Category: Artificial Decisions

Many people think they understand what artificial intelligence is. In reality, many don’t. Here is a simple explanation.

Many people believe they understand AI because they see it talk, write, and answer questions. Follow me to the end, because there is a big confusion to clear up. It is about wrong words and decisions we delegate without noticing.

The first misleading word is “algorithm”. An algorithm is a set of clear rules. Traditional software works like this. It is predictable. The same input gives the same output. AI does not work this way, generative AI does not follow fixed rules. It makes predictions.

Another wrong word is “automation”. Automation defines decisions in advance. AI does not. When we say “we automate with AI”, we are actually delegating decisions. Decisions about language, priority, inclusion, exclusion.

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🇺🇸🇬🇧 Sponsors paying AI to answer the way they want.

If AI follows the same path as social networks, answers will start to depend on who pays to push their message. And if you do not want paid answers, you will have to pay yourself.

Here in the United States, OpenAI announced that in the coming weeks it will start testing ads in ChatGPT. The test involves adult users on the free version and on the Go plan. Plus, Pro, and Enterprise stay ad-free. For now, ads should appear in a separate section at the bottom of answers, clearly labeled.

This is exactly how social networks started. Then it ended with scammers buying scam ads, often without being stopped. In many cases, up to 10% of revenue comes from fraudulent advertising.

OpenAI also says it wants to protect trust and privacy: no ads for users under 18, no ads next to topics like politics or health, and no selling conversation data to advertisers.

The real issue is not the banner itself. The issue is that when an assistant becomes the place where we ask what to buy, which bank to choose, or which doctor to trust, the order of answers becomes power. Today ads are separate and visible. Tomorrow they could be mixed into the text, a “sponsored” suggestion that looks like neutral advice. Visible ads are easy to spot. Ads blended into answers are not.

Think about everyday family questions. “What is the best car insurance in New York for a new driver?” “Which bank account is best if I have a salary and a mortgage?” “Which app should I use to invest small amounts safely?” “Which English course should I choose for my child?”

There is no single truth in these questions. There are rankings, based on criteria. If someone pays to rank higher, the “useful truth” changes. No lies are needed. You just highlight one feature, ignore another, and choose one example instead of others.

OpenAI says answers are not influenced by ads. That is good, but influence is not only in the final sentence. It is also in what is shown, what is left out, and how priorities are built. This is the same mechanism that changed web search over time.

What should we do? Use AI as a tool, not as a judge. For money, contracts, or health, always double-check with independent sources. When advice looks perfect, ask what is missing: alternatives, real costs, limits, negative reviews, conditions.

Otherwise, we risk becoming puppets guided by whoever pays to shape our opinions.

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Banning AI for Kids Is Like Banning Books Because Someone Throws Them

When something scares us, we want to ban it. AI, social media, smartphones. For minors, this sounds safe, but it often creates the opposite result: less skill, less awareness, more hidden use.

Social media runs on algorithms. An algorithm is a set of rules that tracks what you watch, how long you stay, what you click, what you ignore. Then it shows you more of what keeps you there. It is designed for attention, not for truth or quality. Kids need to understand this, otherwise the feed trains them.

AI works with probability, not certainty. It predicts the most likely answer based on patterns. It can be useful, but it can also be wrong, and it can sound confident while being wrong. That is why the key lesson is verification: ask for sources, cross-check, compare.

Money shapes content too. Ads and sponsored posts can push messages higher. And promoted influence will increasingly affect AI answers as well. If we do not teach people to recognize paid visibility and to trace information back to reliable sources, they will be guided by whoever pays.

A phone is not only distraction. It can be a learning tool: look up the meaning of a word, check a date, understand a concept, satisfy a curiosity fast. AI can support studying by asking targeted questions, helping you spot gaps, and explaining the same topic in different ways. Support, not replacement.

Bans are easy. Education works. We did not ban electricity because it is dangerous. We taught rules and built habits. Digital tools need the same: guidance, culture, and responsibility.

