The Economics of Software Teams: Why Most Engineering Orgs Are Flying Blind

> The obvious objection is that code produced at that speed becomes unmanageable, a liability in itself. That is a reasonable concern, but it largely applies when agents produce code that humans then maintain. Agentic platforms are being iterated upon quickly, and for established patterns and non-business-critical code, which is the majority of what most engineering organizations actually maintain, detailed human familiarity with the codebase matters less than it once did. A messy codebase is still cheaper to send ten agents through than to staff a team around. And even if the agents need ten days to reason through an unfamiliar system, that is still faster and cheaper than most development teams operating today. The liability argument holds in a human-to-human or agent-to-human world. In an agent-to-agent world, it largely dissolves.

Then I'd wager it's the same for the courses and workshop this guy is selling...an LLM can probably give me at least 75% of the financial insights for not even .1% of what this "agile coach" is asking for his workshops and courses.

Maybe the "agile coach LLM" can explain to the "coding LLM's" why they're too expensive, and then the "coding LLM's" can tell the "agile coach LLM" to take the next standby shift then, if he knows so much about code?

And then we actual humans can have a day off and relax at the pool.

> A messy codebase is still cheaper to send ten agents through than to staff a team around. And even if the agents need ten days to reason through an unfamiliar system, that is still faster and cheaper than most development teams operating today.

I’ve been on 2 failed projects that have been entirely AI generated and it’s not that agents slow down and you can just send more agents to work on projects for longer, it’s that they becoming completely unable to make any progress whatsoever, and whatever progress they do make is wrong.

I thought it was a good article, till I saw the Slack example.

The copy doesn’t even remotely grasp the scale of what the actual Slack sofware does in terms of scale, relaiability, observability, monitorability, maintability and pretty sure also functionality.

Author only writes about the non-dev work as difference, which seems like he doesn’t know what he’s talking about in all, and what running an application at that scale actually means.

This "clone" doesn’t get you any closer to an actualy Slack copy than a white piece of paper

When I see someone just throwing a lot of numbers and graphs at me, I see that there are in to win an argument, and not propose an idea.

Of late, I've come across a lot of ideas from Rory Sutherland and my conclusion from listening to his ideas is that there are some people, who're obsessed with numbers, because to them it's a way to find certainty and win arguments. He calls them "Finance People" (him being a Marketing one). Here's an example

"Finance people don’t really want to make the company money over time. They just thrive on certainty and predictability. They try to make the world resemble their fantasy of perfect certainty, perfect quantification, perfect measurement.

Here’s the problem. A cost is really quantifiable and really visible. And if you cut a cost, it delivers predictable gains almost instantaneously."

> Choosing to spend three weeks on a feature that serves 2% of users is a €60,000 decision.

I'd really want to hire the Oracle of a PM/ Analyst that can give me that 2% accurately even 75% of the time, and promise nothing non-linear can come from an exercise.

I think the only thing that matters is whether the people on the team care deeply about the product; whether they care more about the product than their own careers (in the short term). Without that, any metric or way of thinking can and will be gamed.

Unfortunately, even with all the management techniques in the world, there are just some projects that are impossible to care about. There’s simply a significantly lower cap on productivity on these projects.

Making it solely about the extraction of dollars is a great recipe to make something mediocre. See Hollywood or Microslop.

Its like min-maxing a Diablo build where you want the quality of the product to be _just_ above the "acceptable" threshold but no higher because that's wasting money. Then, you're free to use all remaining points to spec into revenue.

I feel like there is a lot of nuance around this topic that is getting lost in the noise.

The direct and indirect financial impact of technical decisions are indeed hard to measure. But some technical decisions definitely have greater financial impact than others. Even if it's hard to precisely quantify the financial costs/benefits of every decision. It is possible to order them relatively. X is likely to make more money than Y. So we do X first and Y later.

There is a significant amount of chance involved in whether a product/feature will even make money at all. So even good plans with measurably positive expected value could end up losing money.

Just because it's impossible to be 100% certain of the outcome of any decision. Doesn't mean we should throw the baby out with the bathwater.

Still don’t understand what regular people (like the author) gain from selling how wonderful AI is. I get that the folks at Anthropic and openai shove AI through our throats every day, but nobodies?

