With all the emphasis on the speed of modern AI tools, we often seem to forget that velocity is a vector quantity. Increased speed only gets us where we want to be sooner if we are also heading in the right direction. If we’re far enough off course, increasing speed becomes counterproductive and it ends up taking longer to get where we want to be.

I’ve been noticing that this simple reality explains almost all of both the good and the bad that I hear about LLM-based coding tools. Using AI for research or to spin up a quick demo or prototype is using it to help plot a course. A lot of the multi-stage agentic workflows also come down to creating guard rails before doing the main implementation so the AI can’t get too far off track. Most of the success stories I hear seem to be in these areas so far. Meanwhile, probably the most common criticism I see is that an AI that is simply given a prompt to implement some new feature or bug fix for an existing system often misunderstands or makes bad assumptions and ends up repeatedly running into dead ends. It moves fast but without knowing which direction to move in.

I love this piece and read to the end. Currently working on an idea. Started with Claude and it made a mess. Now enjoying doing it by hand. It just feels easier! AI is assisting on blockers now noy writing code.

Anyway 2 areas I slightly disagree on.

Open source abandonware is fine. Sometimes people give up because they realize it is not a good idea. Or they get busy or sick.

And 10 years at a startup is great but that relies on it being a good startup. Entropy at companies means I have never made it to 10yrs even though I wanted to.

Awesome article, I feel a lot of people have also forgotten that good projects take iteration not 100 new features. To get few features to an excelent state it requires multilpe iterations at multiple stages. 1) The developer who does a task validates that their thinking was the correct one, they see how they changes impact the system, is it scalable? Does it need to be scalable? While you are working and thinking on it you get more and more context which simply wasn't there at the begining. 2) A feature done once (even after my perfect ClaudeCode plan) is not done forever, people will want to make it better/faster/smoother/etc. But instead of taking the time to analyze and perfect it we go onto the next feature, and if we have to iterate on the current one, we don't iterate we redo...

Really like the article I think it is awesome, and I strongly believe AI for coding will stay, but I also beleive that we need to still have a strong understanding of why we are building things and what they look like.

Excellent article!

Some things truly just take time.

You may speed them up when you start, but eventually, you will likely get to pay back the time. That is not necessarily a bad thing; it is just what it is.

AI makes us move faster, but if one is not careful, they may only be moving faster in the wrong direction, and they will eventually spend time moving back.

I find myself sympathetic to the author's PoV, but I am incorporating LLMs into my workflow, with a resultant jaw-dropping (to me) increase in velocity.

But I am not just dispatching to agents. I work interactively with a chat interface, and sometimes, I will just bin a whole hour's worth of back-and-forth, because we're not getting anywhere (in fact, I did exactly that, about 30 minutes ago).

But that hour is peanuts, compared to the ten hours that I would have spent, trying to figure it out on my own. With an LLM (and git), I can "run something up the flagpole, and see who salutes." I can afford to experiment with very large code bases, and toss out a whole bunch of stuff, if need be.

That said, I know damn well, that quite a few folks here, would sneer at my methodology, as "awkward, stodgy, and slow." Nevertheless, I am pretty chuffed with the results. Yeah, it's slower than some folks would do it, but the Quality is really high, and I'm happy with the results.

My favorite thing to do, is (for example) toss all 5 of my SDK files into the LLM, paste in the JSON server interaction, describe the bug, and ask it to help me figure it out.

Nine times out of ten, it finds the bug quickly. The real bug. I am not always happy with the proposed solutions, but finding the root cause is always the time-consuming part.

I've been working on a clone of Sid Meier's Pirates but with a princess theme (for my daughters).

I've been using AI to help me write it and I've come to a couple conclusions:

- AI can make working PoCs incredibly quickly

- It can even help me think of story lines, decision paths etc

- Given that, there is still a TON of decisions to be made e.g. what artwork to use, what makes sense from a story perspective

- Playtesting alone + iterating still occurs at human speed b/c if humans are the intended audience, getting their opinions takes human time, not computer time

I've started using this example more and more as it highlights that, yes, AI can save huge amounts of time. However, as we learned from the Theory of Constraints, there is always another bottleneck somewhere that will slow things down.

