Personal example: I had a software engineering colleague who was the best coder of financial management systems I've ever encountered. He gained these skills through years of in-the-trenches development. One of the things he told me, and that I also observed, was that the vast majority of financial experts (basically, the people in the accounting department of companies) had an extremely difficult time just telling him what the rules of any particular transaction should be. But what they could do was tell him whether the handling of any particular transaction was right or wrong. So often times he would sit down with these accounting folks and go through lots of example transactions he came up with, and from there he essentially built up the requirements spec.

In my experience, that is the primary difference between people I've known who are good software engineers and those who aren't: people who can specify the detailed rules of any system, vs. folks who take a "well, I know it when I see it" approach.

I have a strong suspicion that folks who have a high degree of domain expertise in a particular area will fail as software builders even in an agentic world because they will struggle to elucidate clearly the rules in their head that they've learned over years. As an analogy, it's kind of like asking a native speaker for the grammar rules of their language. Often times they can't, but they'll just say "well, that sounds wrong." They may be "domain experts" in their language, but they'd have a hell of a time prompting an AI system on how to grade a test for grammar correctness.

A mental model I use for this is that we have users, builders and experts when creating AI products. For most individual use cases a person is the user, builder and expert: I make a prompt about how to write code in a way that I like that I will later use. Coding agents moved into the direction of builders that were also experts in the field iterating on a product for third-party users. The next frontier will be finding the right patters for teams to capture expert knowledge handle the collaboration between engineer/builders and experts. Just having PMs handle that interaction will be a super bottleneck.

My cofounder and I are actually working on a project in that direction (https://www.getvalmar.com) --> we'd love any input on how engineers prefer performing feedback loops and getting input from subject matter experts :)

Here's an example I encountered last week:

Someone in my neighborhood is a 75 year old chemical engineer who likes computers and got into Linux a few months ago. I see him from time to time when walking around, he's a nice guy and overall has a scientist's mindset. He doesn't make a lot of assumptions and tries to think things through but sometimes he has big blind spots in unfamiliar fields.

I helped him install Linux and also hook up an SSD to one of his older machines and now it flies.

On his own he had an old sound card that he wanted to use on that machine. He asked me if the card is compatible. I told him it almost certainly is because the Linux kernel has drivers for a ton of devices. His motherboard's built-in sound card was fine but he likes tinkering with audio in general.

He managed to physically install the card correctly but called me and said there's no sound playing. Then he says he spent 12 hours troubleshooting the issue, using ChatGPT and Googling for assistance.

Over the phone he told me he tried many different things. Installing, tweaking and configuration ALSA, PulseAudio and PipeWire related tools and tweaking everything you can imagine. Nothing worked, no matter what happened, it never played sound through his speakers.

He asked me if I could come over to help so I did.

In 30 seconds I solved the problem.

I went to his sound settings in his desktop environment and saw that his sound card was being picked up. I looked at the back of his machine and noticed this card had 2 black ports with the bigger style jack for headphones. There was no usual green port which is usually used for output. I shined a flashlight to look closer. One of the black ports was labeled headphones and the other was unlabeled. His was connected to the unlabeled one. I swapped it to the other port and everything worked right away.

All of that to say, as a software engineer I have second hand embarrassment that a trillion dollars invested into AI didn't think to respond with "did you double check to see which port you connected the speakers to?". I asked him if AI ever suggested that and he said no, it immediately went into polluting his system with a bunch of unnecessary tools and chasing incorrect rabbit hole after rabbit hole. AI understands nothing.

I was on a fishing trip. I asked the charter if he’d want to check out a free app I work on (https://oceanconnect.ca) in case it might be useful for his work.

I don’t know how people on the ocean use ocean data. I don’t really know what they want to know, or why. I wasn’t totally prepared for the incredible onslaught of questions and information pertaining to how people use the data or what we can do with the data, and it was so cool and exciting to get that perspective.

It was a good reminder that models are not the same as the systems they abstract, and knowledge to develop them has almost nothing to do with using them. This guy was a wealth of knowledge about how they use weather data on the water. In a sense, he knows more about the data than I do (even if he doesn’t realize it, or doesn’t understand it in its digital representation), and would be far better equipped to make a useful application for people like him if he could program.

I found myself thinking people like him could actually do amazing stuff with LLMs if they sat down and got their ideas out on a screen. I’d really like to interview people on the water daily some day to refine the product if we ever have the funding. That domain knowledge is highly, highly specialized and people know things you’d never guess after living in a complex domain for decades.

The places it falls flat is domain expertise that isn’t well documented, like specific business processes. Knowing something will fail because X in dept A won’t like it and will undermine it (politics) or some silly process I didn’t know about forbids it.

