Think back to February 2020. People were talking about a virus overseas, but life felt normal. We were planning trips, eating out, and ignoring the noise. Then, in three weeks, the world broke and rearranged itself.
We are in that same "this seems overblown" phase right now with AI. But this time, it’s not a virus—it’s an intelligence explosion.
My Confession as a Senior Engineer
I’m going to be brutally honest: I haven’t written a single line of code in the last three months.
As a Senior Engineer, my job has fundamentally shifted. I no longer build; I review. I describe a complex architectural outcome in plain English, and the LLM builds it. Not a rough draft, not a buggy snippet—a finished, functional product.
If you are a software engineer and you are denying this reality, you are in a dangerous state of denial. You aren’t just missing out; you are positioning yourself to be the horse-cart driver in the age of the automobile.
The "It Wasn't That Good" Trap
I hear this constantly: "I tried AI six months ago and it couldn't handle my stack."
You’re right. Six months ago, it wasn’t good enough. In AI time, six months is ancient history.
- Phase 1: Copilot (Basic code completion)
- Phase 2: Chatbots (Debugging help)
- Phase 3 (Now): Gemini 3 Pro on Antigravity (Full autonomous development)
The difference is night and day. The "tech problem" of AI coding is essentially solved. It is only going to get better from here because we have reached the most critical point on the exponential graph: AI is now coding itself to make itself better.
The Intelligence Explosion
When the machines start debugging their own training runs and managing their own deployments, the pace of progress stops being linear. We are about to witness a vertical spike in capability.
On February 5, 2026, OpenAI released GPT-5.3-Codex. In a historic first, OpenAI revealed that this model was "instrumental in creating itself." Early versions of the model were used by the research team to:
- Debug Training Runs: It monitored its own training cycle and fixed patterns that were causing bottlenecks.
- Optimize Infrastructure: It identified context-rendering bugs and root causes for low cache hit rates in its own harness.
- Manage Deployment: It autonomously scaled GPU clusters to handle launch traffic surges while maintaining stable latency.
This isn't a prediction; it's a documentation of recursive self-improvement. (Reference: Matt Shumer's Analysis | OpenAI GPT-5.3 System Card)
Business Logic is Ruthless
You can’t blame the companies. When a multi-national firm has thousands of employees sitting on a bench, and a tool like Claude Code or Antigravity arrives that can automate their entire workflow for $20 a month, the math is simple.
Companies will always cut costs to increase profit. That’s how business works. The "bench" is about to be cleared.
Evolution, Not Fear-Mongering
This isn't fear-mongering; it's a reality check. When cars took over the market, nobody blamed the horse-cart driver for losing his job—it was just evolution. It was necessary.
What we are seeing now is the same necessity.
So, what do you do?
- Update or Evaporate: Master these tools now. If you can’t use an LLM to do 10x the work of a traditional dev, you are a liability.
- Human-to-Human Connection: Lean into the things AI can't replicate yet—deep strategy, empathy, and high-level trust.
- Get Serious: Stop treating AI like a search engine. Treat it like a team of senior associates that never sleep.
The future is here. It hasn’t knocked on your door yet, but it’s already in the driveway.
Are you going to open the door, or wait for it to be kicked down?