Look! A guy built 95% of slack in 2 weeks! Very skeptical of that btw, but also an organization that justifies every single team by exactly how much $ value they’re generating sounds like hell. How would you ever innovate or try out new ideas? It’s important to quantify what impact your team is generating but there are some cases (e.g. UX) which are really hard to quantify in $ but are still very important for the product

The estimate cost number is for very large companies with massive overhead bulk. Dump the management overhead, the HR machine and other things smaller companies do not have and this number comes down massively.

I've been a software engineer for more than ten years and never cared about these kinds of topics. But lately, I've found them genuinely interesting. Could someone recommend books on the economics of software businesses? I can't take this author's content seriously.

The argument against platform teams needs to be balanced with the compounding nature of technical debt.

The argument to always go for the biggest return works OK for the first few years of high growth (though the timeline is probably greatly compressed the more you use AI), but it turns into a kind of quicksand later.

One interesting factor that I rarely see discussed is this: Let's say a DevOps person does some improvement to internal tooling and a task that devs had to oversee manually now is automated. Every dev spent about 2 hours per week doing this task and now they don't have to anymore. Now, have we saved 2 hours of salary per dev per week?

Not sure. Because it totally depends on what they do instead. Are they utilizing two hours more every week now doing meaningful work? Or are they just taking things a bit more easy? Very hard to determine and it just makes it harder to reason about the costs and wins in these cases.

With a long time in the industry and seeing how so many big software companies work, this really really chimed with me. Many/most teams and projects and busy work are not actually moving the bottom line, at massive opportunity cost! And there's so little awareness that most people in squads and their managers will think they are the exception.

Whereas Whatsapp with its 30 software engineers was the exception etc.

A chat with friends showed how there are parallels with how LLMs will happen in the short-term future - say the next 5 years - and the whole MapReduce mess. Back when Hadoop came along you built operators and these operators communicated through disk. It took years even after Spark was about for the hadoop userbase as a whole to realise that it is orders of magnitude more efficient to only communicate through disk when two operators are not colocatable on the same machine and that most operators in most pipelines can be fused together.

So for a while LLMs will be in the Hadoop phase where they are acting like junior devs and making more islands that communicate in bigger bloated codebases and then there might be a realisation in about 2030 that actually the LLMs could have been used to clean up and streamline and fuse software and approach the Whatsapp style of business impact.

I don't understand the urgency around quantifying every aspect of the software process. Surely, we are in agreement that money in must at least equal money out if the company is to be viable? This is a simple quickbooks report, is it not?

Why don't we instead focus our energies on the customer and then work our way backward into the technology. There are a lot of ways to solve problems these days. But first you want to make sure you are solving the right problem. Whether or not your solution represents a "liability" or an "asset" is irrelevant if the customer doesn't even care about it.

> This does not mean that Slack’s engineering investment was wasted, because Slack also built enterprise sales infrastructure, compliance capabilities, data security practices, and organizational resilience that a fourteen-day prototype does not include.

The LLM-agent team argument also misses the core point that the engineering investment (which actually encompasses business decisions, design and much more than just programming) is what actually got Slack (or any other software product) to the point where is it is now and where it's going in the future and creating a snapshot of the current status is, while maybe not absolutely trivial, still just a tiny fraction of the progress made over the years.

The "author" used someone's vibecoded Slack clone to justify his conclusions. I think he believes that the majority of Slack's value lies in the slick CSS animations.

I do agree with his thesis in the middle, about how the ZIRP decade and the cultures that were born from that period were outrageous and cannot survive the current era. It's a brave new world, and it's not because of AI. It's because there's just not enough money flowing anymore, and what little is left is sucked up by the big boys (AI).

Then let's disregard cost of running and maintaining a system for having exact financial feedback.

We do proxy measurements because having exact data is hard because there is more to any feature than just code.

Feature is not only code, it is also customer training, marketing - feature might be perfectly viable from code perspective but then utterly fail in adoption for reasons beyond of Product Owner control.

What I saw in comments — author is selling his consultancy/coaching and I see in comments that people who have any real world experience are also not buying it.

The 3-5x return threshold is the part most eng leaders never internalize. I've seen teams spend entire quarters on internal tooling that saves maybe 20 minutes per developer per week — nowhere near break-even, let alone a healthy return. The uncomfortable truth is that most prioritization frameworks (RICE, WSJF, etc.) deliberately avoid dollar amounts because nobody wants to see the math on their pet project. Once you attach real costs to sprint decisions, half the roadmap becomes indefensible.

I get "This site can’t be reached"

I see we're once again missing the existence of indirect impact. There's a reason organizations look at revenue/engineer overall instead of trying to attribute it directly to specific teams.