So it’s not domain expertise, it’s idiosyncratic expertise that shines these days. Knowing where things deviate from a domain or the standard and being able to adapt around that. Years ago there’s a group I worked with and I was at the mercy of about 3 people I could ask questions to in order to make sure I was doing things correctly because digging into that domain was time prohibitive. Now, I can sit down one evening and dig fairly deep into a domain. I can understand common practice, approach, acronyms, nomenclature, so on. I can find other popular competing systems. I can rapidly figure out what I need.

The same is true for software, agents don’t collapse software they help people build fairly usable systems but they don’t find the expertise a senior level engineer has where designs or implementations may fail in practice. The idiosyncrasies of software and software systems, especially in your very specific set of constraints you need to operate in that may deviate from the rest of the world.

This has always been true since the dawn of programming.

If you’re a great generalist software engineer today, you aren’t jumping to some random domain to escape AI. Software is your domain. You’re sticking with it as it expands and transforms.

- is the app is properly deployed - how will the release cycle be - is it secure? - can we run two instances of it without messing up the orders/routes/whatever? - will we spend 5k/month in vercel if people start using it - how will we notice service degradation - if we change the data do we have downtime? how do we schedule that downtime window. - where is the code stored? can the team access it? - how are new contributors onboarded? - does the app use credentials and where to store them? - does the app manipulate or store PII? - if the user refreshes the app does it generate a duplicate order/route/whatever? - if there's an upstream service are we making sure our timeouts are properly configured? - if there's an upstream service are we making sure our connection pool is properly configured? - do we have a max connection lifetime so that middleware like AWS NAT or ALB don't leave us with dead connections in our pool?

I think that makes the point clearly. Also it may explain why software developer jobs are currently on the rise despite SWE-Bench-Pro-Ultra-Magic has been maxxed for months now.

I even went as far as setting up their IDEs, configuring their environments, and encouraging them to just vibe-code. It seems that the mental friction involved in switching domains is too high for most people to justify the reward. Perhaps the reward itself is not compelling enough, or perhaps this is simply the limit of adult motivation.

I started programming with Python around the time GPT-3 arrived, when Cursor had generous free tiers and excellent starter plans. A few Raspberry Pis, laptops, desktops, and countless hours of tinkering later, I discovered how much I enjoyed solving problems with software. There is so much to learn from the programming world: the concept of open-source software, the idea that people from anywhere in the world can collaborate on the same codebase, and the fact that many do so with little or no expectation of reward.

As this post points out, in the project I am currently working on—a comprehensive Clinical Decision Support System—it feels almost second nature to translate the rules, hidden rules, social dynamics of hospitals, and the common mistakes that we and our juniors make every day into software. Taking those observations and turning them into systems that work is surprisingly intuitive.

Perhaps the most valuable thing I gained from medical school, combined with my own personality, is the desire to keep learning. I naturally gravitated toward systems thinking, and the path forward seems clear to me: become a true expert in whichever specialties I ultimately practice, while simultaneously becoming highly skilled at systems thinking.

As for systems thinking itself, I find it useful to create rules not only for the codebase but also for the testing harnesses and development processes around it. The goal is to build systems that can enforce quality automatically as the codebase grows, ensuring that standards scale without requiring constant manual oversight.

It will never be organized, never fully optimized, and it will always be custom — because it has to cater to the reality of wildly different tastes, contexts, and localities. There may be some good tools, raw materials that show up once in a while.

This has always been the most valuable person. A skilled software developer who is also deeply experienced in their target domain is untouchable. They can always move the ball forward at some rate, assuming they're making any attempt at all.

It takes a really long time to absorb some domains. The most painful one I've experienced is the banking industry. It took me a solid decade in the trenches before I felt comfortable running a status call with a bank's operations staff without a babysitter.

I like to think of this as a sort of cube of capability. The X axis is technical expertise, the Y axis is domain expertise and the Z is physics (ai/tools/computers). The volume of this space is what we are interested in. Scaling up to a wild amount of AI capability with the best developers on earth (infinite x-z plane) is still going to perform like shit (zero volume) if you have no practical domain expertise available.

Specificity, availability and recency are perhaps the most important parts of domain expertise. None of these tend to be in ample supply with frontier language models. You can get a general sense of how a bank operates from chatgpt, but the FDIC would take over your bank in the middle of the first night of operations because you didn't learn the secret handshake (how to interact with your customers, competitors, regulators, etc).

Okay, but I've always gravitated towards working on tools and libraries and back-end stuff; as much as possible, the domain for me is software.

I'm an infra/admin jack of all trades with a comp sci degree and have been a hobby programmer for 30 years. I have a Lichess rating of 1000 on a good day.