One of my favorite ideas from Nietzsche [1] is that civilizations take millennia to “digest” or integrate concepts. It seems a little obvious, maybe, until you look at the modern world and realize the baseline assumption is something like, “every problem is just a question of resources.”

An example being the common attitude that [advanced tech] is just a math problem to be solved, and not a process that needs to play itself out in the real world, interacting with it and learning, then integrating those lessons over time.

Another way to put this is: experience is undervalued, and knowledge is overvalued. Probably because experience isn’t fungible and therefore cannot be quantified as easily by market systems.

1. Probably not his original idea, and now that I think about it this is kind of more Hegelian. I’m not familiar enough with Hegel to reference him though.

> everybody who is like me, fully onboarded into AI and agentic tools, seemingly has less and less time available because we fall into a trap where we’re immediately filling it with more things

You fill a jar with sand and there is no space for big rocks.

But if you fill the jar with big rocks, there is plenty of space for sand. Remove one of the rocks and the sand instantly fills that void.

Make sure you fit the rocks first.

> We pay premiums for Swiss watches, Hermès bags and old properties precisely because of the time embedded in them

Lost me in paragraph three. We pay for those things because they're recognizable status symbols, not because they took a long time to make. It took my grandmother a long time to knit the sweater I'm wearing, but its market value is probably close to zero.

I've been hearing similar things from a lot of different directions. The underlying issue about "you cannot replace time" is one that is good to internalize early. A number of people I know who "missed" their kids growing up because they were working hard to make lots of money. You can't go buy "time with my kids when they were growing up."

Agentic coding very much feels like a "video game" in the sense of you pull the lever and open the loot box and sometimes it's an epic +10 agility sword and sometimes its just grey vendor trash. Whether or not it generates "good" or even "usable" code fades to the background as the thrill of "I just asked for a UI to orchestrate micro services and BLAMMO there it was!" moves to the fore.

Sounds familiar, for most of my life I have tried to remove all "friction" from life – applying that engineering mindset to make everything as efficient as possible. Only then I realized that life somehow is about that "friction".

My current project is the culmination 15 years of software development.

I started out building a full stack framework like Meteor framework (though I started before Meteor framework was created in 2012 and long before Next.js).

Then I ported it to to Node.js because I saw an advantage to having the same language on the frontend and backend.

Then I noticed that developers like to mix and match different libraries/modules and this was a necessity. The whole idea of a cohesive full stack framework didn't make sense for most software. So I extracted the most essential part of it that people liked and this became SocketCluster. It got a fair amount of traction in the early days.

At the time, some people might have thought SocketCluster was trying to be a more scalable copycat of Socket.io but actually I had been working on it for several years by that point. I just made the API similar when I extracted it for better compatibility with Socket.io but it had some additional features.

A few years ago, I ended up building a serverless low-code/no-code CRUD platform which removes the need for a custom backend and it can be used with LLMs directly (you can give them the API key to access the control panel). It can define the whole data schema for you. I've built some complex apps with it to fully prove the concept with advanced search functionality (including indexing with a million records).

I've made some technical decisions which will look insane to most developers but are crucial and based on 15 years of experience, carefully evaluating tradeoffs and actual testing with complex applications. For example my platform only has 3 data types. String, Number and Boolean. The string type supports some additional constraints to allow it to be used to store any kind of data like lists, binary files (as base64)... Having just 3 types greatly simplifies spam prevention and schema validation. Makes it much easier for the user (or LLM) to reason about and produce a working, stable, bug-free solution.

That said I've been struggling to sell it because there are some popular well funded solutions on the market which look superficially similar or better. Of course they can't handle all the scenarios, they're more complex, less secure, don't scale, require far more LLM tokens, lead to constant regressions when used with AI. It's just impossible to communicate those benefits to people because they will value a one-shotted pretty UI over all these other aspects.

You can check out https://saasufy.com/ if interested.

As for the tree analogy and open source.

Yes, you cannot build years of community and trust in a weekend. But sometimes it's totally sufficient to plant a seed, give it some small amounts of water and leave it on its own to grow. Go ask my father having to deal with a huge maple tree, that I’ve planted 30 years ago and never cared for it.

