The new calculus of AI-based coding

> Instead, we use an approach where a human and AI agent collaborate to produce the code changes. For our team, every commit has an engineer's name attached to it, and that engineer ultimately needs to review and stand behind the code. We use steering rules to setup constraints for how the AI agent should operate within our codebase,

This sounds a lot like Tesla's Fake Self Driving. It self drives right up to the crash, then the user is blamed.

"We have real mock versions of all our dependencies!"

Congratulations, you invented end-to-end testing.

"We have yellow flags when the build breaks!"

Congratulations! You invented backpressure.

Every team has different needs and path dependencies, so settles on a different interpretation of CI/CD and software eng process. Productizing anything in this space is going to be an uphill battle to yank away teams' hard-earned processes.

Productizing process is hard but it's been done before! When paired with a LOT of spruiking it can really progress the field. It's how we got the first CI/CD tools (eg. https://en.wikipedia.org/wiki/CruiseControl) and testing libraries (eg. pytest)

So I wish you luck!

Classic LLM article:

1) Abstract data showing an increase in "productivity" ... CHECK

2) Completely lacking in any information on what was built with that "productivity" ... CHECK

Hilarious to read this on the backend of the most widely publicized AWS failure.

"For me, roughly 80% of the code I commit these days is written by the AI agent" Therefore, it is not commited by you, but by you in the name of AI agent and the holy slop. What to say, I hope that 100x productivity is worth it and you are making tons of money. If this stuff becomes mainstream, I suggest open source developers stop doing the grind part, stop writing and maintaining cool libraries and just leave all to the productivity guys, let's see how far they get. Maybe I've seen too many 1000x hacker news..

This is the first time I see "steering rules" mentioned. I do something similar with Claude, curious how it looks for them and how they integrate it with Q/Kiro.

If you are producing real results at 10x then you should be able to show that you are a year ahead of schedule in 5 weeks.

Waiting to see anyone show even a month ahead of schedule after 6 months.

This reads like "Hey, we're not vibe coding, but when we do, we're careful!" with hints of "AI coding changes the costs associated with writing code, designing features, and refactoring" sprinkles in to stand out.

But here's the critical part: the quality of what you are creating is way lower than you think, just like AI-written blog posts.

Absolutely none of that article has ever even so much as brushed past the colloquial definition of "calculus".

These guys actually seem rattled now.

Interesting enough to me though I only skimmed.

I switched back to Rails for my side project a month ago and ai coding when doing not too complex stuff has been great. While the old NextJS code base was in shambles.

Before I was still doing a good chunk of the NextJS coding. I’m probably going to be directly coding less than 10% of the code base from here on out. I’m now spending time trying to automate things as much as possible, make my workflow better, and see what things can be coded without me in the loop. The stuff I’m talking about is basic CRUD and scraping/crawling.

For serious coding, I’d think coding yourself and having ai as your pair programmer is still the way to go.

first the Microsoft guy touting agents

now AWS guy doing it !

"My team is no different—we are producing code at 10x of typical high-velocity team. That's not hyperbole - we've actually collected and analyzed the metrics."

Rofl

"The Cost-Benefit Rebalance"

In here he basically just talks about setting up mock dependencies and introducing intermittent failures into them. Mock dependencies have been around for decades, nothing new here.

It sounds like this test system you set up is as time consuming as solving the actual problems you're trying to solve, so what time are you saving?

"Driving Fast Requires Tighter Feedback Loop"

Yes if you're code-vomiting with agents and your test infrastructure isn't rock solid things will fall apart fast, that's obvious. But setting up a rock solid test infrastructure for your system involves basically solving most of the hard problems in the first place. So again, what? What value are you gaining here?

"The communication bottleneck"

Amazon was doing this when I worked there 12 years ago. We all sat in the same room.

"The gains are real - our team's 10x throughput increase isn't theoretical, it's measurable."

Show the data and proof. Doubt.

Yeah I don't know. This reads like complete nonsense honestly.

Paraphrasing: "AI will give us huge gains, and we're already seeing it. But our pipelines and testing will need to be way stronger to withstand the massive increase in velocity!"

Velocity to do what? What are you guys even doing?

Amazon is firing 30,000 people by the way.

No.

The way to code going forward with AI is Test Driven Development. The code itself no longer matters. You give the AI a set of requirements, ie. tests that need to pass, and then let it code whatever way it needs to in order to fulfill those requirements. That's it. The new reality us programmers need to face is that code itself has an exact value of $0. That's because AI can generate it, and with every new iteration of the AI, the internal code will get better. What matters now are the prompts.

I always thought TDD was garbage, but now with AI it's the only thing that makes sense. The code itself doesn't matter at all, the only thing that matters is the tests that will prove to the AI that their code is good enough. It can be dogshit code but if it passes all the tests, then it's "good enough". Then, just wait a few months and then rerun the code generation with a new version of the AI and the code will be better. The humans don't need to know what the code actually is. If they find a bug, write a new test and force the AI to rewrite the code to include the new test.

I think TDD has really found its future now that AI coding is here to stay. Human code doesn't matter anymore and in fact I would wager that modifying AI generated code is as bad and a burden. We will need to make sure the test cases are accurate and describe what the AI needs to generate, but that's it.