We tried doing a chess bot competition (open book, use AI to program it, pull in opening books, end game tables, whatever, free for all) and I absolutely stomped him, but I've only beat him in real life over the board twice in 20 years.

He will beat 99% of random players in real life, and I will beat maybe 20%.

I'm not sure what I'm trying to say, but it seems to me that maybe domain knowledge isn't everything anymore? Or the domain itself has shifted?

There are plenty of things which are "trivial" to produce with no moat and yet are still million dollar businesses. Kebab stands. Water bottles. Barber shops. Movers.

What looks like _'expertise'_ may actually be pattern recognition built through repeated practice. I do believe that is what a model can already do faster than humans.

So, to me, we've got to be cautious here in that what this post implies is humanity must strive to be in the 99.99th percentile of domain knowledge to 'beat' the model. This is perhaps problematic.

I do think focusing on judgement is the only tact discourse here, and I do think that is fine. Once we have invariance well-defined and covered in suites of various types of testing strategies, and are focused on regulation, wouldn't that be a more doable objective?

The company I work for is currently trying to accelerate internal AI adoption, and recently laid off people to help force it. As I've written here before, this merely pools accountability (not removing it) and things will break in unexpected ways as people are not domain experts in these new areas added to their jobs.

I wonder if we will see a large reversal in a few years, or if AI will somehow be able to fix this too

After spending the last 5 years building software for venture capital and private equity, this blog post really resonates with me. Writing code is by and far the _easiest_ part of my job; understanding the financial engineering and nuance behind what my company's customers need from us the tough part.

We always joke that we'd rather hire a senior fund accountants and teach them to program if we could, only problem is there just aren't any of these folks around. Teaching an engineer to understand the minutia of fund accounting well enough to build software for these firms is tough.

Sure it lowers the bar, and some people will design decent things, but mostly these things will become mission critical and broken at the same time.

In the past year we've seen these non-technical analysts become more productive when it comes to developing internal tools, by leveraging AI models for the dev part.

Prior to this, pretty much everything was developed in Tableau. It was the most accessible way for non-devs to build working tools.

Just the other day one analyst in our group presented a tool he had been working on, which was basically a port of a tableau report, made into a more flexible app.

After looking through it, the database design was a mess. Some features worked, some didn’t. I explained the missing pieces and why things were breaking. Like OP said, he’s the domain expert.

I used billions of tokens last month alone. The tools are getting better fast. But giving AI to a domain expert doesn’t mean you no longer need software engineers.

A domain expert can use AI to build software. And a software engineer can use AI to learn about the domain. Both bring different expertise to the table.

Meanwhile, as a generalist who has a basic understanding of general things, everything from how to design efficient network protocols, to how cache lines affect the performance of sorting algorithms, without being a real expert in any of those things, I act as a constant course correction for AI agents doing work on my behalf, in a way that LLM context windows simply cannot replicate.

To give a concrete example, I recently used agents to build a specialized sync protocol that broadly resembles Dropbox. It's nowhere near as efficient in terms of how blocks are synced (because it entirely happens on a LAN and the cost difference is minimal), but I constantly had to make objectively more valuable course corrections on how the sync actually traversed the participating nodes. If I'd just let the LLM drive, it would have come up with a reasonably efficient algorithm (better than I probably would have done on my first try in the same timeframe) that would have had an obvious (to me) single bottleneck.

It helps if you can think at the system level and understand how everything is fitting together, why certain things are better than others, etc. Domain knowledge in multiple domains helps for that. That's what it means to be a generalist. And that includes having the prior experience with having built different types of software. Understanding architectural patterns. Being aware of pros/cons of different solutions. Etc. You don't have to be a specialist in any of this. But you do need to understand things at a high level.

Being a generalist equips you to enter new domains quickly. I've done lots of consulting on search projects in the past two decades. Every project is different. But the technology stays the same. I've built search engines for dating websites, maps, addresses, material scientists, art work, etc. Every project, most of the work is figuring out what the product is about. What good search looks like in that domain. And what they are doing that is sub-optimal that needs fixing. You can't work on search ranking unless you understand that. And you only have a few days to figure it out.

But Generalizations aside I think people greatly under estimate how rare is the ability to reduce complex subjects into concrete steps that someone else can follow, human or machine.

Go ask your grandma for a recipe you will find that it never turns out the same, giving her Claude Code is not going to change that.

Having worked across many disjunctive domains the value of "expert in domain" is greatly exaggerated. It's not like search engine doesn't exist nor that it cannot be easily absorbed. Quite often becoming expert is as simple as careful reading API documentation for existing, same domain system.

The piece is heavily one sided and don't recognize the problem of low reliability software: e.g. imagine a software designed in a way that it keeps critical piece of data as a global static variable. This will always work for a single user but will leak with two. Make it semi-critical and e.g. a part of profile loading.

