No stupid questions.

AI is a collective term for systems that perform tasks we normally associate with human intelligence. There are many types: image recognition, recommendation systems, speech recognition — and the large language models (Claude, ChatGPT, Gemini) that you probably think of when you hear the word "AI" today.

A language model is a program trained on massive amounts of text that has learned to predict which word comes next. With enough text and computing power, such a model can write, summarize, code and solve tasks in several steps.

AI has no consciousness and no goals of its own. It follows what you write, which is why it pays to learn the tool well.

No — not in the sense we mean when we talk about human thinking. A language model simulates thought processes by predicting likely text based on training data and your prompt.

But it's a bit more complicated. The models exhibit capabilities that resemble reasoning — they can solve problems they've never seen before, draw conclusions from new information, and change their minds when presented with counterevidence. Whether this is "thinking" or advanced pattern matching is an open question that philosophers and AI researchers are actively debating.

The practical answer: treat AI as an extremely capable tool, not as a colleague with its own opinions and interests.

A language model always generates the next likely token — regardless of whether it actually "knows" the answer or not. It has no built-in way to say "I don't know" unless it has been explicitly trained on that behavior.

The result: the model fills gaps in its knowledge with plausible-sounding text. It invents research papers, incorrect statistics, events that never happened, and quotes that were never said.

The solution: always verify facts that are relevant for decision-making. Use AI for reasoning, phrasing, and structuring — not as a primary source for factual claims.

Probably more than you think — but not from your conversations (unless you have Memory enabled). The models are trained on vast amounts of public internet text up to a certain date. If you have written publicly online, it is possible that the text you produced is included in the training data.

Your conversations: ChatGPT Free, Plus and Pro use your chats for training by default unless you turn off "Improve the model for everyone". On Claude's consumer plans, training depends on the "Help improve Claude" setting. Check the setting in your account.

It depends on which service and how. The basic rules:

• Don't do: Do not paste personal identity numbers, patient data, customer details, salaries, or other sensitive personal information into the free versions of AI tools.
• Proceed with caution: Business plans (ChatGPT Business or Enterprise, Claude Team or Enterprise) do not train on your data by default and come with a data processing agreement (DPA). Use those, not personal Pro plans, if you process personal data on behalf of customers.
• Okay for most: Using AI to write general texts, analyze public information, summarize non-sensitive documents.

The short answer: it depends on what you plan to do.

• For writing, analysis and Swedish business texts: Claude Pro
• For all-in-one with image generation and web search: ChatGPT Plus
• For research with citations: Perplexity
• If you already use Google Workspace: try Gemini first
• For real-time events on X/Twitter: Grok

Daniel's daily driver: Claude Pro as primary, ChatGPT Plus as a supplement for images and web search.

Claude Free: Free, with usage limits and without the Opus models.

Claude Pro ($20 a month billed monthly, before tax): at least five times the usage of Free, but still with limits, access to Claude Opus and Sonnet, long documents, file uploads, and Projects to organize work.

For serious use at work: Pro is worth it. Calculate it as about $1 per workday.

Almost — but not quite. The models are primarily trained on English text (dominant on the internet) and Swedish is a small language in that context.

Claude, ChatGPT, and Gemini handle Swedish excellently for most tasks. You notice the difference in:

• Nuanced formal texts: Claude is clearly stronger
• Dialect, irony, Swedish cultural references: can get a bit clunky
• Technical terms with Swedish equivalents: works well if you are consistent

Daniel's observation: Claude understands Swedish business context better than its competitors. That was one of the reasons for the switch from Gemini.

For private, occasional use: free is more than enough.

For regular professional use: calculate the value. If a tool saves you one hour per day and your hour is worth $50, then $20 a month pays for itself.

Start with one tool and learn it well before you pay for more.

You can only know by checking the answer. Treat AI responses like advice from a smart friend without specialized training in the subject: interesting, often right, but requiring verification for important decisions.

• Factual claims with numbers: always verify
• Legal or medical advice: always supplement with an expert
• Reasoning and structuring: lower risk, but read it through before you use the result
• Code: always test before deploying

It depends on the context and who sets the rules.

In school: many educational institutions have policies regarding AI use — follow them.

At work: if you deliver a result and AI helped you get there — most employers care about the result, not the process. Be transparent if asked.

In creative creation: a discussion without a clear answer. Tools change what is possible.

Daniel's position: AI is a tool. Using a powerful tool skillfully is a skill, not cheating. The important thing is that you understand what you are producing and can stand behind it.

For two reasons:

Temperature — a setting that controls how "creative" the model is. A bit of randomness is built in by design to ensure responses are varied and natural rather than robotic.

