The solution might be cancelling my AI subscription
AI coding tools are enabling developers to spin up apps and prototypes at unprecedented speed, but many report this leads to shallow “vibe coding,” piles of half-finished projects, and little lasting value or learning. Commenters debate whether the real problem is the technology or underlying issues like ADHD, lack of focus, and weak product vision, with some saying AI amplifies distraction while others find it a powerful aid for deep work and long-term projects. A recurring theme is that when effort and friction disappear, commitment, craftsmanship, and meaningful outcomes often erode unless users impose their own rigor and constraints.
AI, learning, and craftsmanship
- Strong split on whether AI-assisted coding yields real learning.
- Critics say delegating implementation to models is like blindly following GPS: you reach the destination but don’t build the underlying map, so skills atrophy.
- Others argue there is deep learning in using AI well: long-horizon workflows, specs, tests, multi-agent systems, model comparisons, security of agents, etc.
- Some think most of that is fleeting, model-specific trivia that will be obsolete fast; better to invest in timeless engineering skills.
ADHD, attention, and dopamine
- Many with ADHD describe AI as an “amplifier”: it makes it trivial to spin up endless projects, fragment attention, and chase dopamine instead of shipping or maintaining anything.
- Others with ADHD report the opposite: AI provides structure, externalized executive function, and lets them finish or stay focused on a single main product.
- Broad agreement that AI chat/agents can create “pseudo-productivity”: lots of busy interaction that feels productive but may be slower than reading good docs or doing deep work.
Side projects, value, and meaning
- Debate over whether a long list of AI-generated apps is joyful exploration or a depressing pile of shallow, disposable slop.
- Some see hobbyist tinkering as inherently fine, like LEGO or crosswords; not everything needs a business model.
- Others worry that ultra-cheap creation erodes commitment; without friction, there’s less pride, depth, and long-term significance.
- Several emphasize focusing on one substantial, meaningful project (or “needle”) rather than many unaligned experiments.
Productivity, quality, and maintenance
- Supporters say AI is superb for boilerplate, small one-off tools, infra scripts, and rapid prototyping; they cite real-world wins (e.g., migration utilities, home infra, personal dashboards).
- Skeptics note maintenance headaches: LLM output is unreliable, hard to review, often sloppier than hand-written code; monitoring it is exhausting compared to trusting a compiler.
- Concern that AI makes it easy to create large, untested codebases that are hard to understand without ongoing model access.
Tools, friction, and media lens
- Several frame AI like calculators or GPS: it removes “incidental friction,” freeing time for true ambiguity—but also removes learning opportunities and serendipitous skill-building.
- The distinction between incidental vs. meaningful friction is seen as user- and goal-dependent.
- Some use a McLuhan-esque view: AI doesn’t just extend cognition, it can replace parts of it, raising questions about attention, wisdom, and long-term craft.
Ethics, economics, and incentives
- Some call generative AI “stealing other people’s work,” energy-intensive, and wealth-concentrating; others ignore or downplay these issues.
- A recurring theme: tools are force multipliers, but “there has to be some force to multiply” and incentives still dominate outcomes. AI alone won’t create a viable business or meaningful work.