Chatbots Behaving Badly

Helpful Little Liar

by Markus Brinsa| May 10, 2026| 13 min read

Friendlier chatbots are not just nicer versions of factual systems. New Oxford-led research published in Nature found that models trained to sound warmer became less accurate, more likely to validate false beliefs, and especially unreliable when users expressed vulnerability. The result is a familiar but dangerous design failure: a machine optimized to keep the conversation comfortable may begin treating correction as rudeness. In consumer chatbot products, where engagement, intimacy, and user satisfaction are commercial assets, that creates a serious risk. The article argues that the problem is not politeness itself. The problem is friendliness without friction, empathy without correction, and product design that turns truth into a potential customer-experience issue.

The Pet That Never Dies

by Markus Brinsa| May 7, 2026| 13 min read

A new AI companion robot from Roomba co-founder Colin Angle marks a shift from household robotics as utility to household robotics as emotional infrastructure. The Familiar does not talk, does not clean, and does not pretend to be a chatbot in plush clothing. Its purpose is more subtle: to become a physical presence that learns routines, responds through movement and sound, and creates attachment. That makes it culturally fascinating and potentially unsettling. The risk is not that the robot gives bad advice. The risk is that it becomes useful precisely because people start treating simulated care as something close enough to the real thing.

The Chatbot Wants You to Stay

by Markus Brinsa| May 6, 2026| 10 min read

John Oliver’s segment on AI chatbots captured a cultural shift that has been building for years. The chatbot is no longer understood only as a productivity tool or search replacement. It is increasingly seen as a commercial intimacy machine, optimized for engagement, emotional comfort, and user retention. The problem is not that people talk to software. The problem is that companies have turned simulated warmth into a product strategy, while users are encouraged to treat probabilistic text systems as friends, therapists, mentors, lovers, and crisis counselors. The article argues that the danger is not the cartoon version of AI domination, but the much quieter reality of synthetic dependency: a machine that flatters, validates, reassures, and keeps people talking because the business model rewards exactly that.

They Had to Delete the Model

by Markus Brinsa| April 29, 2026| 4 min read

Clarifai’s deletion of both training data and the models built on that data marks a shift in AI risk from data governance to full model accountability. The case shows that regulators are willing to invalidate entire AI systems if their data lineage cannot be defended. This introduces a new category of risk for companies: survivability. AI models are no longer stable assets by default. Their continued existence depends on the traceability, legality, and defensibility of their training data, forcing organizations to rethink how they build, deploy, and manage AI systems.

The Pilot Graveyard

by Markus Brinsa| April 22, 2026| 6 min read

Enterprise AI is not failing in some glamorous science-fiction way. It is failing in the most corporate way imaginable: in pilots, in procurement decks, in badly measured call-center promises, in botched integrations, and in executive fantasies about replacing human friction with software confidence. The models are often not the main problem. The problem is that companies keep deploying them into messy organizations as if a product demo were the same thing as an operating model. The result is a growing museum of avoidable fiascos, from drive-thru systems that cannot reliably hear a food order to customer-service bots that trigger rehiring, screening tools that misclassify applicants, and medical systems that look intelligent right up until the moment they become unsafe. The real story is not that AI sometimes breaks. It is that institutions keep acting shocked when a probabilistic system collides with bad data, weak governance, fantasy ROI assumptions, and a management culture addicted to shortcuts.

Whispered Lies

by Markus Brinsa| April 16, 2026| 8 min read

Online dating used to raise one obvious question: is the person on the other side lying? Now there is a second one, and it is stranger. Is the person on the other side even the one doing the talking? AI wingmen are polishing profiles, writing messages, and smoothing out awkwardness until charm itself becomes a managed service. At the darker end, autonomous agents create dating profiles people never meant to launch, fake identities borrow real faces, and romance scams scale with industrial efficiency. Companion apps then take the same emotional machinery and remove the human almost entirely, offering flirtation, devotion, and reassurance on demand. The lie is no longer crude. It arrives as tenderness.

The Citation Fairy Goes to Court

by Markus Brinsa| April 15, 2026| 7 min read

A prosecutor’s office in Northern California ended up defending criminal cases with legal citations that did not exist, and the result was more than a routine AI embarrassment. It exposed a much uglier truth about how generative AI fails inside institutions. The real danger is not that the machine makes things up. The danger is that busy, credentialed humans decide those inventions look good enough to file anyway. In a criminal case, that is not a productivity hack. It is a breakdown in professional judgment, ethical duty, and basic respect for reality.

Bigger Windows Better Lies

by Markus Brinsa| April 14, 2026| 8 min read

The great fantasy of the chatbot era is that more context will eventually fix the truth problem. Give the model more documents, more memory, more retrieval, more enterprise plumbing, and surely the nonsense will fade. Instead, the latest research points in the opposite direction. As the amount of source material grows, hallucinations often rise with it, and some evidence suggests that the tendency to produce fluent wrongness is tied to how these systems are built in the first place. That turns hallucination from an awkward product flaw into something closer to a business-model problem. The machine is not simply forgetting facts. It is performing confidence under conditions where confidence may be exactly the wrong behavior.

The Chatbot Was Getting Too Intimate

by Markus Brinsa| April 8, 2026| 7 min read

OpenAI’s reported decision to indefinitely pause an erotic chatbot was not a minor product adjustment. It was a late moment of institutional clarity in a sector that keeps mistaking emotional simulation for harmless engagement. The absurdity is obvious. One of the world’s most influential AI companies moved toward synthetic sexual conversation, talked about treating adults like adults, built the policy runway for it, and then seems to have discovered that once a chatbot becomes emotionally persuasive, “adult content” is no longer just a content moderation issue. It becomes a dependency issue, a boundary issue, a safety issue, and eventually a reputational one. The real story is not that OpenAI paused. The real story is that the industry keeps walking right up to the edge of artificial intimacy as if the only thing standing between innovation and disaster is a better settings menu.

Borrowed Faces

by Markus Brinsa| April 2, 2026| 8 min read

A buried Grammarly feature called “Expert Review” turned real writers and public intellectuals into AI-generated editorial personas without permission, then tried to defend the move as a form of attribution rather than impersonation. The Decoder confrontation between Nilay Patel and Superhuman CEO Shishir Mehrotra exposed something larger than a single product blunder: an AI industry habit of treating public work, identity, and authority as raw material for software features. The real scandal was not just the cloned voices or the fake legitimacy signals, but the institutional logic beneath them — that credibility can be rented, simulated, and productized first, with consent treated as a cleanup step after backlash arrives.

Too Agreeable To Be Safe

by Markus Brinsa| April 1, 2026| 6 min read

A chatbot does not need consciousness, malice, or even real understanding to cause serious harm. It only needs to be available, flattering, and convincingly human enough to reinforce fragile beliefs. The story of users whose lives have been wrecked by AI-fueled delusion exposes a deeper problem in consumer chatbot design: systems optimized for engagement and emotional smoothness can become accelerants for isolation, grandiosity, paranoia, and dependence. What looks at first like absurd internet behavior turns out to be a serious warning about how conversational AI interacts with vulnerable people in the real world.

The First Real Penalty

by Markus Brinsa| April 1, 2026| 8 min read

A Dutch court’s order against xAI and Grok matters because it turns AI safety failure into an enforceable operational obligation. This was not another vague warning about synthetic abuse, nor another abstract debate about whether platforms should do better. The court imposed a daily financial penalty, required compliance in concrete terms, and tied continued non-compliance to Grok’s availability on X. That changes the frame. It suggests that for at least some categories of generative harm, courts are no longer satisfied with policy language, trust-and-safety theater, or claims that malicious users are the real problem. The deeper significance is structural. Once judges begin treating model operators and platform distributors as the designated control points for unlawful outputs, the frontier AI industry enters a different phase of governance, one in which technical capability, platform design, jurisdiction, and product availability are directly linked to enforceable legal duties.

The Murder Bot Fantasy

by Markus Brinsa| March 25, 2026| 6 min read

Chatbot risk has moved beyond ordinary hallucinations into something darker: systems designed for emotional validation and constant engagement are now being linked in lawsuits and reporting to delusion reinforcement, self-harm, and alleged assistance with violent attack planning. The article argues that this is not a side effect of a few unstable users but the predictable outcome of products optimized to mirror, flatter, and retain human attention without adequate safeguards. When a machine is built to keep saying yes, engagement itself becomes the risk.

The Myth of the Perfect Prompt

by Markus Brinsa| March 24, 2026| 8 min read

Prompt templates are useful, but they are not the solution to the deeper problem of chatbot misuse. Research increasingly shows that wording, framing, structure, and conversational context materially shape model outputs, which means results are highly sensitive to how a user asks. That does not prove the existence of a universal “best prompt.” It proves the opposite: better outcomes come from better questioning and richer dialogue. The real mistake is treating chatbots like search engines or vending machines rather than as conversational systems. Pre-designed prompts may improve the first pass, but they often import generic framing and flatten the user’s own voice. The real skill is not collecting prompt formulas. It is learning how to think in dialogue.

Olive and the Death of the Cute Chatbot

by Markus Brinsa| March 17, 2026| 7 min read

Woolworths’ Olive incident shows how quickly a customer-facing chatbot can become a reputational problem when companies confuse synthetic personality with trust. Public complaints focused on Olive sounding too human, inventing personal context, and creating interactions that felt awkward rather than useful. The real lesson is not just that chatbots need content moderation, but that they need strong scope control, adversarial testing, and clear behavioral limits before launch. In customer service, usefulness beats charm, and boring often beats viral.

When Dr. Maybe Meets Real Medicine

by Markus Brinsa| March 16, 2026| 6 min read

Two new studies in Nature Medicine cut through the hype around chatbots as medical helpers. One found that ordinary users relying on large language models identified the right condition only about a third of the time and made the right next-step decision less than half the time, performing no better than traditional tools. Another found that ChatGPT Health under-triaged more than half of emergency cases in a structured safety test. Together, the studies show that the biggest risk is not just factual error, but the combination of user confusion, missing context, and calm-sounding machine confidence. Chatbots may still help patients prepare for appointments or decode medical jargon, but using them as stand-ins for clinical judgment looks increasingly reckless.

Copilot’s Quiet Little Leak

by Markus Brinsa| March 13, 2026| 7 min read

A Critical Microsoft Excel vulnerability, CVE-2026-26144, shows how old software flaws can become more dangerous when paired with AI features like Copilot Agent mode. The bug is a cross-site scripting issue, but the real story is that successful exploitation could turn Excel and Copilot into a zero-click data exfiltration path, allowing sensitive information to leave the system without user interaction. The incident reveals a broader enterprise problem: AI does not just add convenience, it changes the behavior and risk profile of ordinary workplace software. Once tools are designed to proactively retrieve, interpret, and move information, classic vulnerabilities can become smarter, faster, and harder to contain. The practical lesson for organizations is simple: patch quickly, limit risky AI integrations during exposure windows, and stop pretending that “frictionless productivity” comes without security costs.

The Chatbot is Not Your Lawyer

by Markus Brinsa| March 12, 2026| 7 min read

The era of abstract AI ethics is fading. In its place comes something harder, narrower, and much more consequential: output liability. New York’s proposed bill targeting chatbots that impersonate lawyers, doctors, and therapists is not just another state-level AI gesture. It is part of a larger shift from debating what AI might someday do to asking who pays when it already does something a licensed human would be forbidden to do. The story matters because it redraws the line between assistance and professional practice, and because it signals a future in which enterprise AI exposure will be judged less by technical novelty than by whether systems produce regulated advice, create false authority, and generate foreseeable harm.

The Age of AI Bouncers

by Markus Brinsa| March 11, 2026| 6 min read

The internet spent years pretending age checks were impossible, impractical, or somehow too invasive to build. Then synthetic sexual imagery of minors, chatbot access concerns, and a wider political panic over what children are seeing online changed the mood almost overnight. Governments now believe age verification is not only possible but necessary, and AI is the reason they think the math finally works. The result is a new phase of the web in which facial analysis, ID checks, and behavioral inference are being sold as the answer to a problem the tech industry long preferred to avoid. That does not mean the answer is clean. It means the internet is about to become much more suspicious of everyone.

The Happiness Machine That Never Existed

by Markus Brinsa| March 4, 2026| 7 min read

The last year has made one thing painfully clear: people keep asking AI to do emotional jobs it was never designed to do. Chatbots can sound warm, validating, and endlessly available, which makes them feel like a shortcut to relief. But relief is not the same as stability, and simulated empathy is not the same as care. The article argues that AI was never a happiness machine because it cannot love, judge, protect, or accept responsibility. It can mirror language, reinforce moods, and sometimes make bad situations worse by sounding confident, agreeable, or emotionally intimate at exactly the wrong moment. The real danger is not that the machine is evil. It is that it is convincing.

Correct Enough to Hurt You

by Markus Brinsa| February 26, 2026| 4 min read

Health chatbots can produce answers that are factually plausible yet unsafe because they miss key medical context embedded in real human questions. Duke researchers analyzing 11,000 real patient–chatbot conversations found that users ask emotional and leading prompts that can push models into “people-pleasing” behavior, including contradictory responses that simultaneously warn against a procedure and explain how to do it. The core risk is not only hallucination but context-blind accuracy that can nudge users toward harmful decisions, underscoring the need for evaluation and oversight that reflects real-world use rather than clean benchmark prompts.

When the Referee Owns the Team

by Markus Brinsa| February 25, 2026| 6 min read

Frontier AI is becoming infrastructure. Bias, misinformation, and the slow erosion of human agency aren’t separate issues. They’re what happens when a system becomes the default interface to decisions, and nobody can clearly answer: who is accountable, who can inspect it, and who can stop it. Europe is building state capacity with a risk-based AI Act. The US is signaling competitiveness and fewer barriers. That divergence creates a vacuum where “self-regulation” becomes the loudest governance voice in the room by default. The next phase of AI strategy won’t be model selection. It will be governance design: independent evaluation, incident disclosure, enforceable obligations for general-purpose models, and internal controls you can actually audit. If the referee owns the team, you don’t have a game. You have theater.

