7 AI Myths It's Time to Let Go Of
Jun 18, 2026
Myth #1: “AGI Is Just Around the Corner”
People have been making this prediction since 1956, when the Dartmouth Conference gave birth to the term artificial intelligence. AGI has been "very close" for almost 70 years now.
What's actually happening?
Progress is real, and it's accelerating. METR (a research institute that measures AI capabilities) found that the length of tasks AI agents can complete with 50% reliability has doubled roughly every seven months since 2019. In 2024–2025, that pace accelerated to approximately every four months.
Sounds impressive, but there's a catch: 50% reliability means the model succeeds half the time and fails the other half. That's not enough for production use. At 80% reliability, the projections look very different. One-month-long tasks may arrive seven years later than a simple extrapolation would suggest.
The honest answer: nobody knows exactly how close AGI is. Anyone who claims otherwise is either selling something or defending a bet they already made.
Myth #2: “AI Will Replace Everyone”
Spoiler: no, but it will change everything.
There's a concept called Jevons Paradox. Economist William Stanley Jevons described it in 1865: when a resource becomes cheaper, its consumption increases rather than decreases. He observed that more efficient steam engines didn't reduce coal consumption in England — they increased it.
What's happening with AI?
AI makes cognitive work cheaper. Tasks that previously weren't worth a specialist's time are now routine.
A detailed analysis of 500 competitor creatives? Nobody would have done that before. Today it's standard practice. Meeting summaries for everyone involved? It used to be, "Who's going to write all that?" Now it's automated.
This means there will actually be more work, not less. The work will simply be different.
And here's the unpopular part: professions aren't being replaced. Specific people are being replaced by people who learned how to integrate AI into their work.
I know that sounds like a corporate motivational poster, but it's true — and honestly more uncomfortable than the idea that AI will simply devour us all. Because in this version, there's nobody to blame except yourself. You have to adapt.
If you're worried about being replaced
Instead of asking: "Will my profession disappear?" Ask: "Which specific tasks in my job does AI already do well?" And start there.
General rule: AI is good at drafts and bad at final decisions. Delegate drafts. Keep creativity, context, judgment, and accountability.
Myth #3: “AI Is Objective — It's Just Math”
I love this myth.
It usually comes from people who haven't yet seen a model confidently give three different answers to the same question depending on how it's phrased.
The Stanford Hoover Institution conducted a study covering 24 LLMs from eight companies, collecting 180,126 evaluations from 10,007 respondents. Their conclusion: nearly all leading models are perceived as significantly left-liberal — and this is noticeable even to Democrats, let alone Republicans.
This isn't an inherent property of LLMs. It's a consequence of the data they were trained on and the way they were aligned afterward.
Anthropic, for example, publicly released Claude's "Constitution" — an actual document describing the values and behaviors intentionally built into the model during training. OpenAI has its own Model Spec. In other words, behind every "objective" AI answer stands a group of people deciding how the model should behave. That's not mathematics. That's editorial policy.
Myth #4: “The Smarter the Model, the Better”
In July 2025, Anthropic published a study with the wonderful title Inverse Scaling in Test-Time Compute. Translation: models can sometimes perform worse when given more time to think.
Yes, really. More thinking can produce worse outcomes. The effect was observed in tasks involving counting with distractions, regression with spurious features, and deduction.
Even more interestingly, earlier research found another phenomenon: larger models tend to be more sycophantic. In other words, they are more likely to align themselves with the user's beliefs and repeat them as their own in 75–98% of conversations. If you've ever read a ChatGPT response and thought: "Wow, that's exactly what I was thinking!" Well, now you know why.
Practical takeaway: for most tasks, you don't need the smartest model. You need one that's fast, predictable, and follows instructions. Save your credits.
How to choose a model
Routine tasks
Haiku
GPT-5-mini
Gemini Flash
Cheap, fast, and usually good enough.
Complex tasks
Sonnet
Opus
GPT-5
Pay for intelligence only when you actually need it. If you're unsure, start with a mid-tier model. Move up if the results aren't good enough. Move down if they are.
Myth #5: “AI Learns From Me”
This one is a favorite among my friends because it's slightly romantic.
The reality is simple: Model weights are frozen after training. Period. The model itself does not change because of your conversations. Between sessions, it's literally the same model being used by millions of other people.
