Who Gets Credit for Words Anymore?
As AI blurs traditional signals of authorship, accusations of “that sounds like AI” are replacing real engagement. This post explores what responsibility looks like now for both writers and readers in a world where tools are everywhere.
Writing, Authorship, and Assumptions in the Age of AI
Not long ago, we relied on familiar signals to judge writing. Clear writing suggested clear thinking. Polished writing suggested experience. Confidence implied expertise.
Those signals are breaking down.
Today, it’s common to hear, “That sounds like AI.” Sometimes it’s a neutral comment. More often, it’s a dismissal. And in many cases, it replaces real engagement with what was actually said.
Large language models like ChatGPT have made authorship harder to judge. In the past, tone and structure helped us guess where words came from. Now those same qualities can raise suspicion. Clarity is treated as artificial. Confidence is treated as outsourced.
When authorship isn’t obvious, people often default to assumptions instead of evaluation. Instead of asking whether an idea is accurate or useful, we focus on how it was produced and whether it “counts.” That shift shuts conversations down instead of opening them up.
There is also a real disagreement underneath these reactions. Some people believe readers should judge ideas on their merits, no matter how someone wrote them. Others believe the method matters, that how someone writes shapes meaning and trust. That concern is valid. Methods do shape outcomes.
The problem begins when that concern turns into an assumption. When we skip the idea itself and jump straight to guessing the tool, we lose the chance to evaluate either the idea or the method well. Ethical engagement requires holding both truths at once: methods matter, and ideas still deserve careful examination.
Tools have always shaped writing. Spell check, grammar tools, editors, and style guides have influenced how words appear on the page for decades. What has changed is how quickly we accept suggestions from tools without stopping to evaluate them.
AI isn’t going away. The ethical challenge isn’t deciding whether someone used a tool, but deciding how people handle responsibility.
Authorship has always meant ownership. Writers are responsible for the ideas they publish, regardless of the tools involved in shaping them.
Even when it isn’t clear who wrote something or how it was produced, readers have the responsibility to read carefully, evaluate ideas on their merits, and respond to what is actually being said.
That’s the conversation worth having now: what it means to write in good faith and to read in good faith in a world where tools are everywhere.
What AI Needs from You to Be Helpful
AI isn’t unpredictable. It responds to what you give it. Learn why clarity, context, and goals are essential for getting useful results from AI tools.
People often describe AI as “unpredictable,” “confident but wrong,” or “hit-or-miss.” In reality, most disappointing AI outputs trace back to the same issue: the AI wasn’t given enough to work with.
AI tools don’t think, reason, or understand in the human sense. They respond to what you provide. When what you provide is vague, incomplete, or misaligned, the output reflects that.
Helpful AI starts with three things: clarity, context, and goals.
Clarity: Say What You Actually Mean
AI doesn’t infer intent the way people do. It doesn’t “read between the lines,” pick up on tone, or fill in missing assumptions. You need to make them explicit.
When a request is broad, rushed, or ambiguous, the AI has to guess. Those guesses are based on patterns, not understanding. That’s why vague requests often produce generic or off-target responses.
Clarity isn’t about length. It’s about precision. The more clearly you articulate what you’re asking for, the less guesswork the AI has to do and the more useful the response becomes.
Context: Don’t Assume the Background Is Obvious
Humans automatically carry shared context into conversations. AI doesn’t.
If you don’t explain who something is for, why you need it, or what constraints matter, the AI fills those gaps with defaults. Those defaults may not align with your situation, audience, or standards.
Context gives the AI a frame of reference. It defines the problem's boundaries and helps the output align with your real-world needs rather than a generic version of those needs.
Goals: Know What “Good” Looks Like
One of the most common sources of frustration is asking AI to “help” without defining what success means.
Are you trying to decide, draft, brainstorm, summarize, or refine? Is the goal speed, accuracy, tone, persuasion, or exploration? Without a goal, the AI produces something, but not necessarily something useful.
Goals act like a destination. They guide the response toward an outcome instead of just generating information.
The Bigger Idea
AI isn’t a replacement for thinking. It’s a collaborator that depends on the quality of input.
When people say AI “isn’t helpful,” what they’re often experiencing is a mismatch between what they expect and what they’ve provided. The tool isn’t failing; it’s responding exactly to the information it has.