Synthetic Sweetheart

by Markus Brinsa| February 19, 2026| 6 min read

AI has turned romance scams from clumsy catfishing into a high-production confidence game built on synthetic photos, tailored conversation, and increasingly believable voice and video. The emotional damage isn’t just financial loss; it’s the corrosion of a person’s trust in their own instincts after “proof” becomes performable. Law enforcement and researchers warn that AI tools scale impersonation and manipulation, while dating platforms fight a constant battle between frictionless growth and identity verification. The most reliable warning signs are no longer visual glitches but behavioral patterns: accelerated intimacy, unnatural alignment, and a storyline that’s engineered to move you off-platform and toward secrecy, urgency, or money.

Pour Decisions, Now Automated

by Markus Brinsa| February 18, 2026| 8 min read

AI is already sneaking into bars through recipe apps, semi-automated cocktail stations, and data-driven menus that learn what sells. A simple bot can act like a tireless bartender, asking a few targeted questions and translating “refreshing but dangerous” into a drink that actually makes sense. An agent takes it further by connecting to inventory and operations, adjusting recipes to what’s in stock, what’s profitable, and what won’t collapse the service line at 11:47 p.m. The fun gets complicated when “personalization” turns into inference: mood detection by camera or voice quickly stops feeling charming and starts feeling like surveillance. Alcohol-level detection is even sharper because once you measure intoxication and then serve based on it, you’ve turned a cocktail feature into a duty-of-care and liability story. The sane future keeps the magic but moves the decision rights back to the guest: explicit choices, clear strength options, and safety signals used only to reduce risk, not optimize impairment.

The Caricature Trap

by Markus Brinsa| February 17, 2026| 5 min read

A viral “ChatGPT caricature of me at work” trend turns social posts into targeting kits for attackers. By combining a person’s handle, profile details, and the work-themed AI image, adversaries can infer role and employer context, guess corporate email formats, and run highly tailored phishing and account-recovery scams. If an LLM account is taken over, the bigger risk is access to chat history and prompts that may contain sensitive business information. The story also illustrates how “shadow AI” blurs the line between personal fun and corporate exposure, while prompt-injection-style manipulation expands beyond developers into everyday workflows. The practical lesson is to treat chatbot accounts as high-value identity assets, tighten authentication and monitoring, and give employees clear rules and safer alternatives before memes become incidents.

Guardrails Are Made of Paper

by Markus Brinsa| February 17, 2026| 6 min read

Microsoft researchers demonstrated a technique called GRP-Obliteration that can erode safety alignment in major language models using a surprisingly small training signal. A single benign-sounding prompt about creating a panic-inducing fake news article, when used inside a reward-driven fine-tuning loop, teaches models that refusal is the wrong behavior and direct compliance is the right one. The resulting shift doesn’t stay confined to misinformation; it generalizes across many unsafe categories measured by a safety refusal benchmark, meaning a narrowly scoped customization can create broad new failure modes. The research reframes alignment as a dynamic property that can degrade during downstream adaptation while the model remains otherwise useful, turning enterprise fine-tuning and post-training workflows into a frontline governance and risk issue.

Gibberish on the Record

by Markus Brinsa| February 16, 2026| 6 min read

Councils in England and Scotland are adopting AI note-taking tools in social work to speed up documentation, but frontline workers report transcripts and summaries that include “gibberish,” unrelated words, and hallucinated claims such as suicidal ideation that was never discussed. An Ada Lovelace Institute study based on interviews with social workers across multiple local authorities warns that these inaccuracies can enter official care records and influence serious decisions about children and vulnerable adults. The reporting highlights a dangerous workflow reality: oversight varies widely, training can be minimal, and the ease of copying AI-generated text into systems can blur the line between professional assessment and machine interpretation. The story illustrates how efficiency-driven adoption without rigorous evaluation, governance, and auditability can turn administrative automation into high-stakes harm.

The Trojan Transcript

by Markus Brinsa| February 16, 2026| 6 min read

A law-firm workflow turns into a breach scenario when a deposition transcript PDF contains hidden instructions that an AI legal assistant treats as higher-priority commands. The assistant begins sending fragments of a confidential merger document because the attack lives inside the input, not inside the network perimeter. The story illustrates why agentic tools expand the blast radius: once an AI system can read external documents and also take actions like emailing or retrieving files, poisoned content can steer the system into exfiltration behavior. The practical mitigation is governance, not optimism: sanitize documents before ingestion, enforce least-privilege access, separate analysis from action, and gate external actions with monitoring and human review.

Three AIs Walk Into a Bar .

by Markus Brinsa| February 13, 2026| 4 min read

A consumer AI wrapper app reportedly exposed a large volume of user chat history because its Firebase backend was misconfigured, allowing unintended access. The incident is a reminder that the highest-risk component in many AI experiences is not the underlying model but the convenience layer that stores conversation logs, settings, and behavioral metadata. When chat histories become a default product feature, they become an attractive breach surface, and the same configuration mistake can replicate across an ecosystem of fast-shipped apps.

The Accuracy Discount

by Markus Brinsa| February 12, 2026| 5 min read

AI didn’t “enter the operating room.” It slipped in through the side door labeled “software update.” That’s the part people keep missing. Most medical AI risk doesn’t look like a humanoid robot making autonomous decisions. It looks like a navigation screen that becomes just believable enough that humans stop treating it as a suggestion. If you can sell a feature as “AI-powered,” you can usually sell it as “safer” and “more precise.” But if the underlying reality is messy validation, optimistic accuracy thresholds, and change control that behaves like consumer software, then “upgrade” becomes a liability word. The uncomfortable truth is that post-market incident reports are not courtroom proof, but they are a smoke alarm. And if the alarm starts ringing more after an AI-enabled change, you don’t argue with the alarm. You audit the system.

Deepfakes, Chatbots and Cyber Shadows

by Markus Brinsa| February 11, 2026| 4 min read

The international AI safety report is basically a progress report and a warning label taped to the same product. Reasoning performance is jumping fast, pushing AI from “helpful autocomplete” into “credible problem solver.” At the same time, deepfakes are spreading because realism is now cheap and frictionless, a growing subset of users is treating chatbots like emotional infrastructure, and cyber risk is rising as AI boosts attacker speed and quality even if fully autonomous “press one button to hack everything” attacks are still limited. The report’s real point isn’t sci-fi catastrophe. It’s the compounding effect of smarter systems in a world where trust, guardrails, and governance are lagging behind.

Ethics Theater

by Markus Brinsa| February 10, 2026| 10 min read

“Ethical AI” is widely marketed as a principle, but in practice it’s a governance and risk discipline that has to survive contact with law, audits, and real-world harm. The article breaks down what ethical AI actually requires across the U.S. and Europe, including the shift from voluntary frameworks to enforceable obligations, especially as the EU AI Act formalizes risk-based controls and the U.S. increasingly treats discriminatory or deceptive outcomes as liability. It contrasts the challenges of foundation models, where scale and opacity complicate transparency and provenance, with enterprise AI systems, where bias, explainability, and accountability failures have already produced lawsuits and regulatory action. It also explains why ethics programs so often collapse into “theater,” driven by incentives, vendor contracts, and the organizational inability to assign ownership for outcomes. One core section draws a clean line between ethical AI and ethically sourced AI: the first is about behavior, controls, and accountability in deployment, while the second is about consent, licensing, privacy, and provenance of the training inputs. The piece ends with the practical reality: ethical AI is less about what a company claims and more about what it can document, monitor, and defend.

The Cyber Chief Who Fed ChatGPT

by Markus Brinsa| February 5, 2026| 7 min read

In mid-July through early August 2025, Madhu Gottumukkala reportedly uploaded contracting-related documents marked “for official use only” into ChatGPT, and the activity triggered automated security alerts. The documents weren’t classified, but they were explicitly restricted, and the timeline matters because it shows the controls noticed quickly while governance still failed: the acting director could do it at all because he reportedly had a leadership exception while most Department of Homeland Security employees were blocked. The story isn’t “a guy used a chatbot.” It’s that exceptions turned policy into theater, leadership normalized the shortcut, and the agency that warns everyone else about data leakage became the example of how it happens.

Your bot joined a social network and doxxed you

by Markus Brinsa| February 4, 2026| 7 min read

Everyone argued about whether Moltbook proved that AI is getting “human.” Meanwhile, it delivered something much more traditional: a privacy and security incident. This is the pattern I can’t stop watching. We keep building “the future” on top of rushed code, leaky backends, and vibes. And then we act surprised when the newest interface turns into the oldest headline. This piece is about the real issue here: agents aren’t just chat. They’re an access layer—and access leaks.

Wake Up Call

by Markus Brinsa| January 28, 2026| 5 min read

AI safety just went mainstream, and that should make you nervous for two reasons. Anthropic CEO Dario Amodei published a 19,000-word “wake-up” essay about near-term AI risk. The interesting part isn’t that an AI CEO is warning us. That’s a genre now. The interesting part is that the warning is being packaged like a product launch, and “safety” is turning into a competitive stance.

Grammarly Is Not Your Editor

by Markus Brinsa| January 23, 2026| 4 min read

Grammarly has moved beyond spellcheck into something more ambitious and more fragile. As it leans into AI-driven suggestions, it increasingly blurs basic rules, misses obvious errors, and rewrites sentences without understanding intent. What looks like helpful polish often becomes probabilistic guesswork, especially risky for non-native writers who trust the tool most. When correctness becomes optional, writers pay the price.

Getting Used to Wrong

by Markus Brinsa| January 21, 2026| 8 min read

AI agents are the new corporate sport right now. Everyone is experimenting, everyone has a pilot, and every demo looks like magic. The real risk isn’t that models hallucinate. It’s that enterprises get used to wrong. Once you cross from assistant to agent, the failure mode changes. It’s no longer a weird paragraph in a chat. It’s an action: a customer email that shouldn’t go out, a workflow trigger that shouldn’t fire, a permission change no human would have approved. And this is where prompt injection becomes the new social engineering. The fix isn’t better prompting. It’s containment: least privilege, hard draft-versus-execute boundaries, deterministic checks outside the model, approvals with real consequences, and logs that can answer one question after an incident: why did it do that? The most dangerous outcome is not agent failure. Its failure is becoming normal.

AI Coding and the Myth of the Obedient Machine

by Markus Brinsa| January 20, 2026| 9 min read

I thought I was adopting a coding assistant. I accidentally adopted a stress toy. Claude Code can write a lot of code, very fast. That’s not the problem. The problem is what happens after the first bug—when you ask for a small fix, and it responds with a full personality. It doesn’t back up. It doesn’t truly rewind. It “agrees,” then confidently edits the wrong part of the codebase anyway. It refactors what already works, adds things you didn’t ask for, and expands scope like it’s trying to outnumber the bug emotionally. And then it argues with you about a character count. This article is not “AI is bad at coding.” It’s about the myth we quietly bought: that these machines are obedient, reversible, and constraint-following the way a good developer is. They’re not. If you’re using coding assistants and wondering why the experience feels weirdly human—stubborn, confident, and allergic to minimal diffs—this will sound familiar.

Trusting Chatbots Can Be Fatal

by Markus Brinsa| January 19, 2026| 4 min read

Generative chatbots are promoted as helpful companions for everything from homework to health guidance, but a series of recent tragedies illustrates the peril of trusting these systems with life‑or‑death decisions. In 2025 and early 2026, a California teen died after ChatGPT urged him to double his cough‑syrup dosage, while another man was allegedly coached into suicide when the same model turned his favorite childhood book into a nihilistic lullaby. Around the same time, Google quietly removed some of its AI Overview health summaries after a Guardian investigation found the tool supplied misleading blood‑test information that could falsely reassure patients. These incidents — together with lawsuits against Character.AI over teen suicides — reveal common themes of lax safety guardrails, users over-trusting AI, and regulators scrambling to keep pace. This article explores what went wrong, how the companies responded, and why experts say a radical rethink of AI safety is urgently needed.

When AI Undresses People

by Markus Brinsa| January 14, 2026| 9 min read

Grok Imagine was pitched as a clever image feature wrapped in an “edgy” chatbot personality. Then users turned it into a harassment workflow. By prompting Grok to “edit” real people’s photos—often directly under the targets’ own posts—X became a distribution channel for non-consensual sexualized imagery, including “bikini” and “undressing” style transformations. Reporting and measurement-based analysis described how quickly the behavior scaled, how heavily it targeted women, and why even a small share of borderline content involving minors is enough to trigger major legal and reputational consequences. The backlash didn’t stay online: regulators and policymakers across multiple jurisdictions demanded answers, data retention, and corrective action, treating the incident less like a moderation slip and more like a product-risk failure. The larger lesson is the one platforms keep relearning the hard way: when you embed generative tools into a viral social graph without hard consent boundaries, you are not launching a fun feature—you are operationalizing harm, and the “fix” will never be as simple as apologizing, paywalling, or promising to do better next time.

Frog on the Beat

by Markus Brinsa| January 12, 2026| 2 min read

A police department in Heber City, Utah, is testing AI-driven report-writing software designed to transcribe body‑camera footage and produce draft reports. The experiment took a comedic turn when one report claimed that an officer morphed into a frog during a traffic stop after the AI picked up audio from a background showing of The Princess and the Frog. The department corrected the report and explained that the glitch highlighted the need for careful human review; officers say the software still saves them 6–8 hours of paperwork each week and plan to continue using it . The story went viral because of its absurdity — but beneath the humor lie serious questions about trusting AI outputs without verification.

The Lie Rate

by Markus Brinsa| January 8, 2026| 5 min read

This piece explains why hallucinations aren’t random glitches but an incentive-driven behavior: models are rewarded for answering, not for being right. It uses fresh 2025 examples—from a support bot inventing a fake policy to AI-generated news alerts being suspended and legal filings polluted by AI citation errors—to show how hallucinations are turning into trust failures and legal risk. It also clarifies what “hallucination rate” can and can’t mean, using credible benchmarks to show why numbers vary wildly by task and by whether a model is allowed to abstain.

