The minute you have to discuss those things with someone else, your bandwidth decreases by orders of magnitude and now you have to put words to these things and describe them, and physically type them in or vocalize them. Then your counterpart has to input them through his eyes and ears, process that, and re-output his thoughts to you. Slow, slow, slow, and prone to error and specificity problems as you translate technical concepts to English and back.
Chat as a UX interface is similarly slow and poorly specific. It has all the shortcomings of discussing your idea with a human and really no upside besides the dictionary-like recall.
For me pair programming accelerates development to much more than 2x. Over time the two of you figure out how to use each other's strengths, and as both of you immerse yourself in the same context you begin to understand what's needed without speaking every bit of syntax between each other.
In best cases as a driver you end up producing high quality on the first pass, because you know that your partner will immediately catch anything that doesn't look right. You also go fast because you can sometimes skim over complexities letting your partner think ahead and share that context load.
I'll leave readers to find all the caveats here
Edit: I should probably mention why I think Chat Interface for AI is not working like Pair programming: As much as it may fake it, AI isn't learning anything while you're chatting to it. Its pointless to argue your case or discuss architectural approaches. An approach that yields better results with Chat AI is to just edit/expand your original prompt. It also feels less like a waste of time.
With Pair programming, you may chat upfront, but you won't reach that shared understanding until you start trying to implement something. For now Chat AI has no shared understanding, just "what I asked you to do" thing, and that's not good enough.
For me, there's
- Time when I want to discuss the approach and/or code to something (someone being there is a requirement)
- Time when I want to rubber duck, and put things to words (someone being there doesn't hurt, but it doesn't help)
- Time when I want to write code that implements things, which may be based on the output of one of the above
That last bucket of time is generally greatly hampered by having someone else there and needing to interact with them. Being able to separate them (having people there for the first one or two, but not the third) is, for me, optimal.
Maybe collaborate the first hour each morning, then the first hour after lunch.
The value of pair programming is inversely proportional to the expertise of the participant. Junior devs who pair with senior devs get a lot out of it, senior devs not so much.
GP is probably a more experienced dev, whereas you are the type of dev who says things like “I’m guessing that you…”.
In pair programming as I learned it and as I have occasionally experienced it, two individuals challenge each other to be their best selves while also handing off tasks that break flow so that the pair as a whole is in constant flow. When it works this is a fantastic, productive, intense experience. I would agree that it is more than 2x as productive. I don't believe it's possible to achieve this state at all with a mismatched pair.
If this is the experience people have in mind, then it's not surprising that they think that those who think it's only for training juniors haven't actually tried it very much.
That's very much not my experience. Pairing on design and diagrams is as or more useful than on the code itself. Once you have a good design, the code is pretty simple.
What's your definition of 'learn'? An LLM absolutely does extract and store information from its context. Sure, it's only short term memory and it's gone the next session, but within the session it's still learning.
I like your suggestion to update your original prompt instead of continuing the conversation.
with LLMs that leaning is only context, and in case of “chat ai” is the chat backlog. It’s easy for the LLM to get stuck, and there aren’t many tools yet that help you change and morph that context to be more like shared understanding between two colleagues.
There is research going on in this area, eg “chain of thought”, so we’ll see, maybe things will get better
I’m not saying pair programming is a silver bullet, and I tend to agree that working on your own can be vastly more efficient. I do however think that it’s a very useful tool for critical functionality and hard problems and shouldn’t be dismissed.
I've been coding long enough to notice there are times where the problem is complex and unclear enough that my own thought process will turn into pair programming with myself, literally chatting with myself in a text file; this process has the bandwidth and latency on the same order as talking to another person, so I might just as well do that and get the benefit of an independent perspective.