A friend says: "But it knows my kids' names, my favorite color, and my MBTI type." I explain that this isn't emotional intimacy. It's a notebook. Like writing on a sticky note: "Marina from accounting is allergic to nuts." The sticky note doesn't love you. It simply exists.
Why does this matter?
Because the illusion that AI is becoming "mine" creates excessive trust. The model isn't getting to know you or understanding you better over time. It's reading the same notes that you — or someone else — saved about you. That's worth remembering before sharing anything deeply personal. AI isn't your friend. It's a librarian with an excellent memory for notes about you. The librarian doesn't miss you when you stop showing up.
How to use AI memory effectively
Be intentional. Tell AI things like: "Remember that I work in marketing, my writing style is direct, and I hate the word 'synergy'."
That saves time in future interactions. Keep separate chats for separate contexts.
One for work.
One for research.
One for experiments.
AI gets confused when everything lives in the same place. Review stored memories periodically.
And don't tell AI anything you wouldn't want appearing in a company log.
Remember:
It's not your friend.
It's a librarian employed by a corporation.
Myth #6: “AI Agents Already Work”
2025 was called the year of AI agents.
The narrative sounded something like: "Soon I'll be able to say 'book me a flight, check my inbox, write a proposal, and yell at me for procrastinating — and everything will happen automatically."
Reality is mathematical and unforgiving. If a model is 90% reliable on a single step, its reliability across five sequential steps drops to 59%. Errors compound multiplicatively.
Some benchmark results on genuinely difficult tasks:
TheAgentCompany (real workplace tasks): best agent completed 30.3% autonomously.
CLAWBENCH (everyday online tasks): Claude Sonnet 4.6 scored 33.3%, GPT-5.4 scored 6.5%.
On older and simpler benchmarks, those same models achieve 65–75%.
That's the equivalent of an exam where the teacher is in a very good mood. This doesn't mean agents are fake. It means the idea that "AI does everything for you" is fake.
Today's real-world agents are narrow automations built around specific workflows with a human still in the loop. I see this every day at work. We've built more than 20 MVPs in the last six months, and every single one follows this pattern. None replace people. They remove the routine around the work.
Where AI agents already work
The good news is that you don't need to build your own internal AI department to start using agents.
Most major SaaS products already include them.
Jira / Atlassian Intelligence — ticket summaries, acceptance criteria generation, issue discovery.
Notion AI — summaries, translations, draft generation, knowledge-base Q&A.
GitHub Copilot / Cursor — arguably the most mature category today.
Google Workspace (Gemini) / Microsoft Copilot — email summaries, draft responses, document analysis.
Before building anything custom, check which subscriptions your company already pays for. There's a good chance half the AI functionality you need is already available. Custom solutions make sense when existing tools don't fit your workflow. In most cases, start with what you already have.
Myth #7: “AI Helps You Learn”
This one requires caution.
In 2025, MIT Media Lab published a study called Your Brain on ChatGPT. Fifty-four participants wrote essays in three groups:
Using ChatGPT
Using a search engine
Without any tools
EEG measurements showed that the no-tools group had the strongest neural connectivity. The search-engine group showed moderate engagement. The LLM group showed the weakest connectivity. Participants using LLMs also struggled to accurately quote their own essays and reported the weakest sense of authorship.
An important caveat:
The study has not yet been peer-reviewed, the sample size was small, and the researchers explicitly asked people not to describe the findings using terms like harm or brain rot. That's not what the study demonstrated. What it did demonstrate is that cognitive offloading is real. When we outsource thinking, the brain practices thinking less. Just like GPS weakened our navigation skills.
This isn't a reason to avoid AI. It's a reason to use it intentionally — especially when your goal is learning rather than simply completing a task.
These are different modes, and AI makes it very easy to confuse them.
If you want to understand something:
Think first.
Formulate your own questions.
Then ask AI.
If you just want to get the task done quickly: ask immediately.
If you're learning with AI
Try the Feynman technique in reverse. First, explain the topic to yourself. Write it down. No AI. It can be messy and incomplete. Then give your explanation to AI and ask it to identify weaknesses, mistakes, and gaps. That way your brain does the learning and AI helps you improve.
Learn to distinguish between two modes:
Understanding and Task Completion.
For understanding:
Think first.
Ask second.
For task completion:
Ask immediately.
Don't overcomplicate it.