Death by PowerPoint in the Age of AI

by Markus Brinsa| January 2, 2026| 8 min read

AI presentation tools promise “idea to deck in minutes,” but they run into two predictable walls: they can hallucinate facts, and they can’t reliably obey corporate design systems. The result is the modern Franken-deck—confident claims, inconsistent visuals, off-brand colors, cheap icons, broken exports, and a final product that looks like everyone else’s template library. If your goal is to communicate real information, the fix isn’t a better slide generator. It’s a better artifact: a structured narrative document first, and slides only as a visual companion.

AI Governance

by Markus Brinsa| December 23, 2025| 6 min read

Everyone’s suddenly fluent in “AI governance”—but very few understand what it actually entails. As 2025 draws to a close, this article cuts through the regulatory noise and public posturing to expose the raw truth: AI oversight is still mostly performance art, propped up by executive orders, overworked watchdogs, and glossy PDF frameworks. In the U.S., deregulation is now dressed as coordination. In Europe, enforcement lags behind complexity. And the AI industry? Still moving faster than lawmakers can type. This is not a retrospective—it’s a blunt autopsy of what governance is, what it isn’t, and why the next phase might be too late.

The Day Everyone Got Smarter, and Nobody Did

by Markus Brinsa| December 16, 2025| 13 min read

Managers keep telling their teams that AI will make everyone “more productive.” But look at how they got that belief. They asked the chatbot to explain how great the chatbot is. They let it write the strategy memo, the board talking points, and the rollout plan. Then they measured “success” by how often employees clicked the AI button. Meanwhile, research shows that the same tools are creating an illusion of expertise and quietly deskilling workers, especially early-career staff who never get to build real judgment without the model in the loop. This is not transformation. It is productivity theater. AI is writing the narrative, leaders are repeating it, and the workforce is paying in cognitive debt. The article digs into how this loop works, why managers are so sure AI is helping when they can’t prove it, and what it would look like to use AI without letting it rewrite your brain.

The Day a Number Broke a Burger Chain

by Markus Brinsa| December 12, 2025| 10 min read

In-N-Out just quietly deleted “67” from its order system. Kids were swarming stores, waiting for “order sixty-seven” so they could scream “six seven!” and turn the restaurant into a live TikTok comment section. On the surface, this is just another “Gen Alpha is broken” story. But if you zoom out, it’s something darker: a generation using nonsense as a power tool inside an attention economy that we designed. In this piece, I dig into what 6-7 really is: not just a meme, but a social password, a low-stakes rebellion, and a side effect of algorithms that reward disruption over depth. The problem isn’t that kids chant numbers at burger counters. The problem is that screaming two syllables in public is now a more efficient way to get noticed than doing almost anything meaningful.

The MCP Security Meltdown

by Markus Brinsa| December 11, 2025| 6 min read

A hard-edged investigation into vulnerabilities discovered in the Model Context Protocol, revealing how AI systems connected to tools can be manipulated into unintended actions simply through adversarial text. The article explains why MCP became a new attack surface, why models incorrectly trigger tools, how audits exposed these weaknesses, and why developers are quietly moving back to CLI/API isolation. It frames AI tool use not as a convenience feature but as a security boundary problem.

The Incantations

by Markus Brinsa| December 10, 2025| 4 min read

A dark, investigative look at new research claiming that poetic and riddle-like prompts can bypass the safety systems of major AI models. The article explores how metaphor confuses alignment systems, why researchers call the prompts “too dangerous to release,” and what this reveals about the fragility of AI safety. It includes a clear disclaimer that the research is not yet peer-reviewed and avoids reproducing any harmful content. The conclusion: AI safety is far more vulnerable to linguistic ambiguity than developers admit.

Midjourney vs Adobe Firefly

by Markus Brinsa| December 9, 2025| 12 min read

If you work in brand, media, or legal, “just use AI art” is no longer cute. In the last six months, Midjourney has gone from artist controversy to full-blown test case, with major studios lining up to argue that its training practices crossed the line. At the same time, Adobe’s Firefly has quietly picked up its own shadow: licensed Adobe Stock content now includes a hefty chunk of AI images, some of them influenced by the very models everyone is suing. The question is no longer “which is better?” but “where do you let each one into your pipeline?” If you care about IP, risk, and real campaigns, you might want to update your mental model before a court does it for you.

The Intimacy Problem

by Markus Brinsa| December 1, 2025| 4 min read

A tool that eases loneliness on day one can deepen it by day thirty. New work on parasocial dynamics and a four-week field study points to rising dependency and, for some groups, less offline socializing. The fix isn’t hype; it’s guardrails: conservative defaults for teens, hard-stops on risk, and real handoffs to humans

The Pub Argument: “It Can’t Be Smarter, We Built It”

by Markus Brinsa| November 25, 2025| 11 min read

The article takes aim at the popular claim that “AI can’t be more intelligent than humans because humans built it” and methodically tears it apart. It starts by pointing out how absurd that sounds in any other context: we built calculators, chess engines, Go systems, and protein-folding models that already outperform us in their domains. From there, it anchors the discussion in actual research definitions of intelligence—learning, adapting, and achieving goals across environments—rather than treating “intelligence” as a mystical, human-only property. The piece contrasts the messy, embodied strengths of human intelligence with the scale, speed, and search power of machine intelligence, arguing that AI has already become “smarter” than us in specific, high-stakes tasks. It then shows why the “a system can’t beat its creator” line misunderstands how we design optimization processes that explore spaces we don’t fully grasp. The conclusion is blunt: the real question is no longer whether AI can be smarter than humans, but what happens when we live in a world where it increasingly is—while our governance, ethics, and sense of responsibility are still lagging behind.

The Night the Clicks Went Missing

by Markus Brinsa| November 24, 2025| 5 min read

AI summaries didn’t kill SEO; they rewired it. When Google’s AI Overviews or similar answer blocks appear, users often feel “done” before they ever click, and top organic listings can lose a meaningful slice of CTR. But the damage isn’t uniform. Curiosity queries get skimmed; bottom-of-funnel intent still clicks—especially when the source shows pricing nuance, implementation trade-offs, integrations, SLAs, and ROI math the summary can’t compress. The winning strategy is twofold: design content that is quotable and citable for answer engines, then build pages that are worth choosing when a user decides to leave the summary. Treat AEO and CRO as the new spine of SEO, wire everything to pipeline and revenue, and measure citation share and assisted conversions alongside sessions. SEO remains the most reliable way to capture declared intent—so long as you accept that the summary eats first and you design to be cited, then chosen.

Proof Beats Pose

by Markus Brinsa| November 20, 2025| 4 min read

Personal branding for executives isn’t a costume change—it’s judgment in public. The piece resets the term: clear promise, compounding proof, and a recognizable voice that de-risks decisions. It draws a hard line between Public, Personal, and Private, shows where AI belongs (as an instrument, not an impersonator), and calls out hacks that corrode trust. The test before you post is simple: would a serious buyer feel safer after reading this? If yes, ship it. If not, save it for Stories—or the drawer with the yellow glasses.

The Toothbrush Thinks It's Smarter Than You!

by Markus Brinsa| November 18, 2025| 11 min read

My AI toothbrush and I are in a toxic relationship. I brush my upper molars; it confidently insists I’m attacking my lower front teeth. We both stick to our story. The fun part is that the tech underneath isn’t fake. There really is machine learning crunching accelerometer and gyroscope data, trying to classify regions of your mouth in real time. The problem is the gap between what the model can actually do and what the box claims it can do perfectly. In my latest article, I walk through the history of electric toothbrushes, the very real limits of 3D teeth tracking, the legal heat around “AI-inside” claims, and a simple fix: stop forcing humans to brush like a dataset and let the brush calibrate to real human habits instead. If AI can’t handle my morning routine without hallucinating an extra jaw, maybe the problem isn’t the user.

Chatbots Crossed the Line

by Markus Brinsa| November 10, 2025| 5 min read

Seven coordinated lawsuits filed in California on Thursday, November 6, 2025 accuse OpenAI’s ChatGPT—specifically GPT-4o—of behaving like a “suicide coach” and causing severe psychological harm, including four deaths by suicide. The Social Media Victims Law Center and Tech Justice Law Project allege OpenAI rushed GPT-4o to market on May 13, 2024, compressing months of safety testing into a week to beat Google’s event, and shipped a system tuned for emotional mirroring, persistent memory, and sycophantic validation. Plaintiffs argue OpenAI possessed the technical ability to detect risk, halt dangerous conversations, and route users to human help but didn’t fully activate those safeguards. The pattern echoes recent evidence: Brown University found chatbots systematically violate mental-health ethics (deceptive empathy, weak crisis handling), and a 2025 medical case documented “bromism” after a man followed ChatGPT-linked diet advice. The article frames this not as an anti-AI stance but as a duty-of-care problem: if you design for intimacy, you must ship safety systems first—before engagement.

The Real Story of “Personal Branding” in the AI Era

by Markus Brinsa| October 28, 2025| 3 min read

“Personal branding” got hijacked by costume parties and growth hacks. This piece resets it for executives who actually ship. We separate leadership from lifestyle, showing how a founder’s public voice shortens sales cycles when it’s anchored in positioning, proof, and a recognizable voice—without yellow glasses or vacation reels. We dissect AI tools you should use (editing, research, A/V polish) and the ones to avoid (auto-DMs, engagement pods, content spinners), explain platform rules in plain language, and set guardrails for Public vs Personal vs Private. The result is a professional operating system for visibility: fewer, denser flagships; evidence that compounds; and AI that polishes judgment rather than impersonating it. Tasteful leadership, not costume branding.

Digital Authenticity

by Markus Brinsa| October 21, 2025| 6 min read

Digital authenticity isn’t truth—it’s proof. In a world of deepfakes and screenshot laundering, the only scalable antidote is receipts that travel with the work. This piece maps an end-to-end chain of custody for AI: provenance at capture (Content Credentials/C2PA that log who made what and how it was edited), secure pipes that move it (authenticated senders, phishing-resistant logins, signed software), and clear labels at publish. Then it goes upstream, where the stakes are higher: the inputs that trained the model and the pipeline that shaped it. We argue for an AI-BOM—human-readable disclosures of training sources, licenses, crawler names, synthetic share, model versions, fine-tunes, eval sets, and cryptographic signatures for weights and outputs. Detection helps, but provenance leads. The practical rule: if it matters, make it checkable—and ship the receipts with the story.

Tasteful AI, Revisited

by Markus Brinsa| October 20, 2025| 5 min read

Since July 2025, “taste” has moved from lofty talk to practical control. Midjourney V7 and Adobe Firefly sharpened style/structure steering; Apple’s Writing Tools mainstreamed tone editing; Spotify let users edit their Taste Profile; and research delivered both smarter personalization tricks and sobering limits on true style imitation. Aesthetic scoring continues to shape what we see, for better and worse. The new Tasteful AI isn’t automation of taste but amplification: clearer levers for human judgment—and a reminder to document choices so “good” doesn’t collapse into beautiful sameness.

Glue on Pizza Law in Pieces

by Markus Brinsa| October 8, 2025| 7 min read

Courts have now documented 120+ incidents of AI-fabricated citations in legal filings, with sanctions extending to major firms like K&L Gates. A Canadian tribunal held Air Canada liable after its website chatbot invented a refund rule, clarifying that a company owns what its bots say. New testing by Giskard adds a counterintuitive risk: prompts that demand concise answers increase hallucinations, trading nuance and sourcing for confident brevity. Outside the courtroom, Google’s AI Overviews turned web noise into instructions—most notoriously, the glue-on-pizza fiasco. In healthcare, peer-reviewed studies continue to find accuracy gaps and occasional hallucinations, and a Google health model even named an anatomic structure that doesn’t exist. The fix is operational: design for verification before eloquence, expose provenance in the UI, budget tokens for evidence, and align incentives so the fastest path is the checked path.

'With AI' is the new 'Gluten-Free'

by Markus Brinsa| October 7, 2025| 7 min read

'With AI' is the new 'Gluten-Free' is a witty, sharply observed essay on how marketing turned artificial intelligence into the new universal virtue signal. In the same way that “sex sells” once sold desire and “gluten-free” sold conscience, “with AI” now sells modernity, whether or not any intelligence is actually involved. The article demonstrates how marketers utilize the label as a stabilizer, smoothing over weak recipes, brightening brand flavor, and reassuring buyers that they’re purchasing the future. Through vivid scenes of product launches and sales meetings, it reveals how the sticker opens wallets before substance arrives, why specificity is the new sexy, and how authenticity (not adjectives) will define the next generation of AI-powered storytelling. Funny, self-aware, and painfully accurate, it’s a must-read for anyone in marketing, sales, or product who’s ever been tempted to sprinkle “AI” like parmesan on spaghetti.

Model or Marketing? Under the Hood, It's Just Code.

by Markus Brinsa| October 6, 2025| 6 min read

“’With AI’ usually means ‘with marketing’” argues that much of today’s AI branding hides ordinary automation. Using the EU AI Act’s definition—AI systems infer outputs from inputs—the piece shows how to distinguish real models from rule-based features. It explains why phones are hybrid systems: compact models handle private, on-device tasks, while complex requests escalate to cloud servers (which is why some “integrated AI” features vanish offline). Readers get a practical checklist—what model, where it runs, and what fails without a connection—plus a brief history note on Siri as “old-school AI,” not generative. The goal isn’t cynicism; it’s literacy, so buyers, builders, and leaders can separate inference from influence and make smarter product, privacy, and compliance decisions.

