AI’s impact on engineering jobs may be different than expected

You also have to treat this as outsourcing labor to a savant with a very, very short memory, so:

1. Write every prompt like a government work contract in which you're required to select the lowest bidder, so put guardrails everywhere. Keep a text editor open with your work contract, edit the goal at the bottom, and then fire off your reply.

2. Instruct the model to keep a detailed log in a file and, after a context compaction, instruct it to read this again.

3. Use models from different companies to review one another's work. If you're using Opus-4.5 for code generation, then consider using GPT-5.2-Codex for review.

4. Build a mental model for which models are good at which tasks. Mine is:

  3a. Mathematical Thinking (proofs, et al.): Gemini DeepThink

  3b. Software Architectural Planning: GPT5-Pro (not 5.1 or 5.2)

  3c. Web Search & Deep Research: Gemini 3-Pro

  3d. Technical Writing: GPT-4.5

  3e. Code Generation & Refactoring: Opus-4.5

  3f. Image Generation: Nano Banana Pro

But in the bigger picture, where does it stop?

You had to do manual spark advance while driving in the 30's

You had to set the weights in the distributor to adjust spark advance in the 70's

Now the computer has a programed set of tables for spark advance

I bet you never think of spark advance while you're driving now, does that take away from deeply understanding the car?

I used to think about the accelerator pump in a the carburetor when I drove one, now I just know that the extra fuel richening comes from another lookup table in the ECU when I press the gas pedal down, am I less connected to the car now?

My old Jeep would lean cut when I took my foot off the gas and the throttle would shut quickly. My early fuel injected car from the 80's had a damper to slow the throttle closing to prevent extreme leaning out when you take your foot off the gas. Now that's all tables in the ECU.

I don't disagree with you that a manual transmission lets you really understand the car, but that's really just the latest thing were losing, we don't even remember all of the other "deep connections" to a car that were there 50-100 years ago. What makes this one different? Is it just the one that's salient now?

To bring it back on topic. I used to hand-tune assembly for high performance stuff, now the compilers do better than me and I haven't looked at assembly in probably 10 years. Is moving to AI generated code any different? I still think about how I write my C so that the compiler gets the best hints to make good assembly, but I don't touch the assembly. In a few years will be be clever with how we prompt so that the AI generates the best code? Is that a fundamentally different thing, or does it just feel weird to us because of where we are now. How did the generation of programmers before me feel about giving up assembly and handing it over to the compilers?

That was using pay per token.

> Write every prompt like a government work contract in which you're required to select the lowest bidder, so put guardrails everywhere.

That is what I was doing yesterday. Worked fantastically. Today, I do the very same thing and... Nope. Can't even stick to the simplest instructions that have been perfectly fine in the past.

> If you're using Opus-4.5 for code generation, then consider using GPT-5.2-Codex for review.

As mentioned, I tried using Opus, but it didn't even get the point of producing anything worth reviewing. I've had great luck with it before, but not today.

> Instruct the model to keep a detailed log in a file and, after a context compaction

No chance of getting anywhere close to needing compaction today. I had to abort long before that.

> Build a mental model for which models are good at which tasks.

See, like I mentioned before, I thought I had this figured out, but now today it has all gone out the window.