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First they tracked us. Now they build our decision twin.

Today there is something very real: a decision twin. It is a digital model of a person, built using data that already exists. Nobody needs to follow us live. They just query the model.

Companies collect data from many sources: web browsing, apps, location history, online and offline purchases, loyalty cards, streaming habits, social activity. Everything ends up in structured databases. These databases are analyzed with AI systems, often internal, often local. The system looks for patterns.

When someone types “John Doe”, the system returns a behavioral profile: daily routines, places visited, spending habits, price sensitivity, content watched, reaction to offers, purchase probability, risk level, priority inside automated systems.

Each piece contributes. The loyalty card adds purchases. The smart TV, through ACR, adds viewing habits. The phone adds movement and time. The browser adds attention and reading behavior. The car adds driving style. Wi-Fi adds presence and duration.

This decision twin is used to choose how to interact with that person: prices, ads, content, offers, access to services. Decisions happen before the person is aware.

This is driven by market logic. Companies compete on how well they can predict people. Better prediction means more value. There is no full exit. Leaving the digital world is not realistic. There is only damage reduction.

Every time we give data, we choose who gets it. Every app is a source. Every loyalty card is a trade. Every permission is a transfer. Limit permissions. Connect fewer accounts. Choose tools that collect less data. Treat data like money.

The decision twin grows by accumulation. Reducing that accumulation is the only real control we have today.

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The Chatbot That Moves Your Vote

Here in the United States, political persuasion is changing shape. It now happens through private, one-to-one conversations, with an artificial voice that answers, adapts, and pushes. A few minutes of dialogue can change an opinion.

A TV ad speaks to everyone in the same way. A chatbot follows the topics of the person in front of it: jobs, taxes, healthcare, cost of living. Every objection gets a reply. Every hesitation gets attention. The conversation becomes targeted, persistent, personal. Chatbots persuade more when they fill answers with data, references, examples.

Now add the next step. Chatbot answers will become advertising formats, like sponsored posts on social media. Someone will pay to push a reply, a source, a topic into the conversation. The message arrives with the neutral voice of an assistant that seems helpful, while it guides choices.

All of this happens in private. Each person receives a different version. No one sees what others are told. When political opinions are shaped inside personalized, monetized chats, power moves toward those who control technology, data, and budgets.

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If the internet goes off, daily life freezes

Have you ever thought about what happens if the internet goes off, all at once? You see it in minutes.

Grocery store: the terminal is on, but payments do not go through. Cards and wallets fail. Lines grow. Airport: planes are ready, but systems do not respond. In the US, in 2023, an FAA system issue stopped all domestic departures for hours. Work: laptops on, but email, cloud tools, sales and payments go offline. Every hour means money lost.

Healthcare: doctors keep working, but records, results and prescriptions become slow or unavailable. City: when power and connectivity fail together, coordination breaks. In San Francisco, a substation fire caused a large outage. Traffic lights went dark. Waymo paused service and robotaxis stopped at intersections.

The network is critical infrastructure, like electricity and water, often more. It holds the other systems together.

Are we protecting it enough?

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We look for the soul. Trusting what we see online is getting harder. So people look for something else. They look for the human soul.

We are surrounded by perfect videos. Voices with no hesitation. Faces with no small flaws. Movements too clean. Every platform is full of generated, corrected, optimized content. AI writes, speaks, edits, sets the rhythm. Everything looks smooth. Too smooth.

We no longer stop at the surface. We look for different signals. A pause at the wrong time. A sentence not perfect. A look that does not follow a script. Small friction that exists, not to please. As AI-made content improves, our sensitivity improves too. We can smell fake from far away.

At the time of the Lumière brothers, people ran away when they saw a train coming toward them on a screen. Today that train would scare no one. The same with beauty filters. At first they fooled us. Now we spot them instantly.

