The rapid expansion of context windows has become one of the most visible metrics of progress in modern language models. In just a few years, we have moved from a few thousand tokens to systems advertising over a million. The value proposition seems straightforward: providing more context should lead to a better understanding for the task.

In practice, it’s a bit more complicated. Longer context windows unlock new use cases, but they also introduce costs, failure modes, and design tradeoffs that are easy to overlook. For teams building production systems, these tradeoffs matter as much as raw capability.

Growing up in the 90s and early 2000s, tech was a foundational part of my childhood.

I built more physical computers than I can remember. We went from paper maps to GPS (which itself evolved from DVDs with static maps to internet-connected real-time navigation). CD players became MP3 players, then streaming services. We had Palm Pilots and early attempts at “smart” phones, which were anything but. Our computers could search for extraterrestrial life through SETI. We emerged from the pager era to portable phones to the entire internet in our pocket (which evolved from charging per SMS or megabyte to unlimited data plans).

Although unintentional, the software patent system has evolved into a classic game-theoretic dynamic.

The core issue is that offensive and defensive patent stockpiles look identical from the outside. That ambiguity triggers a feedback loop that sustains the system.

Patents are meant to protect specific inventions, but in software where ideas spread quickly, they have taken the role of strategic assets in an arms race. Useful concepts are rediscovered independently or unintentionally overlapped. As a result, nearly every company is vulnerable to infringement claims at all times, often through no fault of their own.

Few industries have experienced as many disruptions in the 21st century as publishing. The last two decades have brought a shift from print to digital, the decline of ad-supported models, the rise of paywalls, and a surge of self-publishing and social platforms.

For most of history, humanity’s greatest accomplishment has been its ability to out-accelerate its own existential problems.

The acceleration in population growth put pressure on food supplies, health, and energy. In response, we invented fertilizer, sanitation, and a vast array of energy generation and storage technologies. Humanity now thrives in high-density environments.

This acceleration has enabled humanity to adapt and flourish despite a constant barrage of challenges. One might assume that this trend will continue indefinitely.

But what if that era is ending — as we approach limits imposed by physics across a variety of domains?

Vibe coding—the art of guiding LLMs to write code on your behalf, according to stated requirements—is shifting from a quirky, experimental approach to something that, in many cases, is expected even in large, bureaucratic organizations. It can be a huge productivity booster, but it also comes with real risks: even a single misplaced character can lead to SQL injection, bypass logins, or expose sensitive user data.

As AI tools have become more widespread, and generally better, I’ve seen the rift between those who incorporate these tools into their development process and those who don’t, widen.

Large Language Models (LLMs) are transforming the way we build software. Vibe Coding — popularized by tools like Cursor and Windsurf — refers to deeply embedding LLMs into the development workflow. Like all tools, however, it takes time to learn how to use them effectively.