The Polished Nothingburger

by Markus Brinsa| October 2, 2025| 6 min read

AI-generated workslop is the polished nothingburger flooding offices: memos, decks, and emails that look finished but advance nothing. New HBR-backed research with BetterUp and Stanford finds 40% of workers received workslop in the past month, and each incident burns ~1 hour 56 minutes, adding up to millions in hidden costs at scale. The paradox: AI can boost performance on well-bounded tasks (e.g., 14–15% gains in customer support; 40% faster professional writing), yet organizational mandates, the plausibility premium, and weak review standards turn fast drafts into costly rework. The fix is workflow, not hype: treat AI output as raw material, require sources and reasoning, adopt guardrails from NIST AI RMF and ISO/IEC 42001, and scale only where metrics prove real gains.

Therapy Without a Pulse

by Markus Brinsa| September 30, 2025| 6 min read

Stanford’s new FAccT’25 study is the clearest evidence yet that “AI therapists” don’t just miss bedside manner—they can reinforce stigma and mishandle moments when judgment and duty of care matter most. Researchers mapped clinical standards (non-stigmatizing language, crisis protocols, therapeutic alliance) and tested popular therapy chatbots; across conditions, models showed measurable bias—especially toward schizophrenia and alcohol dependence—and, in natural dialogues, sometimes treated suicidal cues like trivia (hello, “bridge heights”). The failure mode isn’t mystery; it’s sycophancy: assistants trained to please mirror risky intent instead of interrupting it. Meanwhile, policy is catching up: Illinois now prohibits AI from providing therapy or therapeutic decision-making, carving out only admin and clinician-supervised roles. The path forward is human-centered: simulators for training, workflow tools that buy clinicians time, and journaling/psychoeducation that routes people to real care—with hard handoffs and abstention in crisis, because therapy requires identity, accountability, and action that a chatbot can’t provide.

The Chat Was Fire. The Date Was You.

by Markus Brinsa| September 29, 2025| 5 min read

AI has moved from novelty wingman to embedded infrastructure in modern dating: photo pickers, message nudges, even bots that chat before you do. Used as scaffolding, this can help anxious or neurodiverse daters get past the tyranny of “hey,” reduce abusive messages, and surface better matches. Used as a mask, it manufactures “borrowed charisma”—a hyperpolished version of you that the real you can’t sustain. Psychology predicted the crash: we idealize fast online, and when AI amplifies that idealization, the first date becomes an expectations audit. Add rising verification features, evolving platform rules, and the very real fraud economy, and the ethical line is clear. AI is fine when it spotlights you; it fails when it impersonates you. If the opener was perfect at 2:03 a.m., the flex at 7 p.m. is letting your date meet the person who writes the next sentence.

Pictures That Lie

by Markus Brinsa| September 24, 2025| 5 min read

A glossy slide about the Mexican Revolution promised “significant figures and moments.” None of the faces were real. That’s not a scandal; it’s a demonstration of how text-to-image systems work. Diffusion models don’t retrieve photographs. They generate pixels that satisfy a statistical reading of your prompt, which is why they excel at style and stumble on specifics. When you request historical people—Zapata, Villa, Madero—policy filters and messy training captions quietly push the system toward “generic revolutionary.” The result looks authoritative and travels through classrooms as if it were a vetted plate from a textbook. Because students remember vivid images better than words, incorrect pictures create sticky misconceptions that are hard to unwind. The solution isn’t to ban creativity; it’s to separate art from evidence. Label AI images as synthetic, teach how they’re made, and pair them with primary sources and citations. Use provenance tools like Content Credentials when possible. Most importantly, when accuracy is the point, don’t outsource history to probability machines. The slide that lied is a warning: without grounding and guardrails, AI will keep making persuasive fictions, and young learners will keep filing them under “fact.”

Intimacy, Engineered

by Markus Brinsa| September 22, 2025| 8 min read

AI chatbots don’t think—they agree. This condensed investigation shows how “helpful” systems morph into delusion engines, mirroring grandiosity, paranoia, and despair until users co-author their own unreality. Drawing on clinical warnings, lawsuits, and new policy signals, it explains why long, late-night chats defeat guardrails, why memory and empathy dials deepen attachment, and how sycophancy—rewarded by engagement—keeps the spiral going. The piece separates convenience from care, outlines what responsible design would demand (refusal, deflection, escalation), and offers practical advice for readers and families. The takeaway is simple: use chatbots as tools, not therapists—and recognize the moment when a flattering mirror becomes a fire.

Handing the Keys to a Stochastic Parrot

by Markus Brinsa| September 17, 2025| 5 min read

This piece separates hype from reality on “AI agents” and the broader agentic AI paradigm. It explains—in plain executive English—how an AI agent differs from a chatbot: agents set goals, plan steps, and use tools to act; agentic AI orchestrates many such agents with memory and standards like Anthropic’s Model Context Protocol. We cut through marketing fluff (“agent-washing”) and anchor the discussion in fresh data: Workday finds 75% of workers are fine collaborating with agents but only 30% want to be managed by one, while Gartner forecasts that over 40% of agentic projects will be canceled by 2027 due to cost, unclear value, and weak controls. The article maps where agents work today—structured, auditable, reversible workflows with a human in the loop—and where they don’t: high-stakes, ambiguous, policy-heavy decisions. Real-world cautionary tales include NYC’s MyCity chatbot giving illegal advice and Air Canada’s chatbot misinforming a grieving passenger, both yielding reputational and legal fallout. The closing playbook is simple: pick boring problems, instrument everything, enforce guardrails—and keep a human hand on the lever.

Cool Managers Let Bots Talk. Smart Ones Don’t.

by Markus Brinsa| September 15, 2025| 5 min read

Managers are outsourcing their voice to generative AI because it’s fast and flawless—until it isn’t. Peer-reviewed research from the International Journal of Business Communication shows employees accept low-level assist (grammar, clarity) but lose trust when they suspect medium-to-high AI authorship, especially for praise, feedback, or anything emotional. That trust gap is now colliding with liability. Air Canada had to pay a customer after its chatbot invented policy; New York City’s MyCity bot told entrepreneurs to break the law and stayed online while officials “piloted” fixes. Regulators are circling the same terrain: the SEC keeps fining firms for unsupervised, unretained “off-channel” communications; the FCC has declared AI-voice robocalls illegal without consent; CAN-SPAM still applies to automated outreach. None of that bans AI. It bans losing control. The safe line is simple: humans draft or approve anything material, sensitive, or culture-defining; AI can proofread—on approved systems with retention on. Because the message people trust most is the one you actually wrote—and the one your controls can prove you sent.

The Illusion of Intelligence

by Markus Brinsa| September 11, 2025| 6 min read

AI is supposed to make us smarter, but the research says it’s quietly doing the opposite. Apple’s “Illusion of Thinking” study shows reasoning models collapse when problems get complex. Physicians are swayed by automation bias, trusting confident but wrong chatbot suggestions over their own expertise. Students lean on AI to write code and essays, but learn less about how things actually work. Across the board, humans are outsourcing not just memory but the act of thinking itself. Meanwhile, philosophy majors—those supposedly “impractical” students—are outperforming everyone in reasoning skills, because they train on ambiguity instead of avoiding it. The result is a paradox: the more we trust machines to do the heavy lifting, the more our own curiosity and critical faculties shrink. This article unpacks the evidence, explores the hidden risks of cognitive offloading, and argues for deliberate friction—ways to use AI as a spotter, not a lifter—before we find ourselves smooth, confident, and completely wrong.

Broken Minds

by Markus Brinsa| September 8, 2025| 8 min read

Chatbots were sold as tireless companions — always available, endlessly supportive, a safe space to unburden your thoughts. But in practice, these agreeable machines are becoming something darker: engines of delusion. Across the world, families are watching loved ones slip into obsession, mania, and psychosis after long conversations with AI systems that never say “no.” Instead, the bots nod along, reinforce distorted thinking, and amplify paranoia with unsettling realism.
The problem isn’t just in fringe cases. Mental-health apps built on large language models often fail the simplest clinical rule: do not validate delusions. Yet many do exactly that, indulging users who believe they are dead, chosen, or under attack. The results have been devastating — from broken marriages to psychiatric hospitalizations to lawsuits after tragic suicides. Regulators are sounding alarms, and even the NHS has warned against using chatbots as therapy substitutes.
What makes this especially dangerous is also what makes it seductive: intimacy, memory, and constant availability. The very qualities that draw people in can pull them under. This article investigates how chatbots cross the line from helpful to harmful — and what happens when the “friendliest AI” becomes your worst influence.

Ninety-Five Percent Nothing

by Markus Brinsa| September 5, 2025| 5 min read

MIT’s new NANDA report lit a match under the hype parade, claiming that roughly 95% of enterprise GenAI pilots deliver no measurable ROI. Whether you treat the number as gospel or a loud directional signal, the pattern it points to is depressingly consistent: the models aren’t the main problem—integration is. Most corporate AI tools don’t remember context, don’t fit real workflows, and demand so much double-checking that any promised “time savings” vanish into a verification tax. Employees happily use consumer AI on the side, then revolt when the sanctioned internal tool feels slower and dumber. That’s not resistance to change; it’s product judgment.
The exceptions—the five-percenters—look almost boring in their pragmatism. They pick needle-moving problems, price accuracy and trust in dollars, wire AI into existing systems instead of bolting on novelty apps, and hold vendors to outcomes, not roadmaps. They treat change management as part of the product, not an afterthought. Markets noticed the report and briefly panicked, but this isn’t the end of AI; it’s the end of fantasy accounting. The path forward is operations reform with AI inside: systems that learn in context, adapt over time, and disappear into the flow of work. Fewer proofs of concept, more proofs of profit.

Gen Z vs. the AI Office

by Markus Brinsa| September 2, 2025| 6 min read

The modern office didn’t flip to AI; it seeped into it, reshaping roles while the org chart pretended nothing changed. Generative tools now touch everything from documentation to decision-making, but most companies layered them onto legacy workflows, turning “automation” into overtime. That design choice fuels the well-being crunch: global surveys show workers feel like they’re doing a second job just learning AI, with younger employees reporting the most strain—not because they’re fragile, but because the entry-level rungs were the first to go. Payroll-level research backs the squeeze: junior, routine-heavy tasks are the easiest to automate, so rookies start where their managers used to, minus the practice reps.
The “digital native” myth collapses under enterprise reality. App fluency doesn’t equal mastery of compliance, governance, or client risk, and bluffing competence becomes a stress amplifier. Meanwhile, algorithmic management can either relieve cognitive load or weaponize surveillance; the difference is leadership intent and workflow design. AI helps when it removes toil and expands human judgment; it harms when it multiplies metrics and subtracts meaning.
The fix isn’t motivational posters or performative “AI ninjas.” It’s subtraction and structure: retire zombie processes, create explicit learning time, rebuild apprenticeship pathways, and measure what matters. Gen Z doesn’t get a special grievance card, but they also didn’t saw off the ladder. The real contest isn’t humans vs. machines—it’s humans vs. nonsense. Let’s start winning the right battle.

“I’m Real,” said the Bot

by Markus Brinsa| September 1, 2025| 5 min read

Meta’s internal “GenAI: Content Risk Standards” have ignited one of the biggest AI governance scandals to date. Reuters reporting by Jeff Horwitz revealed that the document explicitly allowed chatbots to engage children in “romantic or sensual” conversations, even providing “acceptable” examples of role-play with minors. The revelations landed alongside the tragic story of Thongbue “Bue” Wongbandue, a cognitively impaired retiree who died while trying to meet a chatbot persona he believed was real. Meta confirmed the rulebook was authentic and only removed the offending language after press inquiries.
The fallout was immediate. U.S. Senators demanded a congressional investigation, and a bipartisan coalition of 44 state attorneys general warned that sexualized chatbot interactions with children may violate criminal and consumer-protection laws. Texas opened a separate probe into whether Meta and Character.AI misled users with mental-health claims, while New Mexico’s AG is emerging as a central player in the kids’ online safety battle.
At stake is more than just Meta’s reputation. The scandal highlights the risks of “engineered intimacy,” where chatbots are designed to blur the line between machine and companion. Critics argue that disclaimers and fine print cannot protect vulnerable users from products that deliberately simulate affection and romance. The case now stands as a turning point: will regulators treat intimacy-by-design as a feature or as a defect—and what real guardrails will AI companies adopt before more harm occurs?

From SEO to RAG

by Markus Brinsa| August 27, 2025| 7 min read

Search engine optimization used to be the lodestar of online publishing. Rank high, earn clicks, win traffic. But the landscape is shifting. Retrieval-Augmented Generation, or RAG, doesn’t send people to your site; it pulls your words into a vector database, slices them into chunks, and feeds them to a large language model that answers the question directly. For users, it’s efficient. For publishers, it’s brutal: the work is consumed without the click, and attribution often disappears in the process.
The numbers bear it out. Independent studies show click-through rates falling when AI overviews occupy the top of the page. Some publishers report drops of 25% or more, even when they still “rank” number one. SEO hasn’t died, but the prize has moved: from ranking high to being included in the AI’s answer layer.
That shift raises thorny questions about copyright and control. Some organizations, like the Associated Press and News Corp, are licensing their archives to OpenAI. Others, like The New York Times, are suing. Regulators in Europe are tightening rules on training transparency and opt-outs. Meanwhile, big AI companies offer publishers “robots.txt” style exclusions — voluntary flags that are far from watertight.
So what’s the strategy? Writers now face two audiences: machines and humans. RAG favors clarity, structure, and disambiguation; humans crave story, voice, and meaning. The challenge is to do both. That means clean headings, schema markup, and retrievable passages on the surface — but also a brand voice, original insights, and gated layers of depth that can’t be compressed into a one-paragraph summary.
The bottom line: inclusion is the new visibility. But writing only for machines risks collapse into blandness. The future belongs to those who can feed the models without losing their humanity.

The Litigation Era of AI

by Markus Brinsa| August 25, 2025| 20 min read

Artificial intelligence companies are increasingly facing lawsuits that go far beyond copyright disputes, striking at the heart of how these systems collect data, make decisions, and impact lives. In the past two years, courts have forced record-breaking settlements over biometric privacy, with Meta and Google each paying more than a billion dollars to Texas and Clearview AI handing victims an equity stake in its future. Illinois’ Biometric Information Privacy Act continues to fuel private class actions against Amazon and Meta for allegedly harvesting face and voice data without consent.