The above is really more of a design-level discussion. However, there are other times - precisely those times that pair programming is meant for - when the problem is clear enough I can immerse myself in it. Using the slow I/O mode, being deliberate is exactly the opposite of what I need then. By moving alone and focused, keeping my thoughts below the level of words, I can explore the problem space much further, rapidly proposing a solution, feeling it out, proposing another, comparing, deciding on a direction, noticing edge cases and bad design up front and dealing with them, all in a rapid feedback loop with test. Pair programming in this scenario would truly force me to "use the slower I/O parts of your brain", in that exact sense: it's like splitting a highly-optimized in-memory data processing pipeline in two, and making the halves communicate over IPC. With JSON.
As for bus factor, I find the argument bogus anyway. For that to work, pair programming would've to be executed with the same partner or small group of partners, preferably working on the same or related code modules, daily, over the course of weeks at least - otherwise neither them nor I are going to have enough exposure to understand what the other is working on. But it's not how pair programming worked when I've experienced it.
It's a problem with code reviews, too: if your project has depth[0], I won't really understand the whole context of what you're doing, and you won't understand the context of my work, so our reviews of each others' code will quickly degenerate to spotting typos, style violations, and peculiar design choices; neither of us will have time or mental capacity to fully understand the changeset before "+2 LGTM"-ing it away.
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[0] - I don't know if there's a a better, established term for it. What I mean is depth vs. breadth in the project architecture. Example of depth: you have a main execution orchestrator, you have an external data system that handles integrations with a dozen different data storage systems, then you have math-heavy business logic on data, then you have RPC for integrating with GUI software developed by another team, then you have extensive configuration system, etc. - each of those areas is full of design and coding challenges that don't transfer to any other. Contrast that with an example of breadth: a typical webapp or mobile app, where 80% of the code is just some UI components and a hundred different screens, with very little unique or domain-specific logic. In those projects, developers are like free electrons in metal: they can pick any part of the project at any given moment and be equally productive working on it, because every part is basically the same as every other part. In those projects, I can see both pair programming and code reviews deliver on their promises in full.
Peer and near-peer reviews have always wound up being nitpicking or perfunctory.
An alternative that might work if you want two hands on every change for process reasons is to have the reviewer do something closer to formal QA, building and running the changed code to verify it has the expected behavior. That has a lot of limitations too, but it least it doesn’t degrade to bikeshedding about variable name aesthetics.
Pair programming is endlessly frustrating beyond just rubber duckying because I’m having to exit my mental model, communicate it to someone else, and then translate and relate their inputs back into my mental model which is not exactly rooted in language in my head.
I wrote about this here: https://digitalseams.com/blog/the-ideal-ai-interface-is-prob...
Especially with the new r1 thinking output, I find it useful to iterate on the initial prompt as a way to make my ideas more concrete as much as iterating through the chat interface which is more hit and miss due to context length limits.
* I don’t mean that in a negative way, but in a “I can’t expect another person to respond to me instantly at 10 words per second” way.
I mean, isn’t typing your code also forcing you to make your ideas concrete
our brains like to jump over inconsistencies or small gaps in our logic when working by themselves, but try to explain that same concept to someone else and those inconsistencies and gaps become glaringly obvious (doubly so if the other person starts asking questions you never considered)
it's why pair programming and rubber duck debugging work at all, at least in my opinion
Subvocalization/explicit vocalization of what you're doing actually improves your understanding of the code. Doing so may 'decrease bandwith', but improves comprehension, because it's basically inline rubber duck debugging.
It's actually easy to write code which you don't understand and cannot explain what it's doing, whether at the syntax, logic or application level. I think the analogue is to writing well; anyone can write streams of consciousness amounting to word salad garbage. But a good writer can cut things down and explain why every single thing was chosen, right down to the punctuations. This feature of writing should be even more apparent with code.
I've coded tons of things where I can get the code working in a mediocre fashion, and yet find great difficulty in try to verbally explain what I'm doing.
In contrast there's been code where I've been able to explain each step of what I'm doing before I even write anything; in those situations what generally comes out tends to be superior maintainable code, and readable too.
Neural networks and evolved structures and pathways (e.g. humans make do with ~20k genes and about that many more in regulatory sequences) are absolutely more efficient, but good luck debugging them.