Open Source projects sometimes work like this. I've created a .NET library for Firebase Messaging in a weekend a few years ago… and it grew on its own with PRs flowing in. So if your weekend project generates enough interest and continues to grow a community without you, what’s the bad thing here? I don’t get it.

Sometimes a tree dies and an Open Source project wasn’t able to make it.

That said, I’ve just finished rewriting four libraries to fix long standing issues, that I haven’t been able to fix for the past 10 years.

It's been great to use Gemini as a sparring partner to fix the API surface of these libraries, that had been problematic for the past 10 years. I was so quick to validate and invalidate ideas.

Once being one of the biggest LLM haters I have to say, that I immensely enjoy it right now.

Speed is useful, when you have a good idea or a hypothesis you want to test. But if you are running in the wrong direction, speed is of very little value. With LLMs it might be even harder to stop and realize that you are creating the wrong thing, because you are not spending effort to create the wrong thing.

I work at FAANG, and leadership is successfully pushing the urge for speed by stablishing the new productivity expectations, and everyone is rushing as much as they can, as the productivity gain doesn't really match the expectations, and people overwork to make up for this difference. This works very well with internal competition and a quota system for performance ratings, with some extra fear due to the bad job market.

I feel this new world sucks. We have new technology that boosts the productivity of the individual engineer, and we could be doing MUCH better work, instead of just rushed slop to meet quotas.

I feel I'm just building my replacement, to bring the next level of profits to the c-suite. I just wish I wasn't burning out while doing so.

Sometimes to move fast - we have to go slow.

That's a mantra I learned when getting into technology.

Asking questions about how things work, why it is a certain way, or why a shortcut was made often give you far better insights than anything else.

Slowing down and understandng is great. With AI this is even easier. But choose wisely, brains get full.

Is this not an observation bias?

Consider the idea of trying to determine how quickly an unknown number of timers will go ping, It could be 10,000 timers that go ping when finished or 1,000,000 timers that go ping when finished. I don't know when they are going to go ping, just that they all the timers are running at different speeds spread over some distribution.

After one time period, 5,000 pings have been detected. Should you conclude that timers are pinging fairly quickly?

You cannot tell the overall duration of timers if you don't know the number of timers there are out there. Your only data that the timer exists is the ping, consequently you cannot tell if a small population is at high speed or a large population is at a moderate speed. In both cases the data you receive are the fastest of the population.

In other words we haven't yet seen what the 10 year project made using these tools is like (or even if it exists/will exist), because they haven't been around for 10 years.

What's faster now are the time-dependent factors of production - product development, go-to market, etc.

What's slower now are threats to production - even minor regulations take years or decades, and often appear only when workarounds have surfaced.

So what changed in the last 40+ years are the many tools for businesses to shape the conditions of their business -the downstream market, upstream suppliers, and regulatory support/constraints. This is extremely patient work over generations of players, sometimes by individuals, but usually by coalitions of mutual corporate self-interest, where even the largest players couldn't refuse to participate.

It's evolution.

"Nobody is going to mass-produce a 50-year-old oak."

Mass production of engineered structural lumber.[1]

[1] https://www.youtube.com/watch?v=RCYn3xQ0yS8

We value human ingenuity and effort. If there was a button "create an Oscar-worty movie" anyone could press it would make a paradox. The trick is that this won't render film industry useless, since we watch movies only when we believe they're worth our time, which is not true for zero-effort content.

Some things take time

And time takes money

Enough money to pay one’s bills while one tends the growing tree

And if we have a society that ensures everyone is given the dignity of time

We also get a society that reaps what some create with that time

But if we have a society that only rewards pushing money back up the hierarchy

Then we all lose our time and our nest eggs to those who have the most.

I've been slowly working on something for a decade, and I'm always comforted by this poem by Mary Oliver

Things take the time they take.

Don't worry.

How many roads did St. Augustine follow

before he became St. Augustine?

