Creativity starts after your first idea ends.

The cost of entry for AI is lower now than it will ever be again. If we only use AI for quick answers we’ll struggle to justify higher costs in the future. Those who use AI as a thinking partner will see the value differently. They’ll happily pay increased prices because they’ve experienced AI as an affordable teammate rather than just a fancy search engine.

Every piece of text you write adds weight to the story you tell.

At some point that weight makes the story complicated and hard to understand.

Weight becomes the blocker for advancing. You have built debt.

This applies to generating code with AI too.

It’s powerful but can produce code in wrong places, fix unintended problems and make assumptions about what to write.

Just because AI can create a lot quickly doesn’t mean it’s creating what we need.

The real question isn’t “how many hours did you work?” but “did you deliver the value promised?”.

If the client is happy with the results then you’re delivering what they’re paying for.

Hourly billing punishes efficiency and expertise. Outcome matters, not the time spent.

People rarely enjoy all aspects of their job equally. This is particularly noticeable with fullstack developers who are expected to be proficient across the entire stack. From transforming Figma mockups into functional UIs to implementing complex architectural services. The range of skills required is… let’s call it extensive.

At some point the industry decided fullstack was the ideal. You should handle everything from pixel-perfect CSS to database optimization because… it’s efficient? More cost-effective?

Outside of solo entrepreneurs I’ve met remarkably few developers who genuinely enjoy both ends of the spectrum equally. Most have clear preferences. They either light up discussing UI interactions or get excited about backend architecture but rarely both.

This preference gap can explain why tools like vibe coding are gaining popularity. These tools reduce the parts of development work people enjoy least while still getting the job done.

You can build great things that feel effortless now. That simplicity exists only because you invested hard effort earlier.

Like grinding through side quests in a game before tackling the main storyline. All that seemingly extra work makes the final boss battle feel surprisingly manageable.

Tech influencers often preach ‘If it’s easy, it’s probably not special.’ But they miss the timeline. What feels easy today only seems that way because you already did the hard work yesterday.

I’ve found AI actually demands a different kind of thinking from me.

When I’m vague, it gives vague results. This forces me to clarify my own thinking and be more precise.

The cognitive work doesn’t disappear, it shifts to higher level of design and critical evaluation.

The challenge is using AI intentionally rather than letting it replace our thinking entirely.

We are entering an era where users no longer navigate the web through links, but through conversations with AI. These systems don’t just point to information, they break it down into bits and pieces that they can then join, personalize and deliver directly to you. Search results, news pages and social feeds are slowly being replaced with AI assistants that process information on our behalf.

Sure there’ll be links, but think of them more as page 2 of Google results. People that want answers want them quick and GenAI delivers right now.

How this will disrupt the ads business, a business where number of eyes is important we don’t know yet. But just think about the effect it has when an AI Assistant searches on your behalf and give you the results you need.

Social media is no longer social, it is just media. We’ve forgotten to remove the “social” part when we talk about it.

We once came to these platforms to see what our friends were up to. Then we returned because we got what they predicted we should like. Now they’re no different than TV streaming or radio except for their more efficient dopamine-triggering mechanisms.

Algorithms are not social.

Throughout my development career I’ve probably been acting like a code chameleon. Join a company, look at PRs and code and try to mirror the style and ask the subtle question of “Which code and PRs should I mimic if I want my submissions to pass quickly?”

This adaptive behavior isn’t just about following rules. It’s about understanding the unwritten language of this new codebase. That teams develop patterns and practices that represent their knowledge.

Current AI coding tools miss this context. They generate functional but generic code drawn from open source repositories, that in isolation are awesome but in relation to existing internal codebases are off.

They write average code for specific environments that don’t want to be average.

Beyond Simple Generation

I believe we’re heading toward a more sophisticated approach with specialized AI agents handling different aspects of development.

Solution Agents that focuses purely on solving the problem, functionally without concern for style or conventions. Adaptation Agent that transform this solution to match the companies specific patterns and practices and possibly a Readability Agent that ensures that this code is comprehensible by humans.

First write the expectations, then generate the code, then create tests, then refine the code. Each step handled by agents optimized for that specific task.

This mirrors how human teams work today. Establish direction, implement solutions, and ensure quality and consistency. The difference is speed and scale.

The Future of Development

The most powerful development environments won’t be those that simply generate code fastest. They’ll be systems that understand your specific codebase deeply and adapt to your team’s unique approach.

When you begin a project, you’ll decide upfront what you want: TypeScript, specific linting rules, run on Cloudflare. But beyond these technical choices, your AI system will learn what makes your codebase unique.

Unlike the patterns found in open source repositories, your AI agents will understand the complete picture of your application. They’ll recognize that teams evolve their own concepts and styles that may not match external patterns.

This is the natural progression from todays word-by-word generation to truly contextual development assistance.

It’s not just about writing code, it is about writing code that belongs.