In 2024, research from MIT Media Lab showed that when people suspect AI content, they search for emotional coherence, not technical mistakes. They look for humanity. On social media, the most shared videos are not the cleanest. They show effort, discomfort, exposure. Real cost. Many creators keep mistakes, silences, breathing. Because they feel real.

AI can copy a voice. A face. A style. AI has nothing to lose. A human does. That difference is visible. With AI everywhere, this is what we will recognize first. Presence. Being there.

If you feel a real person online, stop. Keep them close. Follow them. They will be rare. We will need them.

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AI and digital: watch out for people selling “easy solutions”. They look friendly, they are selling hype

People want to understand AI. Some sellers use that confusion to sell courses that do not help.

Check the signals before you pay. If they sold NFTs yesterday, then the metaverse, and now AI, that is a red flag. If they cannot show real, verifiable work in the field, that is a red flag. If they use shocked faces and screaming titles like “insane”, that is a red flag. Being funny is not a credential.

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AI everywhere. Jobs are not. What to do now.

Here in the United States I see it every day: companies hire fewer people, but expect more skills. Smaller teams, broader roles. What happens here often reaches Europe soon after. Stop defending your job title. Defend your value. Show results: what you improved, what time you saved, what problems you solved.

Using AI is the baseline, not a superpower. Recruiters assume you can write, summarize, build slides, and analyze data with AI. The difference is knowing when to stop it, when to check it, and when not to use it. Courses are not enough. Proof wins. Real projects, real documents, reports, dashboards. A portfolio beats certificates. Buzzwords kill trust. Simple words and clear examples get you hired.

By 2027, the World Economic Forum says over 40% of required skills will change. Skills move fast. Responsibility stays human.

The best jobs go to people who guide decisions and processes, not to people who only use tools.

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Polymarket: when betting on everything feels like a drug

Here in the United States, Polymarket is changing how many people live the news. They do not just follow it. They trade it. Polymarket takes a real event and turns it into one hard question: will it happen or not. Two buttons. Yes or No. You open the platform, pick an event, then tap Yes if you think it will happen, or No if you think it will not. Each side has a price between $0 and $1.

People read that price like a live probability. $0.30 means 30%. $0.65 means 65%. You choose how much to put in and confirm. If the event happens, each winning share pays $1 when the market closes. If it does not happen, it goes to $0. You can also sell before the end if the price moves your way, like a stock.

The design is built for speed. Numbers update live. Charts move up and down. You can buy and sell again and again. Payments use stablecoins, so the time between decision and action is very short. A few seconds. This is where it stops being only “prediction”. It becomes a constant stimulus. Every small price move feels like a signal. Every refresh promises something.

During the 2024 US election, here in New York City, many people checked Polymarket many times per hour. Not to learn. To watch the number move. A speech, a rumor, a headline, then refresh. Price up, dopamine. Price down, stress. The same pattern showed up in 2023 markets about Federal Reserve decisions. People opened the app dozens of times a day, even with no real news. Open, check, close, repeat.

Research on online gambling shows that fast feedback and endless repeat actions increase the risk of compulsive behavior. Prediction markets add another twist: these events are not fantasy. They are politics, money, and safety. They touch everyone. Over time the line moves. You stop watching reality to understand it. You start wanting a certain outcome because you have money on it. The news becomes personal. The event becomes a private bet. The price becomes a habit: open, check, refresh. After a while it is not information anymore. It is the trigger.

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ChatGPT Health. Useful or dangerous?

ChatGPT Health is a new OpenAI feature in the United States. It is not open to everyone. You join a waitlist, and OpenAI enables access in stages. If you live in the European Union, the UK, or Switzerland, you cannot use it right now.

This matters because health is the most dangerous area to hand to a chatbot. The biggest risk is fake authority. The text sounds calm and confident, so people trust it. But here the decision is not a restaurant. It is a symptom, a lab result, a therapy, an emergency.

