I think this is kind of true, but also kind of not true. Programming, like all writing, is the physical manifestation of a much more complex mental process. By the time that I know what I want to write, the hardwork is done. In that way, you can think of co-pilot as a way of increasing the WPM of an average coder. But the WPM isn't the bit that matters. In fact almost the onlt thing that matters are hte bits you won't predict.

Indeed, in the limit of maximal abstraction, i.e. semantic compression, code becomes unreadable by humans in practice. We can see this in code golf competitions.

Let me rephrase:

> The fact that GPT3 works at all for English indicates that English is too low level and forces a lot of redundancy (low entropy).

I don't think the goal is to compress information/language and maximize "surprise".

That would only follow if we were trying to optimize code for brevity, and I have no clue why that would be your top priority.

I write code directly as gziped text. Saves lots of key strokes and you can use off the shelf compilers by piping through zcat first

>From a programmer productivity optimization perspective it should be impossible to reliably predict the next statement

Why? 99.9% of programming being done is composition of trivial logical propositions, in some semantic context. The things we implement are trivial, unless you're thinking about symbolic proofs etc

Someone at work showed me copilot works on WebStorm today (I also use VSCode).

Luckily, there’s a jetbrains addon for it.

But what about the potential for intellectual property problems?

What do you use it for?

It's not useful if you use it to auto complete pages of code. It is useful to see it propose lines, read, and accept its proposals. Sometimes it just saves you a second of typing. Sometimes it makes a suggestion that causes you to update what you wanted to do. Sometimes it proposes useless stuff. On the whole, I really like it and think it's a boon to productivity.

I was exceptionally skeptical about it, but it's been very useful for me and I'm only using it for minor tasks, like automatically writing loops to pull out data from arrays, merge them, sort information, make cURL calls and process data, etc.

Simply leading the horse to water is enough in something like PHP:

// instantiate cURL event from API URL, POST vars to it using key as variable name, store output in JSON array and pretty print to screen

Usually results in code that is 95-100% of the way done.

From my anecdotal experience, the vast majority of the time (90+%).

In my experience 95% of the time. And 80% of the time it output codes which is better than I would have done myself in a first approach (thinks of corner cases, adds meaningful comments etc.). It’s impressive.

The overwhelming majority. Whatever used to take me an hour or two is now a 10-minute task.