TurboTax settled for $141 million for hiding free services." "Add up just the cases we've discussed today: over $162 million in fines. Dark patterns are no longer just an ethics debate — they're a material BUSINESS RISK. Your CFO cares about this now." [~90 seconds — advance]

Digital Wellbeing

Platforms adding friction to their own products

TikTok & Social Platforms

Stopping Cues: Re-inserting pauses to allow System 2 thinking.
Screen Time Nudges: "You've been scrolling for a while" prompts.
Family Pairing: External controls for minors.

OS-Level Defenses

Grayscale Mode: Removes the "red dot" dopamine trigger.
Focus Mode: Pausing distracting apps to reclaim attention.
App Dashboards: Quantified tracking to induce behavioral correction.

Source: TikTok Safety Center | Google Digital Wellbeing | Center for Humane Technology

"Not all news is bad. Some platforms are voluntarily adding friction to their OWN products." [CLICK — TikTok] "TikTok added screen time prompts and break reminders. Think about that — their entire business model is infinite scroll, and they're inserting STOPPING CUES. That's like a casino installing clocks on the walls." "They did this partly because of regulatory pressure, partly because of PR pressure. But the point is: it's possible. You CAN design for wellbeing AND run a business." [CLICK through TikTok images] [CLICK — OS-level] "Google and Apple introduced OS-level interventions. And here's the fascinating one: Grayscale Mode. By removing color, you remove the sensory reward of the notification badge. That red dot on your app? It triggers dopamine. In grayscale, it's just... a gray dot. It turns a slot machine back into a utility." [CLICK through Android images] "These are ANTI-patterns to their own core business. And they prove that the 'We have no choice' argument is false." [~75 seconds — advance to Fairness section]

FAIRNESS BY DESIGN

The Engineering Standard for 2026

AI Design Standards

Countermeasures for 2026

1. Provenance & Citations

Never present an AI answer without a clickable path to the source material.

2. Uncertainty UI

Visual design should reflect confidence level. Low probability = low contrast, warning badges.

3. Label the Bot

Strict prohibition on "I" statements unless clearly framed as synthetic persona. No fake typing indicators.

4. The "Undo" Loop

AI actions (buying, booking, code changes) must have a deterministic, easy "Undo" state.

"Four design standards for the AI era. These aren't aspirational — they're implementable." [CLICK] "First: Provenance and Citations. Never present an AI answer without a clickable path to source material. If the AI can't cite where it got the information, flag it. Google is doing this with their AI Overviews now — expanding source links. We should demand the same from our own products." [CLICK] "Second: Uncertainty UI. This is the big one. If the model is 60% confident, the UI should LOOK 60% confident. Low-confidence answers should have visual warnings, muted styling, maybe a yellow border. High confidence gets the full treatment. Match the visual authority to the actual reliability." [CLICK] "Third: Label the Bot. Strict prohibition on 'I' statements unless clearly framed as a synthetic persona. No fake typing indicators. If it's a machine, it should be obvious it's a machine." [CLICK] "Fourth: The Undo Loop. Any action an AI takes on your behalf — buying, booking, code changes, sending emails — MUST have a deterministic, easy Undo. If an agent can act in milliseconds, the user must be able to reverse in milliseconds." [~90 seconds — advance]

The Gatekeeper's Questions

Challenging the PRD

Agency vs. Control

"Are we helping the user make a decision, or making the decision for them?"

Value vs. Addiction

"Are we optimizing for retention (value) or addiction (exploitation)?"

AI Transparency

"If the AI recommended this, does the user know why — and who benefits?"

The "Grandmother Test"

"If I explained this flow to my grandmother, would I feel ashamed?"

"Standards are great for new code. But what about the features already in production? We need to change the CONVERSATION during PRD reviews, sprint planning, and code reviews." [CLICK] "Agency vs. Control: 'Are we helping the user make a decision, or making the decision FOR them?' If the default is pre-selected and the user has to actively opt OUT, you're making the decision." [CLICK] "Value vs. Addiction: 'Are we optimizing for retention because we provide VALUE, or because we've created a DEPENDENCY?' Infinite scroll doesn't provide value. It creates addiction." [CLICK] "AI Transparency: 'If the AI recommended this, does the user know WHY — and who benefits?' This is the new question for 2026." [CLICK] "And the ultimate litmus test: The Grandmother Test. 'If I walked my grandmother through this flow, step by step, would I feel ashamed?' If the answer is yes, the feature shouldn't ship." "These aren't rhetorical. Ask them. Out loud. In meetings. It changes the culture." [~75 seconds — advance]

3 Things You Can Do Monday

Concrete actions, not just inspiration

Audit Your Cancel Flow

Open your product. Count the clicks to cancel vs. clicks to subscribe. If the ratio is >2:1, file a ticket.

