TL;DR
Writer Antonin argues that while large language models (LLMs) are useful tools, overreliance on them risks eroding engineers' problem-solving abilities. The piece calls for balancing AI-assisted workflows with deliberate practice of foundational reasoning and sustained focus.
What happened
In a Feb. 11, 2025 essay, author Antonin cautioned that the recent embrace of large language models (LLMs) by software engineers may have unintended consequences. He acknowledges that LLMs can automate repetitive tasks, generate code snippets, assist debugging and brainstorming, and free engineers’ time. But Antonin warns these models can hallucinate, show inconsistencies, and reflect biases present in their training data, so their outputs require careful review. Because LLMs often supply ready-made solutions for known problems, engineers may default to accepting answers without understanding the underlying reasoning. That pattern, he argues, can erode mastery of foundational skills and the capacity to solve novel problems. Antonin contrasts LLMs with traditional search engines, saying search results encourage exploration as well as exploitation, while LLMs tend to promote immediate exploitation. He frames sustained focus—practiced attention to the “why” behind solutions—as the skill the field must preserve.
Why it matters
- Overreliance on LLM outputs could reduce engineers’ ability to solve unprecedented or complex problems.
- LLM limitations—hallucinations, inconsistencies and biases—make human review and understanding essential.
- Loss of foundational skills would shift problem-solving authority away from human engineers and toward tools.
- Maintaining a balance between tool use and deliberate practice of reasoning preserves technical mastery.
Key facts
- The essay was published Feb. 11, 2025 and authored by Antonin.
- Antonin affirms that LLMs are powerful and beneficial for automating repetitive engineering tasks.
- LLMs can hallucinate, exhibit inconsistencies (especially in self-reflection scenarios), and contain biases.
- Training data for LLMs includes known solutions, which may encourage engineers to reuse rather than reason.
- When faced with truly novel problems, LLMs often provide unreliable responses, increasing the burden on engineers to detect errors.
- Accepting LLM outputs without understanding the reasoning risks atrophying simpler, foundational skills.
- The author distinguishes LLMs from 1990s-era search engines by the tendency of LLMs to promote immediate exploitation over exploration.
- Antonin argues that 'focus'—attention to why a solution works—is a skill that requires active practice.
What to watch next
- Whether engineering teams establish practices to verify and understand LLM-generated solutions rather than accepting them uncritically.
- Trends in education and professional development aimed at preserving foundational problem-solving skills in the era of AI.
- not confirmed in the source: specific industry or vendor measures that might be introduced to counteract skill atrophy.
Quick glossary
- Large Language Model (LLM): A machine learning model trained on large text corpora to generate or transform human-like text.
- Hallucination (AI): When a model produces information that is fabricated or unsupported by its training data.
- Bias (in AI): Systematic patterns in model outputs that reflect prejudices or imbalances present in training data.
- Exploration vs. Exploitation: A decision framework: exploration seeks diverse options and information, while exploitation uses known best options.
- Focus: Sustained attention and deliberate practice applied to understanding the reasoning behind solutions.
Reader FAQ
Is the author opposed to LLMs?
No. Antonin says he is not against LLMs and describes them as powerful and useful tools.
What are the main risks Antonin highlights?
He cites hallucinations, inconsistencies, biases in LLMs, and the risk that engineers may stop practicing core reasoning skills.
Does the essay recommend specific training or policy changes?
not confirmed in the source
Does Antonin believe AI will replace human engineers?
He raises a concern about whether future problem-solving might rely too much on self-reflecting AIs, but does not claim replacement is inevitable.
The skill of the future is not 'AI', but 'Focus' by Antonin February 11, 2025 · 3 min read If you frequent Hacker News regurlarly, you have likely noticed…
Sources
- The skill of the future is not 'AI', but 'Focus' (2025)
- Essential Skills for Engineers to Thrive in the AI Era
- 5 Essential Skills Engineers Need to Thrive in the AI Era
- How Engineers Can Prepare for the Future of AI
Related posts
- 27 Million Fewer Car Trips After One Year of Manhattan Congestion Pricing
- Gemini protocol deployment statistics: snapshot of the geminispace, Jan 6 2026
- High-performance header-only container library for C++23 on x86-64