The clearer, more context-rich, and more goal-oriented you are in the interaction, the more helpful AI becomes. Not because the AI is smarter—but because you’ve made it possible for it to work with you, not around you.
Common Misconceptions About AI That Make It Harder to Use
Before AI can be truly helpful, it’s important to understand what it isn’t. This post clears up the most common misconceptions so you can use AI more effectively.
A lot of the confusion around AI isn’t about the technology itself; it’s about the myths that quietly shape how people expect it to behave. When those expectations don’t match reality, the results can feel disappointing or unpredictable. Here are some of the most common misconceptions that can make AI frustrating.
Myth 1: “AI works like Google.”
Many people open an AI tool expecting it to “look things up” the way a search engine does. But AI isn’t retrieving information—it’s generating responses based on the instructions you provide. When you treat it like a search bar, you naturally write short, vague prompts, and the results end up the same. Clear direction leads to far better output.
Myth 2: “AI makes things up.”
When AI gives an incorrect detail, it’s easy to assume it’s fabricating information. What’s really happening is that the AI is filling in gaps because the prompt didn’t give enough clarity, context, or constraints. Without the proper setup, it will try to complete the answer as best it can, even if that means drifting away from what you intended.
Myth 3: “You can’t trust anything AI says.”
This misconception usually arises from a single bad answer. But AI isn’t meant to be blindly trusted or automatically dismissed. With a well-structured prompt and a quick verification step, AI becomes a dependable tool for drafting, brainstorming, planning, and problem-solving. Reliability comes from how you guide it.
Myth 4: “AI knows everything.”
This belief often shows up as the opposite reaction to the idea that AI “makes things up.” If AI can produce detailed answers so quickly, it’s easy to assume it must have a built-in database of facts it can tap into at any moment. But that’s not what’s happening. AI isn’t accessing a vault of information or looking up correct answers behind the scenes. It generates responses based on language patterns, which means it still needs your context to remain accurate and relevant.
Myth 5: “AI remembers everything from past chats.”
People often assume AI tools keep a running memory of every conversation. In most cases, they don’t. AI only works with the information in your current chat, so if something matters, it’s worth restating. When you provide the whole picture, the AI can give you a much stronger, more coherent response.
Once you clear away these misconceptions, AI feels less mysterious and more useful. The moment you understand how AI produces each word of an answer, these myths start to make sense in a new way. If you’d like a quick, visual overview of that process, my “How LLMs Work” infographic walks you through it without the technical jargon. You can download it here.
Smarter Prompts ᐧ Better Results ᐧ No Jargon
AI Isn’t Search: How to Get Better Results With Smarter Prompts
AI isn’t search, and treating it like Google is one of the biggest reasons people feel confused or disappointed with tools like ChatGPT, Claude, or Gemini. AI doesn’t retrieve information — it generates new text based on what you ask. Once you understand that shift, writing clearer prompts becomes much easier. This post explains the difference and how it transforms your results.
AI Works Differently Than Google
Most people open ChatGPT, Claude, or Gemini expecting them to behave like Google. You type something in, hit enter, and hope it “finds” the right answer.
But AI doesn’t work that way, and that mismatch is the source of so much frustration.
Search retrieves.
AI generates. It creates new text based on patterns it has learned, not by pulling information from a database.
Why This Shift Matters for Your Prompts
When you treat AI like a search box, you naturally write short, keyword-heavy questions. But those don’t give AI much to work with, and the result is often something vague, generic, or completely off-base.
When you treat AI like a collaborator, the conversation changes. The tools make far more sense, the answers are more useful, and you don’t feel like you’re guessing at how to phrase things. You’re not “looking something up.” You’re asking the model to create something new based on what you want.
Understanding that shift is the foundation of getting better results, and it removes the pressure to “figure out” some mysterious prompting trick. It’s simply a different kind of interaction than most of us are used to.
And once you see that difference, everything else becomes easier.
Ready to Get Better Results With AI?
If you’re ready to feel confident using AI in your everyday work and life, my Prompt Smarter course shows you how to turn vague prompts into useful, reliable results. It’s practical, jargon-free, and built for real people.