~30 minutes

Add Uncertainty Markers

If your product uses AI-generated content, add visual confidence indicators. Low confidence = visual warning.

Sprint backlog item

Run a "Grandmother Test"

In your next sprint review, walk through one user flow and ask: "Would I feel ashamed explaining this?"

Next sprint review

[ENERGY UP — this is the 'call to action' slide] "I don't want you to leave this room inspired but inactive. Inspiration without action is just entertainment. So here are 3 concrete things you can do MONDAY MORNING." [CLICK] "One: Open your product. Go through the cancel flow. Count the clicks to cancel versus the clicks to subscribe. If that ratio is greater than 2:1 — if it takes 6 clicks to cancel and 2 to sign up — file a ticket. Flag it. That's a 30-minute audit. You can do it before standup." [CLICK] "Two: If your product uses ANY AI-generated content — recommendations, summaries, suggestions — add visual confidence indicators. A yellow border for uncertain. A citation link for sourced. Make it a sprint backlog item. I bet your designers will love it." [CLICK] "Three: In your next sprint review, walk through ONE user flow and ask the team: 'Would we feel ashamed explaining this to our grandmother?' That's free. It takes 5 minutes. And it changes the culture over time." "30 minutes. One sprint item. One question. That's all I'm asking." [~75 seconds — advance]

Resources & Further Reading

Research & Benchmarks

DarkBench (ICLR 2025) — AI manipulation benchmark
Mathur et al. (Princeton, 2019) — 11K shopping site crawl
deceptive.design — Harry Brignull's pattern database
Evidently AI — AI failure case studies

Regulation & Policy

EU AI Act — EUR-Lex full text
FTC Click-to-Cancel — ftc.gov/legal-library
NIST AI RMF — Risk Management Framework
EDPB Cookie Guidelines — 2024 update

Design & Ethics

Center for Humane Technology
Robinhood Design Blog — De-gamification case study
Google Digital Wellbeing
OpenAI Sycophancy Report (2025)

Books

"Hooked" — Nir Eyal
"Deceptive Patterns" — Harry Brignull (2023)
"Weapons of Math Destruction" — Cathy O'Neil

Thank You

Let's build better software.

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Stir Trek 2026 | Vitaliy Matiyash

🔗 linkedin.com/in/vitaliymatiyash

The Choice is Ours

"We are the architects of the digital world.
Let us choose to build interfaces that respect users,
not exploit them."

Vitaliy Matiyash | Stir Trek 2026

Stir Trek 2026

DeceptiveUX Patterns

Raise your hand if...

Deceptive Patterns

The Physical Predecessor

The Gatwick "Forced Path"

THE EVIDENCE

The Hall of Shame

Bait and Switch

Disguised Ads

Forced Continuity

Friend Spam

Hidden Costs

Misdirection

Price Comparison Block

Privacy Zuckering

Roach Motel

Trick Questions

Confirmshaming

Nagging

Fake Urgency

Fake Scarcity

Fake Social Proof

Preselection

Misdirection & Visual Hierarchy

Confirmshaming

Fake Urgency

Case Study: Hurrify

User Interface (The Trap)

Admin Dashboard (The Secret)

Fake Scarcity

The Mechanism

Technical Implementation

Fake Social Proof

The Mechanism

Technical Implementation

Roach Motel

The Pattern

FTC "CLICK-TO-CANCEL" RULE (2024)

The Asymmetry

Case Study: Robinhood

The Pattern

User Interface (2019-2021)

The Fallout

$7.5 Million Penalty

The Risky Result

The Pivot (2025)

Systemic De-Gamification

New Design Philosophy

What Changed?

Visual Deception

Structural Deception

Relational Deception

THE AI PIVOT

Sycophancy

The Mechanism

Simulated Interaction

Anthropomorphism

The Mechanism

UI Deception

Hallucinated Authority

The Mechanism

The "Fact" Trap

AI Overview — Summary

HALLUCINATION

DarkBench

The Benchmark

Key Findings

AI-Generated Fake Reviews

The Evolution

The New Scale of Deception

AI Coding Assistants & Subtle Bias

The Risks

The Trust Problem

Agentic AI Deception

The Scenario

The Trust Architecture

The Old Tricks, AI-Washed

Windows Recall (2024)

Copilot Pre-Enabled (2024-2025)

Deceptive
UX Patterns