>The people really leading AI coding right now (and I’d put myself near the front, though not all the way there) don’t read code. They manage the things that produce code.

I can’t imagine any other example where people voluntarily move for a black box approach.

Imagine taking a picture on autoshot mode and refusing to look at it. If the client doesn’t like it because it’s too bright, tweak the settings and shoot again, but never look at the output.

What is the logic here? Because if you can read code, I can’t imagine poking the result with black box testing being faster.

Are these people just handing off the review process to others? Are they unable to read code and hiding it? Why would you handicap yourself this way?

I think many people are missing the overall meaning of these sorts of posts.. that is they are describing a new type of programmer that will only use agents and never read the underlying code. These vibe/agent coders will use natural(-ish) language to communicate with the agents and wouldn't look at the code anymore than, say, a PHP developer would look at the underlying assembly. It is not the level of abstraction they are working on. There are many use cases where this type of coding will work fine and it will let many people who previously couldn't really take advantage of computers to do so. This is great but in no way will do anything to replace the need for code that requires humans to understand (which, in turn, requires participation in the writing).

> Imagine taking a picture on autoshot mode and refusing to look at it. If the client doesn’t like it because it’s too bright, tweak the settings and shoot again, but never look at the output.

The output of code isn't just the code itself, it's the product. The code is a means to an end.

So the proper analogy isn't the photographer not looking at the photos, it's the photographer not looking at what's going on under the hood to produce the photos. Which, of course, is perfectly common and normal.

The output is the program behavior. You use it, like a user, and give feedback to the coding agent.

If the app is too bright, you tweak the settings and build it again.

Photography used to involve developing film in dark rooms. Now my iPhone does... god knows what to the photo - I just tweak in post, or reshoot. I _could_ get the raw, understand the algorithm to transform that into sRGB, understand my compression settings, etc - but I don't need to.

Similarly, I think there will be people who create useful software without looking at what happens in between. And there will still be low-level software engineers for whom what happens in between is their job.

AI-assisted coding is not a black box in the way that managing an engineering team of humans is. You see the model "thinking", you see diffs being created, and occasionally you intervene to keep things on track. If you're leveraging AI professionally, any coding has been preceded by planning (the breadth and depth of which scale with the task) and test suites.

> What is the logic here?

It is right often enough that your time is better spent testing the functionality than the code.

Sometimes it’s not right, and you need to re-instruct (often) or dive in (not very often).

Don’t read the code, test for desired behavior, miss out on all the hidden undesired behavior injected by malicious prompts or AI providers. Brave new world!

> I can’t imagine any other example where people voluntarily move for a black box approach.

Anyone overseeing work from multiple people has to? At some point you have to let go and trust people‘s judgement, or, well, let them go. Reading and understanding the whole output of 9 concurrently running agents is impossible. People who do that (I‘m not one of them btw) must rely on higher level reports. Maybe drilling into this or that piece of code occasionally.

> I can’t imagine any other example where people voluntarily move for a black box approach.

I can think of a few. The last 78 pages of any 80-page business analysis report. The music tracks of those "12 hours of chill jazz music" YouTube videos. Political speeches written ahead of time. Basically - anywhere that a proper review is more work than the task itself, and the quality of output doesn't matter much.

No pun intended but - it's been more "vibes" than science that I've done this. It's more effective. When I focus my attention on the harness layer (tests, hooks, checks, etc), and the inputs, my overall velocity improves relative to reading & debugging the code directly.

To be fair - it is not accurate to say I absolutely never read the code. It's just rare, and it's much more the exception than the rule.

My workflow just focuses much more on the final product, and the initial input layer, not the code - it's becoming less consequential.

I think this is the logical next step -- instead of manually steering the model, just rely on the acceptance criteria and some E2E test suite (that part is tricky since you need to verify that part).

I personally think we are not that far from it, but it will need something built on top of current CLI tools.

> Because if you can read code, I can’t imagine poking the result with black box testing being faster.

I don't know... it depends on the use case. I can't imagine even the best front-end engineer ever can read HTML faster than looking at the rendered webpage to check if the layout is correct.

> What is the logic here? Because if you can read code, I can’t imagine poking the result with black box testing being faster.

It's producing seemingly working code faster than you can closely review it.

> What is the logic here? Because if you can read code, I can’t imagine poking the result with black box testing being faster.

The AI also writes the black box tests, what am I missing here?

your metaphor is wrong.

code is not the output. functionality is the output, and you do look at that.

>Imagine taking a picture on autoshot mode

Almost everyone does this. Hardly anyone taking pictures understands what f-stop or focal length are. Even those who do seldom adjust them.

There dozens of other examples where people voluntarily move to a black box approach. How many Americans drive a car with a manual transmission?

people care about results. Better processes need to produce better results. this is programming not a belief system where you have to adhere to some view or else.