The second risk is data. Linking medical records and apps like Apple Health or MyFitnessPal makes answers more personal. It also makes your account more valuable. One stolen password, one shared phone, one unlocked laptop, and your private health data can leak.

The third risk is the chain. Data moves across many systems: hospitals, connectors, apps, then ChatGPT. Every step adds a weak point.

OpenAI says Health chats are not used to train the main models. Good. Still, people do not clearly understand what happens with logs, retention, incident response, or legal requests.

Use it only to understand terms and prepare questions for a doctor. Do not use it to decide care. A system that can be wrong and still sound right is a serious risk when the topic is your body.

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Hating institutions has become normal. And fake news are grateful for it

Hating institutions has become normal, and this is perfect for fake news. Stay with me until the end and I will explain why the phrase “they all cheat anyway” helps lies spread online.

When we repeat “they are all the same”, every official message is filtered with suspicion. A press release or a public speech looks biased by default. At the same time, a random video, a forwarded message or a post by an unknown user looks more “authentic” just because it seems to come from outside the system.

Disinformation works exactly on this ground. It does not only invent stories, it uses distrust that already exists. It picks sensitive topics such as health, safety, money, jobs or migration and builds content that confirms the idea that “they” always hide something. Hoaxes on 5G, for example, pushed people in several countries to attack antennas, damaging real infrastructure and services. These lies worked because they reinforced suspicions about institutions and companies.

The phrase “they all cheat anyway” also erases differences. It puts in the same group those who try to work seriously and those who abuse their role. In this way nobody deserves to be heard, and those who spread conspiracies gain credibility just by saying “I am not with them”.

To cut oxygen to fake news we need three things: more transparency on how decisions are taken, real consequences for those who lie in public roles, and critical thinking education for all ages.

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If you don’t control your website, AI controls your reputation

People now ask AI who you are and what you do. Stay with me to the end, I’ll give you a quick check.

AI learns from what it can read online, and most of that is the web. Social media is noisy and unstable. A website is clear, indexable, and easy to reuse as a source.

In the United States this already shows. Updated, detailed websites lead to better AI answers.

Quick check. Look at your last update date. Add real pages that explain what you do, with examples and an FAQ. Publish one solid update each month.

If you already knew this, share the video. If you didn’t, share it anyway. If you want to stay updated, hit follow.

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👉 Important note: We’re planning the upcoming months.
If you’d like to request my presence as a speaker at your event, please contact my team at: management@camisanicalzolari.com

AI breaks without ethics

✅ This video is brought to you by: https://www.ethicsprofile.ai

We are filling the world with artificial intelligence and we still treat it as if it were only software. Stay with me until the end, because ethics is not decoration, it changes real decisions.

Every AI system already carries a view of the world. Someone chooses the data, the goals, which errors are acceptable. Inside there are priorities: safety or freedom, profit or fairness, speed or accuracy. This is already philosophy at work, even if no one calls it that.

Here in the United States risk assessment tools in courts have shaped sentences for years. They punished entire groups because the prejudice was inside the data, and the AI turned it into a rule. No judge chose that philosophy, yet it entered the decisions anyway.

The same happens with moral dilemmas. The MIT Moral Machine project showed that different cultures pick different values in the same scenario: save one person or many, protect privacy or security. If these choices move into models without a clear process, we end up with hidden ethics deciding for us.

When ethics stays implicit, decisions stay implicit too. And step by step technology stops following our values, and we start following its values.

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We’re using the wrong word. AI doesn’t automate, it autonomizes

We are stuck on the wrong word. We keep saying that AI “automates everything”. Stay with me until the end and I will show you why this makes us underestimate the real problem.

To automate means something very simple. It means taking a clear procedure and making a machine execute it, always in the same way. Same input, same output.

ATM: card, correct PIN, sufficient balance, withdrawal. If you repeat the same steps, you get the same result. Old phone menus: “press 1 for…, press 2 for…”. This is automation. A deterministic, testable, certifiable procedure.

