Myth 1: AI is a Master Coder
This is false. AI doesn’t truly “code” because it doesn’t understand. It guesses. When a human developer writes code, they build a mental model and a deep understanding of the system. An LLM, in contrast, simply looks at the prompt and context you provide and predicts the most statistically likely sequence of characters to follow.
The value of hiring a programmer isn’t just the raw output. It’s the thought process, the architectural decisions, and the maintainable structure they create.
Furthermore, the idea that instant code generation at a massive scale is a strength is a misconception. It’s a weakness. Veteran programmers like Bill Gates have long understood that measuring programming progress by lines of code is like measuring aircraft construction by weight. It’s completely backward. The productivity gained from AI generating vast amounts of code vanishes the moment you have to review and debug it. In fact, a recent study showed that 66% of developers report spending more time fixing “almost correct” AI-generated code than if they had written it themselves.
Myth 2: Exponential Growth and Inevitable Replacement
Technology doesn’t always get better exponentially. Just look at your calculator; its core functionality hasn’t changed in decades. Yet, AI boosters insist that current performance is merely an engineering problem waiting to be solved.
Recent developments suggest otherwise. We’ve seen negative surprises, where new model releases expose the gap between AI’s ambition and its reality. LLMs appear to be reaching a plateau.
Consider this data point:
- Model A (7 Billion Parameters): Achieves a certain accuracy score.
- Model B (72 Billion Parameters): Despite a 10x increase in parameters, it only achieves a 1% accuracy increase over Model A.
This indicates that AI performance isn’t just an engineering problem; it’s a mathematical one. You cannot solve it by simply throwing more parameters at the model. Proponents will talk about context engineering, agent harnesses, and scaffolding, but these introduce their own issues.
Long context actually increases the failure rate. Several studies show that less is more:
- Reducing the number of tools available to an LLM significantly improves its function-calling capabilities.
- LLMs get lost in multi-turn conversations; the shorter the context, the better the solution.
Even people building AI agents are betting against them. They’ve seen firsthand that the longer a conversation gets, the more you pay for the agent and the worse the results become. An over-engineered, unstable system is still, at its core, an unstable system.
Myth 3: “You Just Don’t Know How to Use It”
This is a classic case of gaslighting—blaming the user instead of acknowledging the tool’s flaws. The truth is, there are no profound “AI skills.” AI tools are designed to be incredibly intuitive and simple. Most so-called AI skills can be learned in a few minutes.
An expert at AI prompting with zero domain expertise is useless. They cannot verify if the quality of the AI’s output is good, whether the topic is coding, cooking, or medicine. This is precisely why the most effective developers working with AI are already senior experts in their field. They have the knowledge to validate the output.
Myth 4: The AI-Powered Developer Will Replace You
An experiment conducted in an MIT Fortran class provides a powerful counter-narrative. The class was split into three groups to solve a problem:
- Group 1: Used ChatGPT.
- Group 2: Used Meta’s Llama model.
- Group 3: Used only Google search.
Which group was fastest? As you might guess, the ChatGPT users finished first, followed by the Llama users, with the Google group being the slowest.
But here’s the crucial part: which group passed the final exam on the task?
- ChatGPT Users: 0% pass rate. Everyone failed.
- Llama Users: 50% pass rate.
- Google Users: 100% pass rate.
The lesson is clear. Developers who use AI excessively without engaging their own problem-solving skills are slowly making themselves obsolete.
Myth 5: AI is Killing Junior and Mid-Level Jobs
This myth gained traction after a study claimed generative AI has a “seniority bias.” The media quickly jumped on this, painting a picture of an AI-fueled job apocalypse for entry-level workers.
The reality is different. The junior hiring crash began in 2022, well before most people knew what prompt engineering was. It coincided with the Fed jacking up interest rates from near-zero to 5.25%. The preference for hiring senior engineers during an economic slowdown is a known phenomenon that has occurred since the 1980s. When the economy tightens, junior positions are the first to take a hit. It has to do with microeconomics, not AI.
Note for recent graduates: If you’re struggling to find a role, look for jobs adjacent to software development, such as Salesforce administration or Shopify development. Find shelter in these areas until the good times return.
Myth 6: Senior Engineers Are Just Resisting Change
The narrative suggests senior engineers are dinosaurs screaming at the approaching asteroid, unwilling to adapt because they love typing code by hand. This is a fabricated idea.
The data shows that senior developers are the ones using AI the most. They are shipping more code and are the least afraid because they are confident in their expertise. Their skepticism isn’t born from fear, but from experience.
Here’s why they are cautious:
- AI Generates Technical Debt: They know what it’s like to be on a team with three juniors pushing hundreds of lines of un-reviewed, AI-generated code. The senior is the one who has to stay late cleaning up the mess when the application breaks.
- AI Creates More Work: Debugging, code-reviewing, and managing the fallout from faulty AI output is time-consuming.
- Context Engineering is Inefficient: For a senior with deep knowledge of a codebase, it’s often faster to think about the problem and fix it directly than to spend minutes crafting the perfect prompt for a chatbot, only to get a flawed answer. You can see this in countless AI demos where the presenter says, “Oh, it made a mistake, but for the sake of this article, let’s pretend it got it right.” That’s a 20-minute waste that could have been a 3-minute fix.
- AI Creates Weaker Developers: It fosters a generation of programmers who can’t truly code or problem-solve independently.
- The Technology is Unreliable: It cannot be relied upon for critical tasks and often makes you less productive.
Myth 7: It’s Not a Bubble, It’s Infrastructure
The final myth is that AI isn’t a bubble because companies have revenue and are building the infrastructure of the future. While some revenue exists, much of it is part of an “infinite money glitch,” or secular financing. For example, OpenAI buys chips from Nvidia, and Nvidia, in turn, invests in OpenAI.
The financial situation is precarious. OpenAI’s partners are carrying billions in debt, and it’s estimated the company needs to raise around $500 billion in the next four years just to stay alive. That figure is more than the projected available supply of US venture capital and the combined capital of the top ten private equity funds.
As for the “infrastructure” argument, it falls apart under scrutiny. The average useful lifespan of an Nvidia GPU in a hyperscale data center is estimated to be just one to three years. This isn’t a long-term investment; it’s a short-term, high-burn-rate cycle. This is a bubble.
My advice for you as a software engineer is to be cautious. Avoid working at companies with huge exposure to AI, especially startups, as they will be the first to get cut when the bubble bursts.
In the meantime, you have to play the game. If your company’s leadership is singing the AI song, just sing along. Say, “Yes, I’m using AI,” and then go and solve the problem in the most effective way you see fit.