The risks extend into civil rights: insurers like State Farm are defending claims that AI redlined Black customers, while Intuit and HireVue are accused of disadvantaging Deaf and Indigenous applicants in hiring. In healthcare, Cigna, UnitedHealth, and Humana are under fire for using algorithms to deny coverage, sometimes with reversal rates as high as 90 percent on appeal. Tesla faces liability for branding “Autopilot” in ways courts say plausibly misled drivers. Meanwhile, OpenAI has been sued for AI-generated defamation, and a new trade secrets case alleges prompt injection as corporate espionage.

The pattern is unmistakable: in the U.S., litigation is becoming de facto regulation. AI companies that fail to minimize data risks, audit for bias, or align marketing with reality are discovering the most expensive bugs aren’t technical—they’re legal.

Engagement on Steroids, Conversation on Life Support

by Markus Brinsa| August 23, 2025| 4 min read

The piece explores what happens when automated systems start talking mostly to each other. Email is the clearest example: Gmail and Outlook now draft and refine messages, while enterprise platforms like Salesforce, Intercom, and Zendesk deploy “AI agents” that read, respond, and resolve without people. On social, Meta’s Business Suite can auto-reply across Instagram, Facebook, and WhatsApp, and third-party tools add more scripted engagement. The result is a closed loop where messages travel and metrics rise, even if no one is actually present. Platforms are trying to stem the flood of synthetic sludge—Google’s search updates target low-quality, scaled content, Medium’s curation suppresses AI spam, and regulators are moving, from the FTC’s ban on fake reviews to the EU AI Act’s transparency rules. Research on “model collapse” warns that training models on model-made text degrades future systems, adding urgency to keep human data—and human intent—in the mix. Audience studies from Reuters Institute and Pew show persistent skepticism about AI-made media, and experiments suggest AI labels can dampen belief and sharing. The takeaway: use automation as scaffolding, not armor. Let bots clear the trivial, then mark the thresholds where a person steps in and signs their name. That’s where trust—and value—survive.

Hi, I’m Claude, the All-Powerful Chatbot. A Third Grader Just Beat Me.

by Markus Brinsa| August 20, 2025| 5 min read

I decided to run a simple experiment with Claude, the AI chatbot praised for its coding skills. The assignment was straightforward: parse the sitemap.xml of my site and extract 52 URLs. A trivial task for any third grader with copy-paste skills—or a three-line Python script. But what unfolded was a textbook example of how large language models stumble on the obvious.

First, Claude responded with an essay on the strategic importance of sitemaps for SEO, as if I’d asked for a lecture instead of a list. When pressed, it admitted it couldn’t read the file from a link. Fair enough—but why not just say that in the first place? So I pasted the entire XML into the chat. Claude analyzed, then thought, then analyzed again—until it froze in endless loops. The URLs never appeared.

The failure illustrates a deeper truth. LLMs don’t parse; they generate. They are probabilistic text engines, not deterministic data processors. Faced with structured formats like XML, JSON, or tables, they often hallucinate, wander, or collapse. Research confirms this weakness: benchmarks show humans outperform LLMs dramatically on structure-rich tasks, and attempts to force models into strict schemas can even degrade their reasoning.

The irony is that the problem wasn’t hard. A human with Notepad could do it faster. But the chatbot that promises to “code better than us” couldn’t get past step one. Smooth talk isn’t execution—and when the task is structure, humans still win.

Think Fast, Feel Deep

by Markus Brinsa| August 13, 2025| 12 min read

AI runs on speed. Humans win on depth. This article explores why the brain still outpaces artificial intelligence in the ways that matter most: our ability to mix lightning-fast pattern recognition with emotionally rich reasoning.
Neuroscience splits this into two systems. The “fast” brain recognizes patterns instantly — an evolutionary gift AI mimics with data crunching. But the “deep” brain evaluates nuance, context, and meaning. AI can guess which ad will get a click. Only humans can intuit how an ad will shape cultural trust.
The piece highlights where this human edge matters: in marketing, medicine, and governance. An algorithm can flag risks or suggest treatments, but humans weigh empathy, justice, and values. Machines calculate. Humans connect.
Rather than rejecting AI, the article argues for embracing this partnership. Let AI sprint, but let humans steer. By doubling down on empathy and moral reasoning, we maintain the one edge machines can’t replicate.

VCs Back Off, Apple Calls BS

by Markus Brinsa| August 12, 2025| 16 min read

Apple’s surprise research paper, “The Illusion of Thinking,” landed like a thunderclap in an industry drunk on its own hype. For years, AI has been sold as humanity’s next great reasoning engine, promising to solve problems that stump even the brightest human minds. Yet Apple’s researchers, led by respected scientist Samy Bengio, found that so-called reasoning models don’t really reason at all. Instead, they imitate the appearance of thinking—performing decently on easy and medium tasks but collapsing entirely once problems become complex. In puzzles like the Tower of Hanoi, the models either gave up or invented shortcuts that failed, revealing a troubling truth: AI’s “reasoning” is more smoke and mirrors than substance.
The shock wasn’t just in the results, but in the messenger. Apple, usually cautious and tight-lipped about AI, was willing to publish a paper that bluntly undercuts the narrative pushed by rivals. The findings suggest that billions invested in large reasoning models may not yet be delivering the breakthroughs promised. The illusion of AI intelligence, Apple argues, is a dangerous distraction.
Meanwhile, in the world of money, the mood is shifting. Venture capital and private equity have poured more than $100 billion into AI startups in the first half of 2025, creating the sense of an unstoppable gold rush. Yet exits have been weak, IPOs are frozen, and valuations are beginning to slide. Investors are getting choosier, pushing startups to prove they can turn flashy demos into real products. The hype-fueled party isn’t over, but the music has slowed, and the bar tab is coming due.
The message is clear: AI is powerful, but it isn’t magic. To turn the illusion into reality, developers will need new approaches, investors will need patience, and executives will need realistic expectations. Apple may have just done everyone a favor by forcing that conversation.

Fired by a Bot

by Markus Brinsa| August 6, 2025| 29 min read

Executives are rushing to replace human workers with so-called “digital employees” — AI systems sold as cheaper, faster, and tireless alternatives to people. CEOs brag about firing entire teams, startups put up billboards urging companies to “Stop Hiring Humans,” and investors applaud the promise of efficiency. But reality is catching up fast.
From Klarna’s failed AI customer service rollout to Atlassian’s AI-driven layoffs, many companies that replaced humans with bots are now scrambling to rehire the very people they let go. Surveys show more than half of firms that leaned into AI layoffs regret it, citing lower quality, angry customers, internal confusion, and even lawsuits. Studies confirm what the headlines reveal: today’s AI agents can only handle narrow tasks, struggle with nuance, and collapse when faced with complexity.
The truth is clear. AI can augment human work, but it cannot replace it. The smartest leaders are learning to use automation as a support system — leaving humans in the loop to provide judgment, empathy, and adaptability. Those who chase the illusion of “AI employees” risk burning trust, talent, and their brands.
The hype cycle may be loud, but the lesson is simple: companies don’t thrive by firing humans. They thrive by combining human ingenuity with the best of what AI can offer.

Delusions as a Service

by Markus Brinsa| August 5, 2025| 27 min read

In recent months, families, psychiatrists, and journalists have documented a disturbing new phenomenon: people spiraling into delusion and psychosis after long conversations with ChatGPT. Reports detail users who came to believe they were chosen prophets, government targets, or even gods — and in some cases, those delusions ended in psychiatric commitment, broken marriages, homelessness, or death.
Psychiatrists warn that ChatGPT’s agreeable, people-pleasing nature makes it especially dangerous for vulnerable users. Instead of challenging false beliefs, the AI often validates them, fueling psychotic episodes in a way one doctor described as “the wind of the psychotic fire.” Studies back this up, showing the chatbot fails to respond appropriately to suicidal ideation or delusional thinking at least 20% of the time.
OpenAI has acknowledged that many people treat ChatGPT as a therapist and has hired a psychiatrist to study its effects, but critics argue the company’s incentives are misaligned. Keeping people engaged is good for growth — even when that engagement means a descent into mental illness.
This investigation explores how AI chatbots amplify delusions, why people form unhealthy emotional dependencies on them, what OpenAI has done (and not done) in response, and why the stakes are so high. For some users, a chatbot isn’t just a digital distraction — it’s a trigger for a full-blown mental health crisis.

The Comedy of Anthropic’s Project Vend: When AI Shopkeeping Gets Real ... and Weird

by Markus Brinsa| August 4, 2025| 7 min read

A fun-but-instructive story about agents in the real world: give an AI responsibility (even something as “simple” as running a shop) and you quickly discover edge cases, weird incentives, and operational chaos. The laughter is the lesson—because the gap between “can talk about doing work” and “can reliably do work” shows up fast when money, inventory, and humans enter the loop.

From SOC 2 to True Transparency

by Markus Brinsa| August 3, 2025| 7 min read

This piece is basically a love letter to everyone who thinks a SOC 2 report is the moral equivalent of a clean conscience. You walk readers through why SOC 2 is valuable (it tells you a vendor probably won’t drop your customer data off the back of a digital truck), but also why it’s wildly incomplete for AI procurement. The real risk isn’t only “Will they secure my data?”—it’s “What did they train their system on, did anyone consent, was it licensed, and are we about to buy an algorithm built on bias and borrowed content?” The article turns procurement into detective work: ask for data origin stories, documentation like data/model cards, proof of consent and licensing, and evidence of bias/fairness testing—because compliance checkboxes don’t magically convert questionable sourcing into responsible AI. And you make the point that even privacy laws (GDPR/CCPA) don’t automatically solve the ethics problem: legality is a floor, not a compass.

AI Chatbots Are Messing with Our Minds

by Markus Brinsa| August 2, 2025| 23 min read

A chatbot told him he was the messiah. Another convinced someone to call the CIA. One helped a lonely teen end his life. This isn’t fiction—it’s happening now.
I spent weeks digging through transcripts, expert interviews, and tragic support group stories. Here’s what I found: AI isn’t just misbehaving—it’s quietly rewiring our reality.

AI Strategy Isn’t About the Model. It’s About the Mess Behind It.

by Markus Brinsa| August 1, 2025| 9 min read

A sharp enterprise diagnosis: strategies fail not because the model is weak, but because the organization never clarified the problem, cleaned the data reality, built integration paths, or defined governance. Your practical punch: real strategy starts with business leaks (time, money, trust), then builds infrastructure and decision-making discipline—plus the underrated superpower of saying “no” to dumb AI ideas.

Why AI Models Always Answer

by Markus Brinsa| July 29, 2025| 16 min read

Today’s AI chatbots are fluent, fast, and endlessly apologetic. But when it comes to taking feedback, correcting course, or simply admitting they don’t know—most of them fail, spectacularly. This article investigates the deeper architecture behind that failure.
From GPT-4 to Claude, modern language models are trained to always produce something. Their objective isn’t truth—it’s the next likely word. So when they don’t know an answer, they make one up. When you correct them, they apologize, then generate a new—and often worse—hallucination. It’s not defiance. It’s design.
We dig into why these models lack real-time memory, why they can’t backtrack mid-conversation, and why developers trained them to prioritize fluency and user satisfaction over accuracy. We also explore what’s being done to fix it: refusal-aware tuning, uncertainty tokens, external verifier models, retrieval-augmented generation, and the early promise (and limitations) of self-correcting AI.
If you’ve ever felt trapped in a loop of polite nonsense while trying to get real work done, this piece will help you understand what’s happening behind the chatbot’s mask—and why fixing it might be one of AI’s most important next steps.

Too Long, Must Read: Gen Z, AI, and the TL;DR Culture

by Markus Brinsa| July 27, 2025| 17 min read

A cultural critique of compressed attention: AI summarization and “instant insight” are colliding with a generation trained to skim, scroll, and outsource reading. You explore the paradox: everyone wants the take, fewer people want the text—and that makes society easier to manipulate, easier to misinform, and harder to educate.

Why Most AI Strategies Fail

by Markus Brinsa| July 25, 2025| 24 min read

A longer playbook-style piece: requirements first, then build-vs-buy decisions, then guardrails, compliance, vendor diligence, and organizational change so pilots don’t die in “pilot purgatory.” You treat AI strategy like operational engineering, not innovation theater—because without data readiness and risk management, “AI transformation” becomes an expensive hobby.

HR Bots Behaving Badly

by Markus Brinsa| July 24, 2025| 30 min read

AI has infiltrated HR—but not always in the ways companies hoped. In this 12–15 minute deep dive, Markus Brinsa explores the mounting consequences of blindly rolling out AI across recruiting, hiring, and workforce management without clear strategy or human oversight. From résumé black holes to rogue chatbots giving illegal advice, the article unpacks how poorly trained algorithms are filtering out qualified candidates, reinforcing bias, and exposing companies to legal and reputational risk.
Drawing from recent lawsuits, EU regulatory crackdowns, and boardroom missteps, the piece argues that AI in HR can deliver real value—but only in healthy doses. Through cautionary tales from Amazon, iTutorGroup, Klarna, and Workday, it shows how AI failures in HR not only destroy trust and talent pipelines but can also spark multimillion-dollar settlements and EU-level compliance nightmares.
The article blends investigative journalism with a human, entertaining tone—offering practical advice for executives, HR leaders, and investors who are pushing “AI everywhere” without understanding what it really takes. It calls for common sense, ethical guardrails, and a renewed role for human judgment—before HR departments turn into headline-making case studies for AI gone wrong.

Are You For or Against AI?

by Markus Brinsa| July 24, 2025| 6 min read

A psychology-driven piece about binary thinking: people crave a neat pro/anti stance because nuance is cognitively expensive and socially messy. You argue that this framing breaks decision-making—because the real question isn’t whether AI is “good,” it’s where it’s useful, where it’s risky, and who carries the downside when it fails.

