> 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 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.

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.

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?

> 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.

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.

> 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.

I get "This site can’t be reached"

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).

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.

> 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.

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