Modern AI works differently. The models that generate text, images, answers do not follow a fixed table of rules written by hand. They are probabilistic systems. They have seen millions of examples and, each time, they calculate which answer is most likely in that moment.

Ask the same question twice and the text can change. Change one word in the prompt and the whole output changes. There is no longer a simple “if A then B”. There is a system that interprets, estimates, decides.

When a company says “we automated customer service with AI”, very often it is doing something else. It puts a model in charge of deciding what to answer to the customer, whether to insist, whether to close, whether to pass you to a human operator. This is operational autonomy, not simple execution.

In the United States this is very clear. Driver assistance systems read sensors and images and choose how to move the car. Credit algorithms use personal data to decide who is “reliable” and who is not. There is no readable list of rules. There is a model that makes decisions with a large internal margin of freedom.

This is why I say that, in practice, AI autonomizes. It shifts pieces of autonomy from people to systems.

It autonomizes the filter in the call center. It decides which requests reach a human. It autonomizes hiring. It decides which CVs are shown first. It autonomizes the social media feed. It decides which political content to push to the top during a campaign, in Italy as well as in the United States.

If we think only in terms of “automation”, we imagine technology simply making what already existed faster. If we understand that it is “autonomization”, we see that what is changing is who decides what happens.

At that point, the questions become serious. What data are these systems learning from. Who chooses the objectives they have to optimize. Who takes responsibility when a decision causes harm.

Words are not a detail here. If we keep talking only about automation, we tell ourselves a tidy world that no longer exists. AI enters processes and moves autonomy, decision power, and responsibility inside the machines. Using the right term, autonomization, means looking this shift of power straight in the eyes, before it is too late.

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What to do in 2026 to survive artificial intelligence

Learn to use AI for everyday work, not for entertainment. Writing, summaries, document prep, email analysis, presentation drafts. Use AI as a first step, not the final one. Let it draft, keep control yourself. Remember that AI processes your data and you do not know where it ends up.

At work, document results. Show that AI helps you do more in less time. Automate repetitive tasks and keep decisions human. If you have a manager, explain clearly what AI does and what you do. This protects your role. Update your resume with real AI tools you actually use, not generic buzzwords.

Security matters more because AI makes scams easier. Turn on two factor authentication for email, banking, Apple ID or Google, and social accounts. Use a password manager and different passwords. Enable automatic backups and test recovery.

Set alerts on bank accounts and cards, here in the United States. Keep two payment methods ready. Use a family code word for emergencies. Call back only numbers already saved. Stop any urgency. No codes, no transfers during a call or video.

This series is called Artificial Decisions for a reason. In 2026 many decisions will be made by automated systems. Staying human means knowing how to use them, not ignoring them.

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10 digital things to fix today so you don’t pay for them all year

January 1st is the right day to fix what will cost us time, money, and control over the next twelve months.
Because digital systems degrade when we leave them on autopilot.
January 1st is the only day when fixing them costs little. From January 2nd onward, it always costs more.

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My predictions for 2026

In 2026, humanoid work robots will arrive in real settings: warehouses, logistics, factories, large facilities. They will look intelligent because they learn from human behavior, just like today’s AI learns from human texts. They will repeat physical actions, pick up objects, move them, sort them, load them. Companies will cut time and shifts. Pressure will rise on wages and on the pace of operational jobs.

In 2026, more daily decisions will be made by automated systems. Credit, insurance, rentals, spending limits. We will receive outcomes: approved, rejected, review needed. Families will plan with less certainty. Decisions will arrive more often without a useful explanation.

In 2026, money will be blocked more often by automatic checks. Triggers will include changing city, phone, SIM, address, or making unusual purchases. Systems will rely on complex models, not simple rules that can be changed quickly. Support will say more often, “I can’t intervene, the system decided.” Unlock times will get longer. The burden will fall on the user.