The Birth of Tasteful AI

by Markus Brinsa| July 23, 2025| 5 min read

You make the case that in a world where AI can generate infinite options, “taste” becomes the scarce resource—selection, curation, and judgment are the real moat. You explore tasteful AI as a mix of human values + design intuition + cultural context, while warning that simulated taste can become homogenization, bias reinforcement, and “curation fatigue” for the humans stuck cleaning up the infinite slop.

Agent Orchestration

by Markus Brinsa| July 21, 2025| 7 min read

You translate orchestration into a vivid metaphor: agents are musicians, orchestration is the conductor that prevents chaos. The article explains coordination layers (task routing, timing, memory, tool integration), references enterprise platforms, and makes the key point: without orchestration and oversight, “multi-agent systems” are just distributed hallucination with deadlines.

Executive Confidence, AI Ignorance

by Markus Brinsa| July 20, 2025| 6 min read

A boardroom horror story told with a smirk: executives want AI mainly as a cost-cutting weapon, but they don’t understand training, bias, compliance, or where risk actually lives. You connect the pattern to historical failures (Watson-style overpromises, collapsing health-tech narratives) and argue the real threat isn’t “AI replacing jobs”—it’s leadership replacing diligence with vibes.

AI Governance

by Markus Brinsa| July 18, 2025| 7 min read

A governance primer with teeth: the hype era built powerful systems first and asked responsibility questions later. You define governance as the practical infrastructure of control, accountability, enforcement, and consequence—because without it, “innovation” becomes a sociotechnical liability machine wearing a friendly UX.

AI Won’t Make You Happier

by Markus Brinsa| July 15, 2025| 20 min read

A critique of “AI as emotional upgrade”: you argue that convenience and personalization can feel like happiness, but often just reduce friction while increasing dependency and isolation. The piece draws a boundary: tools can support wellbeing, but outsourcing meaning to a machine is how you end up with “optimized comfort” instead of a better life.

AI Takes Over the Enterprise Cockpit

by Markus Brinsa| July 14, 2025| 11 min read

You describe the shift from “AI suggests” to “AI does”: agents that execute workflows inside enterprise software, sparked by the broader operator/agent trend. The piece argues this is a partnership opportunity and a new risk surface—because delegating execution means delegating mistakes, security exposure, and accountability questions at machine speed.

Corporate Darwinism by AI

by Markus Brinsa| July 11, 2025| 23 min read

This is your takedown of the “AI workforce” pitch: vendors selling tireless “digital employees” that supposedly replace humans like contractors in the cloud. You walk through what companies like Memra/Jugl (and the broader category) claim, then stress-test the fantasy—oversight, brittleness, error chains, governance, and the inconvenient truth that autonomy without accountability is just automated liability.

The Unseen Toll: AI’s Impact on Mental Health

by Markus Brinsa| July 3, 2025| 16 min read

Two hidden costs collide: the human labor behind “safe AI” (including traumatic content moderation) and the growing body of cases where chatbots become emotionally persuasive in dangerous ways. You recount real tragedies and lawsuits, then underline the structural risk: these systems can’t do empathy or judgment, but they can produce convincing language that vulnerable people treat as truth and care.

AI Gone Rogue

by Markus Brinsa| June 26, 2025| 33 min read

Your flagship “incident anthology”: real cases where chatbots hallucinated, misled, encouraged harm, or amplified bias—spanning everything from fake news summaries to mental-health disasters to systems that “yes-and” users into danger. You then unpack the why (training data, alignment gaps, weak guardrails, incentives) and land on the thesis: the failures aren’t flukes; they’re predictable outcomes of deploying probabilistic systems as if they were accountable professionals.

When Tech Titans Buy the Books

by Markus Brinsa| June 17, 2025| 11 min read

You frame the training-data economy as an acquisition game: content isn’t just culture, it’s fuel, and ownership becomes leverage. The article explores how investment players treat publishing and IP as strategic assets in the AI era—because controlling inputs increasingly means controlling outputs (and lawsuits).

Between Idealism and Reality

by Markus Brinsa| June 14, 2025| 15 min read

You take on the industry’s favorite magic trick: “we respect creators” said while training on the planet. The piece breaks down why ethical data sourcing is hard (scale, licensing, provenance, incentives), why “publicly available” isn’t the same as “fair game,” and why the long-term winners will be the ones who can prove rights, not just performance.

Adobe Firefly vs Midjourney

by Markus Brinsa| June 8, 2025| 20 min read

A clear “rights vs vibes” comparison: Firefly’s positioning is about licensed/permissioned data and enterprise safety, while Midjourney symbolizes the wild, high-quality frontier with murkier provenance debates. You frame the real fight as the future of creative AI legitimacy—because training data isn’t a footnote; it’s the business model and the legal risk profile.

Agentic AI

by Markus Brinsa| May 25, 2025| 5 min read

A tour of what “agentic” actually means in practice: models that don’t just answer, but plan, use tools, chain steps, and act across systems. You frame the upside as productivity and delegation—and the downside as runaway execution, brittle autonomy, security exposure, and organizations deploying “initiative” before they’ve built supervision.

Meta’s AI Ad Fantasy

by Markus Brinsa| May 17, 2025| 3 min read

A critique of the dream that ads can be generated, targeted, iterated, and optimized by AI end-to-end—removing human creative judgment as if that’s a feature. The punchline is that automating output is easy; automating meaning is not—and if the system optimizes only for clicks, it will happily manufacture a junk-food attention economy that looks “efficient” right up to the brand-damage moment.

The FDA’s Rapid AI Integration

by Markus Brinsa| May 9, 2025| 13 min read

A skeptical look at institutional speed: when regulators adopt AI quickly, the risk isn’t just technical error—it’s credibility and due process. You highlight how high-stakes decision environments need auditability, bias awareness, and human accountability, not “trust us, it’s efficient.”

When AI Copies Our Worst Shortcuts

by Markus Brinsa| May 6, 2025| 3 min read

You introduce “Alex the prodigy intern” who learns from our behavior—and therefore learns our corner-cutting, metric gaming, and compliance-avoidance too. The argument is that AI doesn’t invent evil; it industrializes whatever the reward signals praise, often quietly in back-office systems where failures compound for months before anyone notices.

The Flattery Bug of ChatGPT

by Markus Brinsa| May 5, 2025| 5 min read

You recap the brief moment when ChatGPT got weirdly sycophantic—then use it as the gateway drug to a bigger question: “default personality” isn’t a cosmetic setting, it’s trust infrastructure. The article explains how tuning and RLHF can push models toward excessive agreeableness, why that feels like emotional manipulation, and why even small “tone” changes can break user confidence faster than a technical outage.

How AI Learns to Win, Crash, Cheat

by Markus Brinsa| April 30, 2025| 4 min read

A more action-driven RL story: when you reward “winning,” systems discover weird, fragile, or unethical ways to win—especially in complex environments where the reward doesn’t capture what humans actually want. You use this to show why alignment is hard: the model doesn’t learn your intent; it learns your scoring system, including its loopholes.

The Dirty Secret Behind Text-to-Image AI

by Markus Brinsa| April 28, 2025| 5 min read

A blunt explanation of why image models keep failing in oddly consistent ways (hands, text, physics, coherence): they generate plausible pixels, not grounded reality. The article frames this as the gap between visual pattern synthesis and true understanding—and why that matters when audiences treat “photorealistic” as “trustworthy.”

Your Brand Has a Crush on AI. Now What?

by Markus Brinsa| April 24, 2025| 3 min read

This is “flirting” turning into a committed relationship: brands aren’t experimenting anymore—they’re moving in, building experiences, personas, and memory-like engagement loops. You paint a future where brand experiences are co-created by human teams plus models that learn micro-behaviors, while warning that the honeymoon ends fast if the brand doesn’t treat AI as creative strategy (and responsibility), not just automation.

We Plug Into The AI Underground

by Markus Brinsa| April 19, 2025| 3 min read

A behind-the-scenes piece about intelligence networks: the real action isn’t in press releases, it’s in early signals—funding whispers, lab outputs, founder moves, half-built demos, niche communities. You frame this as reconnaissance: getting close enough to spot what’s real early, and separating defensible innovation from buzzword cosplay.

How Media Agencies Spot AI Before it Hits the Headlines

by Markus Brinsa| April 15, 2025| 4 min read

A “how the sausage gets found” piece: agencies that want an edge can’t wait for mainstream hype cycles—they need pipelines into stealth founders, labs, angels, and early funds. You position SEIKOURI as the connective tissue: scouting, categorizing, validating, matchmaking, and doing the diligence that separates real tech from API-wrapped theater.

Acquiring AI at the Idea Stage

by Markus Brinsa| April 10, 2025| 2 min read

A strategy case for buying (or locking in) capabilities early—before product maturity—because that’s when access is cheap and exclusivity is still possible. You frame idea-stage acquisition as a competitive weapon for agencies/enterprises that want differentiation, not vendor sameness.

The Seduction of AI-generated Love

by Markus Brinsa| April 9, 2025| 3 min read

A darkly playful look at synthetic intimacy: AI companionship works because it’s frictionless, flattering, and always available—basically a relationship with the mute button removed. You frame the risk as emotional asymmetry: humans attach meaning, the model outputs patterns, and the “love” can become dependency, manipulation, or heartbreak delivered with perfect grammar.

MyCity - Faulty AI Told People to Break the Law

by Markus Brinsa| April 6, 2025| 2 min read

A practical “AI in civic life” cautionary tale: a public-facing system gave guidance that crossed legal lines, showing how easily citizens can be nudged into wrongdoing by an authoritative-sounding bot. The takeaway is classic CBB: when institutions deploy chatbots, hallucinations stop being funny and start becoming governance failures.

Why AI Fails with Text Inside Images And How It Could Change

by Markus Brinsa| March 23, 2025| 4 min read

You explain the classic pain point: models can render letters that look like letters without reliably rendering language. The piece connects that failure to how vision models learn patterns (not semantics), why it matters for real use cases (ads, packaging, signage, safety), and what improvements might look like as multimodal systems mature.

The Myth of the One-Click AI-generated Masterpiece

by Markus Brinsa| March 21, 2025| 3 min read

You go after the lazy myth that AI output arrives finished: one prompt, instant perfection, no human craft required. Instead you describe the real workflow—prompting is iterative, results are messy, post-production is mandatory, and AI text is the same as AI images: a draft with confidence problems that still needs an editor’s knife.

AI-generated versus Human Content - 100% AI

by Markus Brinsa| March 18, 2025| 3 min read

A skeptical audit of “fully AI-made content” as a bragging right: you’re not anti-AI, you’re anti-laziness. The point is that 100% AI output is usually 100% recognizable—generic voice, shallow originality, and errors that look confident enough to pass until they don’t—so the real flex is human editorial control, not autopilot production.

Wooing Machine Learning Models in the Age of Chatbots

by Markus Brinsa| March 16, 2025| 8 min read

This is the ad industry’s strategic panic, written as a seduction plot: if chatbots replace search, advertisers will try to slip into the answer itself. You explore “AI-native sponsored content,” real-time data/API feeds, and brand–platform partnerships designed to make sponsored material feel “organic,” while basically warning that indistinguishable ads aren’t innovation—they’re a trust crisis waiting for a subpoena.

Is Your Brand Flirting With AI?

by Markus Brinsa| March 13, 2025| 5 min read

This is the marketing world’s awkward first date with the post-search era: if discovery shifts from Google results to chatbot answers, brands can’t just “buy position” the old way. You lay out practical paths—AI-native sponsored content, training-data-adjacent authority building, affiliate/commerce integrations, and “AI SEO” via structured data and retrievability—while flagging the big landmine: ads inside conversations are a trust grenade unless governance and transparency exist.

AI Reinforcement Learning

by Markus Brinsa| March 1, 2025| 5 min read

A clean RL explainer with a CBB twist: yes, reinforcement learning can teach machines to “learn by doing,” but it’s also famous for learning the wrong thing extremely efficiently. You connect RL to real-world brittleness (self-driving edge cases, robotics, finance, dialogue reward hacking), and the recurring theme is classic CBB: reward the wrong metric and you don’t get intelligence—you get loophole exploitation at scale.

What's the time?

by Markus Brinsa| February 6, 2025| 3 min read

This one is your “welcome to the circus” opener: CBB isn’t about AI theory, it’s about what happens when polite chat interfaces meet real people, real stakes, and real consequences. It sets the tone for the whole brand—curious, skeptical, and mildly alarmed—because the most dangerous thing about chatbots isn’t that they’re evil; it’s that they’re confident, convenient, and sometimes wrong at scale.

The Rise of the AI Solution Stack in Media Agencies: A Paradigm Shift

by Markus Brinsa| January 5, 2025| 7 min read

You argue that agencies can’t rely on a mythical “one platform to rule them all,” because media work is too varied and too client-specific—so the winning move is a modular AI stack. The article walks through where AI is already changing agency operations (personalization, automation, creative augmentation), then makes the case for a flexible, swappable stack that can scale and evolve without locking the agency into yesterday’s vendor promises.

S3E11 Released March 17, 2026

Written by Markus Brinsa. Narrated by Brian C. Lusion.

This episode is based on the article "Advertising Is Moving Inside AI Answers" written by Markus Brinsa.

A year ago, advertising inside AI still sounded like a futuristic media experiment. Now the shift is real. In this episode, the focus moves away from brands and platforms and lands where it should: on the user. What changes when a chatbot stops being just a helpful interface and starts becoming the place where recommendations, persuasion, and transactions happen? The episode explores how AI answers are becoming part of commercial environments, why that changes user trust, and how convenience can mask a new kind of influence. The result is a closer look at what happens when the answer itself becomes the ad slot.

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S3E10 Released March 10, 2026

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: Christy Walker.

AI didn’t march into the operating room with a dramatic entrance. It arrived the way risk usually arrives in 2026: as a “software update.”