I guess his students get to relearn that on their own.

Also, any post talking about building software and then contains the suggestion that "cost per unit" is an efficiency metric needs to come to the red courtesy phone, Taylorism would like to have a chat about times gone by.

> The obvious objection is that code produced at that speed becomes unmanageable, a liability in itself. That is a reasonable concern, but it largely applies when agents produce code that humans then maintain. Agentic platforms are being iterated upon quickly, and for established patterns and non-business-critical code, which is the majority of what most engineering organizations actually maintain, detailed human familiarity with the codebase matters less than it once did. A messy codebase is still cheaper to send ten agents through than to staff a team around. And even if the agents need ten days to reason through an unfamiliar system, that is still faster and cheaper than most development teams operating today. The liability argument holds in a human-to-human or agent-to-human world. In an agent-to-agent world, it largely dissolves.

I keep seeing this assumption that "unmanageable" caps out at "kinda hard to reason about", and anyone with experience in large codebases can tell you that's not so. There are software components I own today which require me to routinely explain to junior engineers (and indeed to my own instances of Claude) why their PR is unsound and I won't let them merge it no matter how many tests they add.

> even if the agents need ten days to reason through an unfamiliar system, that is still faster and cheaper than most development teams operating today

Citation needed. A human engineer can grok a lot in 10 days, and an agent can spend a lot of tokens in 10 days.

>Given that software teams are expensive

In many companies there are 3 to 5 other people per developer (QA, agile masters, PO, PM, BA, marketing, sales, customer support etc.). The costs aren't driven just by the developer salaries.

A CEO can cost as much as 10 developers, sometimes more.

If you want to understand economics, I recommend watching some of Don Reinertsen's videos on Lean 2.0. He goes into a few concepts quite deeply that are quite intuitive.

Cost of delay: calculating the cost of delaying by a few weeks in terms of lost revenue (you aren't shipping whatever it is you are building), total life value of the product (your feature won't be delivering value forever), extra cost in staffing. You can slap a number on it. It doesn't have to be a very accurate number. But it will give you a handle on being mindful that you are delaying the moment where revenue is made and taking on team cost at the cost of other stuff on your backlog.

Option value: calculating the payoff for some feature you add to your software as having a non linear payoff. It costs you n when it doesn't work out and might deliver 10*n in value if it does. Lean 1.0 would have you stay focused and toss out the option for that potential 10x payoff. But if you do a bit of math, there probably is a lot of low hanging fruit that you might want to think about picking because it has a low cost and a potential high payoff. In the same way variability is a good thing because it gives you the option to do something with it later. A little bit of overengineering can buy you a lot of option value. Whereas having tunnel vision and only doing what was asked might opt you out of all that extra value.

A bad estimation is better than no estimation: even if you are off by 3x, at least you'll have a number and you can learn and adapt over time. Getting wildly varying estimates from different people means you have very different ideas about what is being estimated. Do your estimates in time. Because that allows you to slap a dollar value on that time and do some cost calculations. How many product owners do you know that actually do that or even know how to do that?

Don't run teams at 100% capacity. Work piles up in queues and causes delays when teams are pushed hard. The more work you pile on the worse it gets. Worse, teams start cutting corners and take on technical debt in order to clear the queue faster. Any manufacturing plant manager knows not to plan for more than 90% capacity. It doesn't work. You just end up with a lot of unfinished work blocking other work. Most software managers will happily go to 110%. This causes more issues than it solves. Whenever you hear some manager talking about crunch time, they've messed up their planning.

Stretching a team like that will just cause cycle times to increase when you do that. Also, see cost of delay. Queues aren't actually free. If you have a lot of work in progress with inter dependencies, any issues will cause your plans to derail and cause costly delays. It's actually very risky to do that if you think about it like that. If you've ever been on a team that seemingly doesn't get anything done anymore, this might be what is going on.

I like this back of the envelope math; it's hard to argue with.

I used to be a salaried software engineer in a big multinational. None of us had any notion of cost. We were doing stuff that we were paid to do. It probably cost millions. Most decision making did not have $ values on them. I've since been in a few startups. One where we got funded and subsequently ran out of money without ever bringing in meaningful revenue. And another one that I helped bootstrap where I'm getting paid (a little) out of revenue we make. There's a very direct connection between stuff I do and money coming in.

Using ‘blind’ to mean ‘ignorant’ is like using any disability label as a synonym for ‘bad’—it turns a real condition into an insult.