In 2026, AI will be built into everything: email, messages, photos, documents, customer support. It will write and reply for us. We will check less. A small error will affect a payment, an appointment, a case, a job choice. Texts will be cleaner. Decisions more automatic.

In 2026, scams will be more targeted and live. Phishing will move to real time calls, even video. On the other side there will be a family member, or something that looks like one, generated by AI in real time. Voice, face, reactions. Requests for money or codes will arrive while we are talking. Trust inside the family will be used as leverage.

In 2026, office work will compress. Fewer middle roles. More tasks per person. Customer care, marketing, administration. Companies will use more freelancers and project contracts. With AI as justification, layoffs will be easier. Work will be more fragmented. Fewer protections. More pressure on those who remain.

In 2026, the first to be replaced will not be the least skilled. They will be those who do not use AI. Refusing tools will be seen as slower, more expensive, less flexible. Using them poorly will bring control. Using them well will keep you employed.

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The three warning signs nobody talks about

We live immersed in technology and we tend to think risks come from robots. In reality the most important signals are already here and they concern everyone. Follow me to the end and I will explain why these three warning signs affect attention, freedom, and even energy.

The first warning sign is “cultural Alzheimer’s”, meaning the loss of focus. We no longer plan long term, we live in very short cycles. Data from the University of California, Irvine shows a drastic drop in our attention span, from two and a half minutes in 2004 to forty seven seconds today. This change weighs on school, work, and everyday life. Here in the United States many teachers report students who cannot stay on one task for more than a few moments. In Italy psychologists and educators see the same effect in families. If we can no longer think in perspective, we lose our ability to build the future.

The second warning sign concerns the end of choice. Algorithms select what we see, who we follow, and what moves us. Platforms study our behavior and use it to decide in our place. There is no need for science fiction scenarios. It happens while we scroll a feed or open a video. In the United States studies show how extreme personalization pushes different groups of citizens to live in separate information realities. And when AI constantly corrects our decisions, free will becomes fragile. This is a concrete problem for families, for young people, and for those who work with data.

The third warning sign is energy. Every AI request consumes far more resources than a traditional web search. A question to ChatGPT can require up to ten times the energy of a Google query. In the United States utilities are already studying the impact of data centers on power grids. In Europe operators report that demand is growing faster than supply. The point is simple. Those who control energy will also control intelligence. Without energy autonomy there is no technological autonomy.

These three signals tell us something clear. The future depends on how we manage attention, freedom, and resources. What do you think, do you already see this in your daily life, in your family, at school, or at work? Tell me in the comments, because your examples also help others recognize these warning signs. And if you find this useful, share the video with someone you care about.

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Humanoid robots need new rules

Humanoid robots are entering warehouses and factories, and soon homes and hospitals. Society is not ready, and the risks are concrete.

In the United States they already move heavy loads. They stay upright only through power and active balancing. If power cuts, they can fall. A 65 kg fall in a corridor can injure someone. That is why “slow stop” behaviors matter: slow down, put the load down, lower the body, then shut off. Standards bodies like IEEE and ISO are working on rules for actively balanced robots.

We also need shared signals, like traffic rules: lights, movements, clear cues. In noisy workplaces voice is useless, and with many robots around, people must understand fast what each one is doing. A humanoid shape also creates false expectations of empathy and social intelligence, which increases confusion and risk, especially with children and older adults.

AI errors will happen. If a robot misreads a gesture or misses an obstacle, liability must be immediate. A simple fix should be mandatory: a visible ID plate, like a car license plate, linking that robot to maker, model, software version, and the operator responsible.

Minimum line: safe stopping, standard signals, always-on human override, honest design, visible ID.

#ArtificialDecisions #MCC

✅ This video is brought to you by: https://www.ethicsprofile.ai

👉 Important note: We’re planning the upcoming months.
If you’d like to request my presence as a speaker at your event, please contact my team at: management@camisanicalzolari.com