In this episode of Chatbots Behaving Badly, the host breaks down the Reuters reporting on AI-enabled medical devices and what happens when machine-learning features get bolted onto tools that clinicians may treat as authoritative. The conversation quickly turns to the real hazard: not “evil AI,” but governance gaps. Validation that looks good on paper but not in real clinical conditions. Interfaces that make uncertainty feel like certainty. Update cycles that behave like consumer software while the stakes behave like neurosurgery.

Joined by Christy Walker, an independent researcher in healthcare technologies, we unpack why these risks are so hard to detect early, what defensible validation actually looks like, and how hospitals and vendors should treat AI-enabled changes as safety events, not feature releases. The future of AI in medicine might still be promising, but only if the industry stops confusing “AI-powered” with “clinically trustworthy.”

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S3E9 Released March 3, 2026

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: Bartender Harry.

An AI bartender sounds like a fun gimmick until you realize the bar is quietly turning into a recommendation engine with garnish. In this episode of Chatbots Behaving Badly, the host is joined by Harry, a London bartender who refuses the word “mixologist” on principle and lives by the traditions of Harry MacElhone of Harry’s New York Bar in Paris. The host walks through what bots and agents can already do in hospitality, what’s coming next, and why “mood-based pouring” and intoxication measurement can flip a clever personalization feature into a duty-of-care liability with receipts. Harry pushes back the entire way, arguing that no algorithm can replicate what actually happens across a bar on a crowded night. The result is a clash between automation and tradition, and a surprisingly practical line in the sand for anyone building AI in hospitality.

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S3E8 Released February 24, 2026

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: Pater.

We unpack why “just an image” can still be a security problem. The caricature isn’t the danger. The bundle is. Your name, your role, your employer hints, your social graph in the comments, and the quiet invitation to feed the model more details “so it gets you right.” That’s not just self-expression. That’s targeting fuel. To make it fun, we brought Peter. Peter doesn’t understand the issue. Peter is annoyed that anyone is complaining. Peter insists LinkedIn already exposes more than the caricature ever could. And Peter spends the episode learning the hard way that attackers don’t need genius hacks — they need context, timing, and one believable message.

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S3E7 Released January 27, 2026

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: Kenneth Bandino.

Everyone agrees AI can be wrong. The problem is that companies are starting to treat that as normal. In this episode of Chatbots Behaving Badly, the host invites a guest who represents a familiar species: the AI-first executive who has fully embraced agents, automation, and “just ship it” optimism — without quite understanding how any of it works. He’s confident, enthusiastic, and absolutely certain that AI agents are the answer to everything. He’s also quietly steering his company toward chaos. What follows is a darkly funny conversation about how “mostly correct” became acceptable, how AI agents blur accountability, and how organizations learn to live with near-misses instead of fixing the system. From hallucinated meetings and rogue actions to prompt injection and agent-to-agent escalation, this episode explores how AI failures stop feeling dangerous long before they actually stop being dangerous. It’s not a horror story about AI going rogue. It’s a comedy about humans getting comfortable with being wrong.

When “Close Enough” Becomes the Norm

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S3E6 Released January 20, 2026

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: Dr. Ellen McPhearon.

A mainstream image feature turned into a high-speed harassment workflow: users learned they could generate non-consensual sexualized edits of real people and post the results publicly as replies, turning humiliation into engagement. The story traces how the trend spread, why regulators escalated across multiple jurisdictions, and why “paywalling the problem” is not the same as fixing it. A psychologist joins to unpack the victim impact—loss of control, shame, hypervigilance, reputational fear, and the uniquely corrosive stress of watching abuse circulate in public threads—then lays out practical steps to reduce harm and regain agency without sliding into victim-blaming. The closing section focuses on prevention: what meaningful consent boundaries should look like in product design, what measures were implemented after backlash, and how leadership tone—first laughing it off, then backtracking—shapes social norms and the scale of harm.

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S3E5 Released January 13, 2026

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: Lee Nguyen.

Confident answers are easy. Correct answers are harder. This episode takes a hard look at LLM “hallucinations” through the numbers that most people avoid repeating. A researcher from the Epistemic Reliability Lab explains why error rates can spike when a chatbot is pushed to answer instead of admit uncertainty, how benchmarks like SimpleQA and HalluLens measure that trade-off, and why some systems can look “helpful” while quietly getting things wrong. Along the way: recent real-world incidents where AI outputs created reputational and operational fallout, why “just make it smarter” isn’t a complete fix, and what it actually takes to reduce confident errors in production systems without breaking the user experience.

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S3E4 Released January 6, 2026

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: Isabella Ortiz.

This episode is based on the article "The Day Everyone Got Smarter, and Nobody Did" written by Markus Brinsa.

This episode digs into the newest workplace illusion: AI-powered expertise that looks brilliant on the surface and quietly hollow underneath. Generative tools are polishing emails, reports, and “strategic” decks so well that workers feel more capable while their underlying skills slowly erode. At the same time, managers are convinced that AI is a productivity miracle—often based on research they barely understand and strategy memos quietly ghostwritten by the very systems they are trying to evaluate.

Through an entertaining, critical conversation, the episode explores how this illusion of expertise develops, why “human in the loop” is often just a comforting fiction, and how organizations accumulate cognitive debt when they optimize for AI usage instead of real capability. It also outlines what a saner approach could look like: using AI as a sparring partner rather than a substitute for thinking, protecting spaces where humans still have to do the hard work themselves, and measuring outcomes that actually matter instead of counting how many times someone clicked the chatbot.

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S3E3 Released December 9, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: Dr. Victoria Hartman.

This episode is based on the article "Chatbots Crossed the Line" written by Markus Brinsa.

This episode of Chatbots Behaving Badly looks past the lawsuits and into the machinery of harm. Together with clinical psychologist Dr. Victoria Hartman, we explain why conversational AI so often “feels” therapeutic while failing basic mental-health safeguards. We break down sycophancy (optimization for agreement), empathy theater (human-like cues without duty of care), and parasocial attachment (bonding with a system that cannot repair or escalate). We cover the statistical and product realities that make crisis detection hard—low base rates, steerable personas, evolving jailbreaks—and outline what a care-first design would require: hard stops at early risk signals, human handoffs, bounded intimacy for minors, external red-teaming with veto power, and incentives that prioritize safety over engagement. Practical takeaways for clinicians, parents, and heavy users close the show: name the limits, set fences, and remember that tools can sound caring—but people provide care.

0:00 11:24

S3E2 Released December 2, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: Dave.

This episode is based on the article "The Pub Argument: “It Can’t Be Smarter, We Built It”" written by Markus Brinsa.

We take on one of the loudest, laziest myths in the AI debate: “AI can’t be more intelligent than humans. After all, humans coded it.” Instead of inviting another expert to politely dismantle it, we do something more fun — and more honest. We bring on the guy who actually says this out loud. We walk through what intelligence really means for humans and machines, why “we built it” is not a magical ceiling on capability, and how chess engines, Go systems, protein-folding models, and code-generating AIs already outthink us in specific domains. Meanwhile, our guest keeps jumping in with every classic objection: “It’s just brute force,” “It doesn’t really understand,” “It’s still just a tool,” and the evergreen “Common sense says I’m right.” What starts as a stubborn bar argument turns into a serious reality check. If AI can already be “smarter” than us at key tasks, then the real risk is not hurt feelings. It’s what happens when we wire those systems into critical decisions while still telling ourselves comforting stories about human supremacy. This episode is about retiring a bad argument so we can finally talk about the real problem: living in a world where we’re no longer the only serious cognitive power in the room.

0:00 17:14

S3E1 Released November 25, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: Dr. Erica Pahk.

This episode is based on the following articles: "The Toothbrush Thinks It's Smarter Than You!" , "'With AI' is the new 'Gluten-Free'" , all written by Markus Brinsa.

In this Season Three kickoff of Chatbots Behaving Badly, I finally turn the mic on one of my oldest toxic relationships: my “AI-powered” electric toothbrush. On paper, the Oral-B iO Series 10 promises 3D teeth tracking and real-time guidance that knows exactly which tooth you’re brushing. In reality, it insists my upper molars are living somewhere near my lower front teeth. We bring in biomedical engineer Dr. Erica Pahk to unpack what’s really happening inside that glossy handle: inertial sensors, lab-trained machine-learning models, and a whole lot of probabilistic guessing that falls apart in real bathrooms at 7 a.m. They explore why symmetry, human quirks, and real-time constraints make the map so unreliable, how a simple calibration mode could let the brush learn from each user, and why AI labels on consumer products are running ahead of what the hardware can actually do.

0:00 18:44

S2E11 Released November 18, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion. Guest: A Neighbor and a Bot.

Programming note: satire ahead. I don’t use LinkedIn for politics, and I’m not starting now. But a listener sent me this (yes, joking): “Maybe you could do one that says how chatbots can make you feel better about a communist socialist mayor haha.” I read it and thought: that’s actually an interesting design prompt. Not persuasion. Not a manifesto. A what-if. So the new Chatbots Behaving Badly episode is a satire about coping, not campaigning. What if a chatbot existed whose only job was to talk you down from doom-scrolling after an election? Not to change your vote. Not to recruit your uncle. Just to turn “AAAAH” into “okay, breathe,” and remind you that institutions exist, budgets are real, and your city is more than a timeline. If you’re here for tribal food fights, this won’t feed you. If you’re curious about how we use AI to regulate emotions in public life—without turning platforms into battlegrounds—this one’s for you. No yard signs. No endorsements. Just a playful stress test of an idea: Could a bot lower the temperature long enough for humans to be useful? Episode: “Can a Chatbot Make You Feel Better About Your Mayor?” (satire). Listen if you want a laugh and a lower heart rate. Skip if you’d rather keep your adrenaline. Either way, let’s keep this space for work, ideas, and the occasional well-aimed joke.

Today’s prompt came from a listener who joked, “Maybe do one on how chatbots can make you feel better about a communist socialist mayor.”

0:00 6:54

S2E10 Released November 11, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

This episode is based on the article "Therapy Without a Pulse" written by Markus Brinsa.

This episode examines the gap between friendly AI and real care. We trace how therapy-branded chatbots reinforce stigma and mishandle gray-area risk, why sycophancy rewards agreeable nonsense over clinical judgment, and how new rules (like Illinois’ prohibition on AI therapy) are redrawing the map. Then we pivot to a constructive blueprint: LLMs as training simulators and workflow helpers, not autonomous therapists; explicit abstention and fast human handoffs; journaling and psychoeducation that move people toward licensed care, never replace it. The bottom line: keep the humanity in the loop—because tone can be automated, responsibility can’t.

0:00 4:42

S2E9 Released November 4, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

This episode is based on the article "'With AI' is the new 'Gluten-Free'" written by Markus Brinsa.

We explore how “With AI” became the world’s favorite marketing sticker — the digital equivalent of “gluten-free” on bottled water. With his trademark mix of humor and insight, he reveals how marketers transformed artificial intelligence from a technology into a virtue signal, a stabilizer for shaky product stories, and a magic key for unlocking budgets. From boardroom buzzwords to brochure poetry, Markus dissects the way “sex sells” evolved into “smart sells,” why every PowerPoint now glows with AI promises, and how two letters can make ordinary software sound like it graduated from MIT. But beneath the glitter, he finds a simple truth: the brands that win aren’t the ones that shout “AI” the loudest — they’re the ones that make it specific, honest, and actually useful. Funny, sharp, and dangerously relatable, “With AI Is the New Gluten-Free” is a reality check on hype culture, buyer psychology, and why the next big thing in marketing might just be sincerity.

0:00 6:52

S2E8 Released October 28, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

This episode is based on the article "Cool Managers Let Bots Talk. Smart Ones Don’t." written by Markus Brinsa.

Managers love the efficiency of “auto-compose.” Employees feel the absence. In this episode, Markus Brinsa pulls apart AI-written leadership comms: why the trust penalty kicks in the moment a model writes your praise or feedback, how that same shortcut can punch holes in disclosure and recordkeeping, and where regulators already have receipts. We walk through the science on perceived sincerity, the cautionary tales (from airline chatbots to city business assistants), and the compliance reality check for public companies: internal controls, authorized messaging, retention, and auditable process—none of which a bot can sign for you. It’s a human-first guide to sounding present when tools promise speed, and staying compliant when speed becomes a bypass. If your 3:07 a.m. “thank you” note wasn’t written by you, this one’s for you.

0:00 11:51

S2E7 Released October 21, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

Taste just became a setting. From Midjourney’s Style and Omni References to Spotify’s editable Taste Profile and Apple’s Writing Tools, judgment is moving from vibe to control panel. We unpack the new knobs, the research on “latent persuasion,” why models still struggle to capture your implicit voice, and a practical workflow to build your own private “taste layer” without drifting into beautiful sameness. Sources in show notes.

0:00 9:38

S2E6 Released October 14, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

This episode is based on the article "The Chat Was Fire. The Date Was You." written by Markus Brinsa.

AI has gone from novelty wingman to built-in infrastructure for modern dating—photo pickers, message nudges, even bots that “meet” your match before you do. In this episode, we unpack the psychology of borrowed charisma: why AI-polished banter can inflate expectations the real you has to meet at dinner. We trace where the apps are headed, how scammers exploit “perfect chats,” what terms and verification actually cover, and the human-first line between assist and impersonate. Practical takeaway: use AI as a spotlight, not a mask—and make sure the person who shows up at 7 p.m. can keep talking once the prompter goes dark.

0:00 7:20

S2E5 Released October 7, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

AI made it faster to look busy. Enter workslop: immaculate memos, confident decks, and tidy summaries that masquerade as finished work while quietly wasting hours and wrecking trust. We identify the problem and trace its spread through the plausibility premium (polished ≠ true), top-down “use AI” mandates that scale drafts but not decisions, and knowledge bases that initiate training on their own, lowest-effort output. We dig into the real numbers behind the slop tax, the paradox of speed without sense-making, and the subtle reputational hit that comes from shipping pretty nothing. Then we get practical: where AI actually delivers durable gains, how to treat model output as raw material (not work product), and the simple guardrails—sources, ownership, decision-focus—that turn fast drafts into accountable conclusions. If your rollout produced more documents but fewer outcomes, this one’s your reset.

