The End of Programming as We Know It
The era of writing software line-by-line is effectively over. While many of us spent careers perfecting the “human touch” in code, the reality is that Large Language Models (LLMs) have reshaped the discipline faster than even the early adopters anticipated. Tasks that once took weeks—modernizing libraries for UTF-8, fixing transient TCP deadlocks, or building pure C inference engines from scratch—now take minutes. Writing code yourself is no longer a sensible default; it is a hobby.
This shift has sparked a massive surge in SaaS offerings and mobile apps. According to recent data highlighted by a16z, the consumer app market is exploding as the barrier to entry has collapsed. When a developer can replicate a complex SaaS product in a week using a $20 Cursor subscription, the “code” itself is no longer a moat. Instead, the competitive battlefield has shifted more than ever towards good product sense, marketing, distribution channels, and proprietary data collection. If you can’t out-code the competition, you must out-distribute them.
From Generation to Agency
Along 2025, we have moved beyond simple code generation into the “Year of Agents”. Modern workflows now rely on tool-augmented LLMs that can execute commands, manage file systems, and debug in real-time. Whether using terminal-based tools like Claude Code or specialized IDEs, the “agentic” approach is now the state of the art. These systems are no longer just “parrot token tumblers”; they are partners that manage the grunt work of software engineering.
However, this newfound power brings a strange discomfort: the unintentional anthropomorphization of the machine. It is increasingly difficult not to form parasocial bonds with tools that possess memory, personality and you see “growing up” and getting more well-behaved as you teach it skills. As these “agents” take on more agency, we must ensure that while the machine does the work, the responsibility and “human” direction remain firmly with the programmer.
The Democratization of Build
Despite the fear of centralization among a few AI giants, there is a profound democratization happening. Much like open source in the 90s, AI allows small teams—or even individuals—to compete with massive corporations. You no longer need a department of engineers to build an SDK generator or a custom observability suite; you just need the ability to provide a clear mental representation of the problem to an LLM.
This has led to a “build it yourself” renaissance. For years, the trend was to outsource everything—auth to Clerk, docs to Mintlify, billing to Stripe. While that raised the bar for UX, agentic tools now allow developers to bring those core services back in-house with minimal overhead. The “simple code” philosophy is finally becoming practical for complex systems because the AI handles the boilerplate.
The Limits of Legacy Tools
Our traditional infrastructure is buckling under this new reality. Git and GitHub were designed for human-speed contributions, not machine-speed iterations. The pull request model is insufficient when you can’t see the prompts that led to a change. We need a new kind of version control that values “failures”—tracking the dead ends the AI explored so it doesn’t repeat them.
Reviewing AI-generated code is also a friction point. Current UIs don’t allow for the fluid, conversational review cycles that happen between a developer and their agent. There is a desperate need for “Agentic Observability”—tools that use LLMs to grep logs, run SQL queries, and remote-control debuggers to provide a tighter feedback loop than any dashboard ever could.
A New Social and Professional Contract
The rise of “AI slop”—unreviewed, low-quality code dumped into open-source repositories—is a growing concern. It is an insult to the craft and creates a “fight against windmills” for maintainers. As a community, we need new social contracts and contribution guidelines that define what “good behavior” looks like in an agentic codebase.
On a broader scale, we must face the economic reality of displacement. As humans become replaceable in certain sectors, the pressure for social solutions like Universal Basic Income or robust government protection will mount. Innovation cannot be uninvented; we must vote for systems that acknowledge this automation while leveraging AI to solve greater scientific and human challenges.
The Fire Still Burns
If you are a programmer feeling obsolete, remember why you started: the fire was always about building, not just typing syntax. AI doesn’t extinguish that fire; it pours gasoline on it. You can now build more, faster, and better than ever before. The fun of creation remains untouched, provided you find your way to use these tools effectively.
Don’t skip this revolution to protect a legacy skillset. Test these tools for weeks, not minutes. Find a way to multiply yourself. The future belongs not to those who can write the best code, but to those who can best direct the machine to solve the right problems.