> everybody who is like me, fully onboarded into AI and agentic tools, seemingly has less and less time available because we fall into a trap where we’re immediately filling it with more things

I do wonder if productivity with AI coding has really gone up, or if it just gives the illusion of that, and we take on more projects and burn ourselves out?

On the contrary, you can solve the tree problem with money. There are nurseries that sell mature trees -- most people though will not choose to spend $20k on a tree.

Some of the items listed in the "takes time" list say the beginning are not great examples. They are better emblems of artificial scarcity, especially Hermes bags.

>Nobody is going to mass-produce a 50-year-old oak. And nobody is going to conjure trust, or quality, or community out of a weekend sprint.

absolutely although i wonder how different 'trust' is in the culture of tomorrow? will it 'matter' as much, be as cherished, as earned over the fullness of time?

i suspect it is a pendulum - and we are back to oak trees at some point - but which way is the pendulum swinging right now?

> I’m also increasingly skeptical of anyone who sells me something that supposedly saves my time.

Imagine a world in which the promise of AI was that workers could keep their jobs, at the same compensation as before, but work fewer hours and days per week due to increased productivity.

What could you do with those extra hours and days? Sleep better. Exercise more. Prepare healthy meals. Spend more time with family and friends. The benefits to physical and mental well-being are priceless. Even if you happened to earn extra money for the same amount of work, your time can be infinitely more valuable than money.

Unfortunately, that's not this world. Which is why the "increased productivity" promise doesn't seem to benefit workers at all.

If you look at the technological utopias that people imagined 50, 60+ years ago, they involved lives of leisure. If you would have told them that advances in technology would not reduce our working hours at all, maybe they would have started smashing the machines back then. Now we're supposed to be happy with more "stuff", even if there's no more time to enjoy stuff.

I love this, and it applies to a lot more than software and trees :)

> Trees take quite a while to grow. If someone 50 years ago planted a row of oaks or a chestnut tree on your plot of land, you have something that no amount of money or effort can replicate. The only way is to wait. Tree-lined roads, old gardens, houses sheltered by decades of canopy: if you want to start fresh on an empty plot, you will not be able to get that.

This is a bad start. Louis XIV at Versailles and Marly famously made while forests appear or disappear overnight, to the utter dismay of Saint-Simon, the memorialist, who thought this was an unacceptable waste of money and energy.

And this was before the industrial revolution. Today I'm sure many more miracles happen every day.

I don't disagree with the sentiment, but I think the signals that we use to determine whether we're doing the right things are different with the new AI enhanced toolsets.

Refactoring decent sized components are an order of magnitude easier than it was, but the more important signal is still, why are you refactoring? What changed in your world or your world-view that caused this?

Good things still take time, and you can't slop-AI code your way to a great system. You still need domain expertise (as the EXCELLENT short story from the other day explained, Warranty Void if Regenerated (https://nearzero.software/p/warranty-void-if-regenerated) ). The decrease in friction does definitely allow for more slop, but it also allows for more excellence. It just doesn't guarantee excellence.

yes, we want to avoid counterproductive things that are done too quickly

no we don't want to miss genuine ways to speed things up to improve our productivity so we can do other or more things

It's worth noting that just because something takes time doesn't mean it's automatically worth doing.

Vibe slop-ing at supersonic speeds and waiting years to grow aren't the only options, there's something in between where you have enough signal to keep going and enough speed to not waste years on the wrong thing.

I feel that today's VCs have completely disregarded the middle and are focused on getting as big as possible as fast as possible without regard to the effect it's having on the ecosystem.

I think it's hard to argue with the idea that we should slow down and think more, and that AI is pushing us to do the opposite. But time is limited, it's very limited. And at least in a professional setting, to spend time on the correct things is key.

What AI allow us is to do those things we would not have been able to prioritize before. To "write" those extra tests, add that minor feature or to solve that decade old bug. Things that we would never been able to prioritize are we noe able to do. It's not perfect, it's sometimes sloppy, but at least its getting shit done. It does not matter if you solve 10% of your problem perfect if you never have time for the remaining 90.

I do miss the coding, _a lot_, but productivity is a drug and I will take it.