Phrasing — AI is extremely sensitive to how you phrase your question. The same question with slightly different word choices can yield noticeably different answers. That is why clear wording gives better answers.

Only during the current conversation — unless you activate the Memory feature (available in Claude and ChatGPT). Without the memory feature, every new conversation starts from scratch.

This means: if you want the AI to understand your context, your company, or your writing style — you either need to tell it in every conversation, use Memory features, or build a system prompt that includes the context automatically.

Claude Free/Pro: Saved to show in your history. Whether chats are used for training depends on the "Help improve Claude" setting under Settings → Privacy.

ChatGPT Free/Plus: Saved in history. Not used for training if you turn off "Improve the model for everyone" in the settings.

API (direct integration): Anthropic and OpenAI log API calls for a limited time for security/abuse detection, but do not train on them by default.

Recommendation: go into the settings of your AI service and verify what applies to your subscription.

Yes, to some extent. Training large models requires enormous amounts of electricity and generates significant CO2 emissions — training a single frontier model uses a lot of electricity.

Inference (using the model) is energy-intensive, but the cost per query is small compared to the training.

Counterargument: AI can contribute to efficiencies that reduce total energy consumption in other sectors. The net picture is unclear and is actively debated.

The EU AI Act is the EU's law regulating AI based on risk level. It affects you if you run a company that uses AI.

What you probably don't need to do: Anything special to use Claude, ChatGPT, or Gemini for common office tasks — emails, texts, analyses. These are GPAI (General Purpose AI) and are not classified as high-risk.

What you should do: Ensure your staff has basic AI literacy (Article 4, applicable from February 2025). The Act doesn't set a format; what counts is that staff understand the tools they use and their risks.

What requires more: If you use AI for recruitment, credit decisions, medical assessments, or similar — then it is high-risk and requires documentation, risk assessment, and human oversight.

High-risk rules apply from 2 December 2027 for Annex III uses such as recruitment and credit, and from 2 August 2028 for AI in regulated products (Annex I). Transparency rules (Article 50) have applied since 2 August 2026.

Not legally mandatory (yet) — but strongly recommended.

A simple AI policy (one page is enough) should cover:

• Which AI tools are approved at the company
• What can and cannot be sent to AI tools (personal data, etc.)
• How AI-generated content should be labeled/handled
• Who is responsible for AI-related decisions

It takes an hour to write. Polaris has a template in the prompt library (FO-08).

Automation does exactly what you programmed it to do — always, every time, without deviation. An Excel macro is automation. A Zapier integration is automation.

AI can handle unstructured input, make judgments, and manage situations it has never seen before. It is flexible but unreliable in a different way than automation — it can make "creative" mistakes.

The most powerful approach is to combine them: automation for the deterministic, AI for what requires judgment.

Yes. Writing code is one of the most common uses. Claude, ChatGPT, and Gemini can all write, explain, debug, and refactor code. Claude Code (the agent tool) can do so autonomously in several steps.

Limitations: AI-written code needs to be reviewed. It may contain security issues, be inefficient, or solve the wrong problem. The more you understand code, the more effectively you can use AI to write it.

As a starting point for understanding — yes. As a substitute for legal advice — no.

AI can explain what a contract clause likely means, summarize a law, or identify questions you should ask a lawyer. That is valuable.

But AI may have outdated information, miss jurisdiction-specific nuances, and bears no professional responsibility for its advice. Use AI to understand the question, and ask a lawyer before you decide.

Nobody knows for sure. But some trends seem clear:

• Agents are becoming mainstream — AI that doesn't just answer but acts autonomously in complex environments.
• Multimodality continues — text, image, audio, video in one and the same model.
• Local execution is becoming more accessible — models running on your phone or laptop without internet.
• Prices are falling — the price per token has fallen sharply in recent years.
• Regulation is increasing: more countries are writing AI laws after the EU AI Act.

What doesn't change: you still need to understand what you are asking for, verify what you receive, and take responsibility for what you publish.

In broad terms:

• Pre-training: The model is fed with massive amounts of text (webpages, books, code, articles) and learns to predict the next token. Extremely expensive and energy-intensive.
• Fine-tuning: The model is adjusted on more specific data to become good at interpreting instructions.
• RLHF (Reinforcement Learning from Human Feedback): Human reviewers rate the model's responses. The model learns to generate answers similar to those that received high ratings.
• Constitutional AI (Anthropic's method): The model is trained with principles and learns to evaluate its own responses.

The result: a model that is good at following instructions, being helpful, and avoiding harmful responses.