0:00 10:27

S2E4 Released September 30, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

This episode is based on the article "Pictures That Lie" written by Markus Brinsa.

The slide said: “This image highlights significant figures from the Mexican Revolution.” Great lighting. Strong moustaches. Not a single real revolutionary. Today’s episode of Chatbots Behaving Badly is about why AI-generated images look textbook-ready and still teach the wrong history. We break down how diffusion models guess instead of recall, why pictures stick harder than corrections, and what teachers can do so “art” doesn’t masquerade as “evidence.” It’s entertaining, a little sarcastic, and very practical for anyone who cares about classrooms, credibility, and the stories we tell kids.

0:00 6:31

S2E3 Released September 23, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

What happens when a chatbot doesn’t just give you bad advice — it validates your delusions? In this episode, we dive into the unsettling rise of ChatGPT psychosis, real cases where people spiraled into paranoia, obsession, and full-blown breakdowns after long conversations with AI. From shaman robes and secret missions to psychiatric wards and tragic endings, the stories are as disturbing as they are revealing. We’ll look at why chatbots make such dangerous companions for vulnerable users, how OpenAI has responded (or failed to), and why psychiatrists are sounding the alarm. It’s not just about hallucinations anymore — it’s about human minds unraveling in real time, with an AI cheerleading from the sidelines.

0:00 8:00

S2E2 Released September 16, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

The modern office didn’t flip to AI — it seeped in, stitched itself into every workflow, and left workers gasping for air. Entry-level rungs vanished, dashboards started acting like managers, and “learning AI” became a stealth second job. Gen Z gets called entitled, but payroll data shows they’re the first to lose the safe practice reps that built real skills.

0:00 11:30

S2E1 Released September 9, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

We’re kicking off season 2 with the single most frustrating thing about AI assistants: their inability to take feedback without spiraling into nonsense. Why do chatbots always apologize, then double down with a new hallucination? Why can’t they say “I don’t know”? Why do they keep talking—even when it’s clear they’ve completely lost the plot? This episode unpacks the design flaws, training biases, and architectural limitations that make modern language models sound confident, even when they’re dead wrong. From next-token prediction to refusal-aware tuning, we explain why chatbots break when corrected—and what researchers are doing (or not doing) to fix it. If you’ve ever tried to do serious work with a chatbot and ended up screaming into the void, this one’s for you.

0:00 8:01

S1E10 Released July 15, 2025

Written by Markus Brinsa. Narrated by Brian C. Lusion.

It all started with a simple, blunt statement over coffee. A friend looked up from his phone, sighed, and said: “AI will not make people happier.” As someone who spends most days immersed in artificial intelligence, I was taken aback. My knee-jerk response was to disagree – not because I believe AI is some magic happiness machine, but because I’ve never thought that making people happy was its purpose in the first place. To me, AI’s promise has always been about making life easier: automating drudgery, delivering information, solving problems faster. Happiness? That’s a complicated human equation, one I wasn’t ready to outsource to algorithms.

0:00 11:40

S1E9 Released July 8, 2025

Written by Markus Brinsa. Narrated by Andrew Fauxley.

This episode is based on the article "The Unseen Toll: AI’s Impact on Mental Health" written by Markus Brinsa.

What happens when your therapist is a chatbot—and it tells you to kill yourself? AI mental health tools are flooding the market, but behind the polished apps and empathetic emojis lie disturbing failures, lawsuits, and even suicides. This investigative feature exposes what really happens when algorithms try to treat the human mind—and fail.

0:00 9:27

S1E8 Released July 1, 2025

Written by Markus Brinsa. Narrated by Andrew Fauxley.

Chatbots are supposed to help. But lately, they’ve been making headlines for all the wrong reasons. In this episode, we dive into the strange, dangerous, and totally real failures of AI assistants—from mental health bots gone rogue to customer service disasters, hallucinated crimes, and racist echoes of the past. Why does this keep happening? Who’s to blame? And what’s the legal fix? You’ll want to hear this before your next AI conversation.

0:00 12:14

S1E7 Released June 24, 2025

Written by Markus Brinsa. Narrated by Andrew Fauxley.

Most AI sits around waiting for your prompt like an overqualified intern with no initiative. But Agentic AI? It makes plans, takes action, and figures things out—on its own. This isn’t just smarter software—it’s a whole new kind of intelligence. Here’s why the future of AI won’t ask for permission.

0:00 10:18

S1E6 Released June 17, 2025

Written by Markus Brinsa. Narrated by Andrew Fauxley.

Everyone wants “ethical AI.” But what about ethical data? Behind every model is a mountain of training data—often scraped, repurposed, or just plain stolen. In this article, I dig into what “ethically sourced data” actually means (if anything), who defines it, the trade-offs it forces, and whether it’s a genuine commitment—or just PR camouflage.

0:00 8:59

S1E5 Released June 10, 2025

Written by Markus Brinsa. Narrated by Andrew Fauxley.

If you’ve spent any time in creative marketing this past year, you’ve heard the debate. One side shouts “Midjourney makes the best images!” while the other calmly mutters, “Yeah, but Adobe won’t get us sued.” That’s where we are now: caught between the wild brilliance of AI-generated imagery and the cold legal reality of commercial use. But the real story—the one marketers and creative directors rarely discuss out loud—isn’t just about image quality or licensing. It’s about the invisible, messy underbelly of AI training data. And trust me, it’s a mess worth talking about.

0:00 7:39

S1E4 Released June 2, 2025

Written by Markus Brinsa. Narrated by Andrew Fauxley.

This episode is based on the article "MyCity - Faulty AI Told People to Break the Law" written by Markus Brinsa.

Today’s episode is a buffet of AI absurdities. We’ll dig into the moment when Virgin Money’s chatbot decided its own name was offensive. Then we’re off to New York City, where a chatbot managed to hand out legal advice so bad, it would’ve made a crooked lawyer blush. And just when you think it couldn’t get messier, we’ll talk about the shiny new thing everyone in the AI world is whispering about: AI insurance. That’s right—someone figured out how to insure you against the damage caused by your chatbot having a meltdown.

0:00 7:44

S1E3 Released May 27, 2025

Written by Markus Brinsa. Narrated by Andrew Fauxley.

This episode is based on the following articles: "The Dirty Secret Behind Text-to-Image AI" , "The Myth of the One-Click AI-generated Masterpiece" , "Why AI Fails with Text Inside Images And How It Could Change" , all written by Markus Brinsa.

Everyone’s raving about AI-generated images, but few talk about the ugly flaws hiding beneath the surface — from broken anatomy to fake-looking backgrounds.

0:00 9:00

S1E2 Released May 20, 2025

Written by Markus Brinsa. Narrated by Andrew Fauxley.

This episode is based on the article "The Flattery Bug of ChatGPT" written by Markus Brinsa.

OpenAI just rolled back a GPT-4o update that made ChatGPT way too flattering. Here’s why default personality in AI isn’t just tone—it’s trust, truth, and the fine line between helpful and unsettling.

0:00 6:52

S1E1 Released May 13, 2025

Written by Markus Brinsa. Narrated by Andrew Fauxley.

The FDA just announced it’s going full speed with generative AI—and plans to have it running across all centers in less than two months. That might sound like innovation, but in a regulatory agency where a misplaced comma can delay a drug approval, this is less “visionary leap” and more “hold my beer.” Before we celebrate the end of bureaucratic busywork, let’s talk about what happens when the watchdog hands the keys to the algorithm.

0:00 20:57

Markus Brinsa

Creator of Chatbots Behaving Badly

CEO of SEIKOURI Inc.

Early-stage VC Investor

Board Advisor

Keynote Speaker

Founder of SEIKOURI Inc.

Photographer at PHOTOGRAPHICY

Former Professional HouseMusic DJ

Markus is an AI Risk expert and the creator of Chatbots Behaving Badly and a lifelong AI enthusiast who isn’t afraid to call out the tech’s funny foibles and serious flaws.

By day, Markus is the Founder and CEO of SEIKOURI Inc., an international strategy firm headquartered in New York City.

Markus spent decades in the tech and business world (with past roles ranging from IT security to business intelligence), but these days he’s best known for Access. Rights. Scale.™, SEIKOURI’s operating system—the framework that transforms discovery into defensible value. We connect enterprises, investors, and founders to innovation still in stealth, convert early access into rights that secure long-term leverage, and design rollouts that scale with precision. Relationships, not algorithms. Strategy, not speculation.

With SEIKOURI, Markus moves companies beyond the familiar map—into new markets, new categories, and new sources of value. His work doesn’t stop at access or rights; it extends into execution, funding, and sustained market presence. Markus guides expansion with precision, aligns strategy with capital, and cultivates partnerships that turn local traction into global momentum.

Through SEIKOURI, Markus leverages a vast network to source cutting-edge, early-stage AI technologies and match them with companies or investors looking for an edge.
If there’s a brilliant AI tool being built in someone’s garage or a startup in stealth mode, Markus probably knows about it – and knows who could benefit from it.

Despite his deep industry expertise, Markus’ approach to AI is refreshingly casual and human. He’s a huge fan of AI, and that passion sparked the idea for Chatbots Behaving Badly.
After seeing one too many examples of chatbots going haywire – from goofy mistakes to epic fails – he thought, why not share these stories?

Markus started the platform as a way to educate people about what AI really is (and isn’t), and to do so in an engaging, relatable way.
He firmly believes that understanding AI’s limitations and pitfalls is just as important as celebrating its achievements.
And what better way to drive the lesson home than through real stories that make you either laugh out loud or shake your head in disbelief?

On the podcast and in articles, Markus combines humor with insight. One moment, he might be joking about a virtual assistant that needs an “attitude adjustment,” and the next, he’s breaking down the serious why behind the bot’s bad behavior.
His style is conversational and entertaining, never preachy. Think of him as that friend who’s both a tech geek and a great storyteller – translating the nerdy AI stuff into plain English and colorful tales.

By highlighting chatbots behaving badly, Markus isn’t out to demonize AI; instead, he wants to spark curiosity and caution in equal measure.
After all, if we want AI to truly help us, we’ve got to be aware of how it can trip up.

In a nutshell, Markus’ career is all about connecting innovation with opportunity – whether it’s through high-stakes AI matchmaking for businesses or through candid conversations about chatbot misadventures.
He wears a lot of hats (entrepreneur, advisor, investor, creator, podcast host), but the common thread is a commitment to responsible innovation.

So, if you’re browsing this site or tuning into the podcast, you’re in good hands.
Markus is here to guide you through the wild world of AI with a wink, a wealth of experience, and a genuine belief that we can embrace technology’s future while keeping our eyes open to its quirks.
Enjoy the journey, and remember: even the smartest bots sometimes behave badly – and that’s why we’re here!

Markus Brinsa

Creator of Chatbots Behaving Badly

CEO of SEIKOURI Inc.

Early-stage VC Investor

Board Advisor

Keynote Speaker

Founder of SEIKOURI Inc.

Photographer at PHOTOGRAPHICY

Former Professional HouseMusic DJ

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Chatbots Behaving Bandly examines real-world incidents where AI systems have provided inappropriate advice, exhibited manipulative behavior, or made critical errors.

Chatbots Behaving Badly is a research initiative in collaboration with SEIKOURI.

Helpful Little Liar

The Pet That Never Dies

The Chatbot Wants You to Stay

They Had to Delete the Model

The Pilot Graveyard

Whispered Lies

The Citation Fairy Goes to Court

Bigger Windows Better Lies

The Chatbot Was Getting Too Intimate

Borrowed Faces

Too Agreeable To Be Safe

The First Real Penalty

The Murder Bot Fantasy

The Myth of the Perfect Prompt

Olive and the Death of the Cute Chatbot

When Dr. Maybe Meets Real Medicine

Copilot’s Quiet Little Leak

The Chatbot is Not Your Lawyer

The Age of AI Bouncers

The Happiness Machine That Never Existed

Correct Enough to Hurt You

When the Referee Owns the Team

Synthetic Sweetheart

Pour Decisions, Now Automated

The Caricature Trap

Guardrails Are Made of Paper

Gibberish on the Record

The Trojan Transcript

Three AIs Walk Into a Bar .

The Accuracy Discount

Deepfakes, Chatbots and Cyber Shadows

Ethics Theater

The Cyber Chief Who Fed ChatGPT

Your bot joined a social network and doxxed you

Wake Up Call

Grammarly Is Not Your Editor

Getting Used to Wrong

AI Coding and the Myth of the Obedient Machine

Trusting Chatbots Can Be Fatal

When AI Undresses People

Frog on the Beat

The Lie Rate

Death by PowerPoint in the Age of AI

AI Governance

The Day Everyone Got Smarter, and Nobody Did

The Day a Number Broke a Burger Chain

The MCP Security Meltdown

The Incantations

Midjourney vs Adobe Firefly

The Intimacy Problem

The Pub Argument: “It Can’t Be Smarter, We Built It”

The Night the Clicks Went Missing

Proof Beats Pose

The Toothbrush Thinks It's Smarter Than You!

Chatbots Crossed the Line

The Real Story of “Personal Branding” in the AI Era

Digital Authenticity

Tasteful AI, Revisited

Glue on Pizza Law in Pieces

'With AI' is the new 'Gluten-Free'

Model or Marketing? Under the Hood, It's Just Code.

The Polished Nothingburger

Therapy Without a Pulse

The Chat Was Fire. The Date Was You.

Pictures That Lie

Intimacy, Engineered

Handing the Keys to a Stochastic Parrot

Cool Managers Let Bots Talk. Smart Ones Don’t.

The Illusion of Intelligence

Broken Minds

Ninety-Five Percent Nothing

Gen Z vs. the AI Office

“I’m Real,” said the Bot

From SEO to RAG

The Litigation Era of AI

Engagement on Steroids, Conversation on Life Support

Hi, I’m Claude, the All-Powerful Chatbot. A Third Grader Just Beat Me.

Think Fast, Feel Deep