> We know this intuitively. We pay premiums for Swiss watches, Hermès bags and old properties precisely because of the time embedded in them. Either because of the time it took to build them or because of their age.

Oh, I thought it was because they're a way to show off about being rich.

> We require age minimums for driving, voting, and drinking because we believe maturity only comes through lived experience.

Even if she could reach the pedals, my 4yo doesn't have the attention span to drive. This isn't a "lived experience" thing, it's a physical brain development thing. IIRC the are effects with learning math, where starting earlier had limited impact on being able to move to certain more advanced topics earlier; ie there's more going on than just hours of experience.

The standard age for voting is also the age for being a legal adult. There are sound logical reasons that these ages should match.

The standard drinking age is due to pressure by activists, and AIUI is lower in other countries.

Also you know, for programmers, say a 3 day work week is right there up for grabs. Even still employers would see big productivity increases.

Can someone ELI5 what Earandil purpose is?

lots of things take days, not hours. And idt AI changes that much. It does let you (or - let's be real - your middle management) try to make it happen with hours tho :P

great article. reminds me of the saying “9 women can’t make a baby in a month”

I feel for the larger companies and the people who started 10 years ago, though.

They have spent the last decade building processes and guardrails for getting consistent average performance from people. But now, some talented people who worked at those companies are building their own new companies without the overhead and moving much, much more quickly.

I think what we assume is "vibe slop at inference speed" is not as simple as people make it out to be. From a perspective, I think generally it might be people trying to save jobs.

I'm seeing more slop come out of larger, older companies than the new ones (with experienced operators).

And the speed is somewhat scary. For smaller team it doesn't take as much effort to build deep, beautiful product anymore.

The bottleneck was never the ability for a engineer to code. It was the 16 layers between the customer and the programmer which has vanished in smaller companies and is forcing larger ones to produce slop.

"So welcome to the machine"

I'm reading Against The Machine by Paul Kingsnorth, and now reading this blog piece is hard not to make connections with the points of the book: the usage of the tree as a counter-argument for the machine's automation credo exposed in the blog post very much aligns with I've read so far.

Go slow to go fast.

We're gonna have to learn some lessons from other engineering fields in this regard. Electrical, civil, mechanical, aerospace... They've all had to put processes in place to intentionally slow things down for a long time. I could throw a circuit board layout together 1000x faster then a team of engineers could have 50 years ago, but that industry has developed a culture of rigorous review processes to ensure quality, which means I couldn't actually move nearly as fast as possible.

Undoubtedly a lot of that comes down to production cost and safety. A plane is far more likely to kill people and it costs a shitload more to produce then an app (though plenty of software is mission critical). But now in software we can move quick enough up front that if we don't start applying some discipline it's going to bite us in the ass in the long run.

Everyone is obsessed with speed.

But no one wants to go out of their house.

Social connections. Trust. Facetime. All matter more than ever.

Want a moatable software business? Know your customers on a personal level. Have a personal relationship. Know the people that sign the contracts, know their kids names, where they vacationed last winter, their favorite local restaurant.

Get out of the house.

I don't see the problem - everything the author describes has, and will always be, true. You can't vibe code anything of value in a weekend exactly because anyone _else_ with the same level of experience can do the exact same thing in the same weekend! This has always been true across all trades and technologies. Once again, the domain expertise, wisdom, and simply _time_ of doing something always win. LLMs literally don't change that at all.

https://simplytrees.com/

“The power of doing anything with quickness is always prized much by the possessor, and often without any attention to the imperfection of the performance.”

Was hoping this wasn't ai related, disappointed

> We require age minimums for driving, voting, and drinking because we believe maturity only comes through lived experience.

Not true, we do this because the 99% of the time it's true, however there are people who would be perfectly competent and responsible to drive without living to the age of 16-18. Same with voting, there are humans who have a deep understanding and intelligence about politics at a younger age than suffrage. Equally there are people who will be reckless drivers at 40 and vote on whim at 60.

We have these rules not because sophistication only comes through lived experience, we have them because it's strongly correlated and covers of most error cases.

To take this to AI, run the model enough times with a higher enough temperature, then perhaps it can solve your challenges with a high enough quality - just a thought.