> The act of typing isn't the hard part - its understanding what's going on, and why you're doing it. Using AI to generate code is only faster if you try and skip that step - which leads to an inevitable disaster

It’s more than just typing though. A simple example remembering the exact incantation of CSS classes to style something that you can easily describe in plain English.

Yes, you could look them up or maybe even memorize them. But there’s no way you can make wholesale changes to a layout faster than a machine.

It lowers the cost for experimentation. A whole series of “what if this was…” can be answered with an implementation in minutes. Not a whole afternoon on one idea that you feel a sunk cost to keep.

Those mean spirited people are actually capitalists and they've been chasing the dream of perpetual labor since the 1800s.

We do different things, I do code for other people to use.

Once we tried shipping features and updates every week, because we could ideate, code, test and deploy that fast.

No user wanted that - product owners and business wanted that or they thought they wanted, until users came with torches and pitchforks.

Don’t forget there is user adoption and education.

Churning out features no one will use because they don’t know about is useless.

If you’ve never read Fred Brooks, I’d recommend it. The aphorism is a bit dated but rings true: you can’t add another developer and make the process go faster. It usually slows teams down.

I’ve seen it time and again: startups move from their market-fit phase into an operational excellence phase on the backing of VC funding and they start hiring a ton of people. Most of those developers are highly educated, specialized people with deep technical skills and they’re often put to work making the boxes more blue or sitting in meetings with PMs for hours. Teams slow down output when you add more people.

You don’t have a quota. It’s not like you’ll have fewer units to sell if you don’t add that 30k lines of code by Friday.

This is knowledge work. The work is understanding problems and knowing how to develop solutions to them. You add more people and you end up adding overhead. Communication, co-ordination, operations overhead.

The real bottle necks are people and releasing systems into production. Every line of code change is a liability. There’s risk tolerance to manage in order to achieve five-nines.

A well-sized team that has worked together a long time can outperform a massive team any day in my experience.

I've worked in two different types of environments - one where what you said is absolutely true (most of my jobs), and another where it's not true and the quote holds up.

The difference, I think is:

- Code factories where everything is moving fast - there's no time to think about how to simplify a problem, just gotta get it done. These companies tended to hire their way out of slowness, which led to more code, more complexity, and more code needed to deal with and resolve edge cases introduced by the complexity. I can count many times I was handed directives to implement something that I knew was far more complex than it had to be, but because of the pressure to move forward it was virtually impossible to push back. Maybe it's the only way they can make the business case work, but IMO it undoubtedly led to far, far more code than would've been necessary if it were possible to consider problems more carefully and if engineers had more autonomy. In these companies also a lot of time was consumed by meetings trying to "sync up" with the 100 other people moving in one direction.

- Smaller shops, open source projects, or indie development where there isn't a rush to get something out the door. Here, it's possible to think through a problem and come up with a solution that reduces code surface area. This was about solving the largest number of problems with the least amount of complexity. Most of my time at this company was spent thinking through how to solve the problem and considering edge cases and exploratory coding, the actual implementation was really quick to write. It really helped that I had a boss who understood and encouraged this, and we were working on safety critical systems. My boss liked to say "you can't birth a baby in less than 9 months just by adding another woman".

I think most of the difference is in team size. A larger team inherently results in more code to do less, because of the n*(n-1)/2 communication overhead [1].

Recently I learned the Navy SEALs saying "Slow is smooth, smooth is fast" which I feel sums up my experience well.

[1] https://en.wikipedia.org/wiki/The_Mythical_Man-Month

Even if the entire totem pole of decision makers in a company thinks writing code is the bottleneck doesn't make it true that writing code is the bottleneck.

On the extreme end to prove the point, the suits intentionally abstract out reality into neat forecasts and spreadsheet cells.

It's hard for me to think of something concrete that will convince you. Does code map directly to business outcomes in your experience? Because it's overwhelmingly not even remotely true in my experience.

even just "all lines of code are not created equal" tells me there's no direct correlation with business value.

But how much time per week does an SWE actually spend writing code?

It sounds like you just aren't very good at managing teams of programmers tbh. If your bottleneck is producing code, very rarely does hiring more programmers actually help.

I agree the slogan isn't very true. It's similar to another line of commentary that would suggest soft skills are more important than the hard skills of actually being able to program, i.e. the primary service being paid for.

There is some truth to it, like Brooks' Law (https://en.wikipedia.org/wiki/Brooks's_law) about how adding people to an already late project will just make it later. There are many factors in how long a software engineering task takes beyond pure typing speed, which suggests there are factors beyond code produced per day as well. But some typing has to be done, and some code has to be produced, and those can absolutely be bottlenecks.

Another way of looking at it that I like is Hickey's hierarchy of the problems of programming and their relative costs, from slide 22: https://github.com/matthiasn/talk-transcripts/blob/master/Hi... If you have inherent domain complexity, or a misconception on how to apply programming to a domain, those are 10x worse costs than any day-to-day practice of programming concerns ("the code"), and there's a 10x further reduction for trivialisms like typos.

I think some of it must be cope since so many are in organizations where the more they get promoted the less they program, trending towards (and sometimes reaching) 0. In such an organization sure, code isn't the bottleneck per se, it's a symptom of an underlying cause. The bottleneck would be the bad incentives that get people to schedule incessant unnecessary meetings with as many people as they can to show leadership of stakeholders for promotion doc material, and other questionable things shoved on the best engineers that take them away from engineering. Remove those, and suddenly productivity can go way up, and code produced will go up as well.

I've also always been amused by estimates of what constitutes "good" productivity if you try to quantify it in lines of code. There's a paper from 1994 by Jim Coplien, "Borland Software Craftsmanship: A New Look at Process, Quality, and Productivity". It's summarized in the free book by Richard Gabriel, "Patterns of Software". (https://www.dreamsongs.com/Files/PatternsOfSoftware.pdf pg 135) They were making new spreadsheet software for Windows, and had a group of "very high caliber" professionals, with a core group of 4 people (2 with important prior domain expertise) and then 4 more team members added after a year. "The QPW group, consisting of about eight people, took 31 months to produce a commercial product containing 1 million lines of code. This elapsed time includes the prototypes, but the line count does not. That is, each member of this group produced 1,000 lines of final code per week."

Later on, Coplien was asked "what he thought was a good average for US software productivity", and the answer was "1000 to 2000 non-commentary source lines per programmer per year". Also: "this number was constant for a large in-house program over its 15-year lifetime -- so that original development and maintenance moved at the same pace: slowly". An average of 1k lines a year is 19 lines a week, or about 4 lines a day for a work-week. This was considered acceptable for an average, whereas for an exceptional team you could get 200 a day. Might not there be ways to boost the average from 4 to something like 12 or 20? If your organization is at 4, there is clearly a bottleneck. (For extra context, the QPW group was in C++, and Gabriel notes he had personal experience with several groups demonstrating similar productivity levels. "I watched Lisp programmers produce 1000 lines of Lisp/CLOS code per month per person, which is roughly equivalent to 350 to 1000 lines of C++ code per week." Of course language matters in lines of code comparisons.)