In May, OpenAI agreed to pay $3 billion for Windsurf, the AI coding assistant formerly known as Codeium. Three billion dollars. For a VSCode fork.
The deal eventually fell apart, but what matters is that they wanted to do it in the first place.
Last week, Anthropic made an interesting acquisition: they bought Bun, the JavaScript runtime. Bun is open source and MIT-licensed. Anthropic could have forked it and built on top of it for free. They have Claude Code, an excellent code-writing tool.
Instead, they bought the company. Because they wanted Jarred Sumner and his team.
This is what I keep coming back to when I see another “Programming is dead” post go viral. The companies building AI, the ones who supposedly know exactly what it can and can’t do, are spending billions to acquire engineering talent. Not fire them, acquire them.
If OpenAI believed GPT could replace software engineers, why wouldn’t they build their own VS Code fork for a fraction of that cost? If Anthropic thought Claude could do the work, why make an acquisition at all?
Programming isn’t the job
Here’s my take: AI can replace most of programming, but programming isn’t the job.
Programming is a task. It’s one of many things you do as part of your work. But if you’re a software engineer, your actual job is more than typing code into an editor.
The mistake people make is conflating the task with the role. It’s like saying calculators replaced accountants. Calculators automated arithmetic, but arithmetic was never the job. The job was understanding financials, advising clients, making judgment calls, etc. The calculator just made accountants faster at the mechanical part.
AI is doing something similar for us.
What the job is
Think about what you actually do in a given week.
You sit in a meeting where someone describes a vague problem, and you’re the one who figures out what they actually need. You look at a codebase and decide which parts to change and which to leave alone. You push back on a feature request because you know it’ll create technical debt that’ll haunt the team for years. You review a colleague’s PR and catch a subtle bug that would’ve broken production. You make a call on whether to ship now or wait for more testing.
None of that is programming, but it’s all your job.
Some concerns
I’m not going to pretend nothing is changing.
Will some companies use AI as an excuse to cut headcount? Absolutely. Some already have. There will be layoffs blamed on “AI efficiency gains” that are really just cost-cutting dressed up as something else.
But think about who stays and who goes in that scenario. It’s not random. The engineers who understand that programming isn’t the job, the ones who bring judgment, context, and the ability to figure out what to build, those are the ones who stay. The ones who only brought code output might be at risk
A common worry is that juniors will get left behind. If AI handles the “doing” part, how do they build judgment? I actually think the opposite is true. AI compresses the feedback loop. What used to take days of flipping through books or waiting for Stack Overflow answers now takes seconds. The best juniors aren’t skipping steps, but getting through them faster.
Now think about your own situation. Say you were hired two years ago, before the current AI wave. Your company wanted you. They saw value in what you bring. Now, with AI tools, you’re significantly more productive. You ship faster. You handle more complexity. You’re better at your job than ever before.
“You got way more productive, so we’re letting you go” is not a sentence that makes a lot of sense.
What to do about it
If you’re reading this, you’re already thinking about this stuff. That puts you ahead. Here’s how to stay there:
- Get hands-on with AI tools. Learn what they’re actually useful for. Figure out where they save you time and where they waste it. The engineers who are doing this now will be ahead.
- Practice the non-programming parts. Judgment, trade-offs, understanding requirements, communicating with stakeholders. These skills matter more now, not less.
- Build things end-to-end. The more you understand the full picture, from requirements to deployment to maintenance, the harder you are to replace.
- Document your impact, not your output. Frame your work in terms of problems solved, not lines of code written.
- Stay curious, not defensive. The engineers who will struggle are the ones who see AI as a threat to defend against rather than a tool to master.
The shape of the work is changing: some tasks that used to take hours now take minutes, some skills matter less, others more.
But different isn’t dead. The engineers who will thrive understand that their value was never in the typing, but in the thinking, in knowing which problems to solve, in making the right trade-offs, in shipping software that actually helps people.
OpenAI and Anthropic could build their own tools. They have the best AI in the world. Instead, they’re spending billions on engineers. That should tell you something.
Terrible Software 