Just as non-expert cannot verify if result is correct from a domain perspective, expert in domain cannot verify the output based on the basic principles (data security, safety, isolation, persistence, etc.) or even know that monetary values shouldn't be kept in floats.

Nothing like article claim had changed outside of a false promise of being productive.

Software engineers without domain expertise can fake it with unreliable results and experts without swe skills can fake it with unreliable results.

I’d suggest that the domain expert partner with a GenAI senior engineer to build together. In fact I believe this is the new dev team model. Domain Expert + Senior Engineer + QA. Not sure we still need a project manager anymore and we certainly don’t need scrum masters.

First being good developer and learning how to use AI was sufficient, next it was being able to design architecture, then it was “taste” that made all the difference and now being an expert in the domain is the only thing that matters really.

Until AI is basically in a stable, predictable, state of improvement or stagnation, these takes will continue to be pointless and most likely completely wrong.

This is a failure mode that senior engineers have seen a few times throughout their career: They know how certain choices will play out over time... and the kind of problems and roadblocks these choices might cause.

Nah, we’ve always produced software without much understanding of the domain. It’s the premise behind lean: we don’t know much, so get something in front of customers and refine it.

So I don’t believe there’s been a strong decoupling here akin to the degree that understanding the code and writing the code has been.

Writing software has never been difficult. It is the domain that has been the issue. Always.

I believe there are domains that are very well encoded. The model very often can know that a shift can't be longer than 11h and if you ask an agent for scheduling software it can surprise the developer by encoding that rule.

Both domain knowledge and coding skills became cheaper.

It might depend on the domain. Highly regulated domains like finance have entire books around how they should work.

However, I agree that verification skills became more important in both areas. A domain expert needs to catch 12h work shifts and experienced programmer needs to catch when the LLM accidentally put a route in a section that doesn't require authentication.

Both require some kind of harness and automatic verifications methods.

Obviously it is a very different kind of track, take a long time to develop and means you are no longer programming but then with LLMs, hand rolled programming has been massively reduced anyway.

The real moat I believe is the ability to hold the the problem in the head, isolate it and mentally design a way to structurally solve it iteratively.

Very few people have it. Much less common with domain experts.

I would rather bet on educating domain to the engineer than teaching a domain expert to architect software.

I say this as someone who uses AI a lot. Its still a far cry from cheap, especially with that pesky “working” word in there.

AI is going to struggle at building a consistent internal model of the domain into the software unless you’re able to give a structured explanation of the domain.

If you’re just giving it a set of inputs and expected outputs, it’s not going to generalise well and fail at out of sample input, unless the AI already understands the domain from its training set.

Being able to give a structured explanation of a domain (and being able to judge if the internal model of the software makes sense) is not the same as having experience in a domain.

Lots of ppl with domain experience can tell a right output from a false one, but can’t tell you why.

(Agree with the article’s general sentiment - but just wanted to make this tangential comment)

And if you think LLM are (or will be) good enough to not care about software part, what makes you think that your domain will not be completely resolved by AI?

If the inputs and outputs are only and exactly those of the domain, sure. But software is more than that. And your logistics operator (or my actual work example: our extremely talented designers with deep understanding of our product) can validate parts of the agents output, but the rest of it they can’t and it makes a mess.

I’m sure this will change, but it hasn’t yet.

It's absolutely true that domain knowledge is incredibly useful, and developers aren't always great at gaining it. But there's also something about decomposing systems into their component parts, understanding algorithms, and knowing how code works that's also incredibly useful, even with agents in the picture. A really good developer needs both of those skills.

Take that example, of the generated shift that's illegal (by coincidence, I do freight optimization and work with examples like that in my day job). A domain expert will know the specific example is illegal. So they'll tell the agent to fix it. The agent will probably fix it for that case.

How does the domain expert then know that the agent has produced a thorough fix, as opposed to just that scenario? Not because the agent says so. So it is because they test it manually (but which cases)? Or because they review the strategy of the agent's tests, and know how the algorithms work, and know the edge cases that the tests need to cover? But they can't do that, by stipulation, because they're not experienced with code, they're just using the agent.

So yes, if the agent gets to the point where it can design robust software that avoids edge cases in a complex domain, doing complex operations and is thoroughly tested, and so on, then half of my skills are going to be irrelevant.

Out of the box, agents don't do that today. Perhaps they'll get to that point, but until then, your knowledge of where to put a semicolon has become less useful, but your ability to specify and test processes precisely has not.

But yeah, knowing your domain well is a damn good idea.

I don't think that's the moat.

Using AI to more rapidly learn a domain will help in the short term

But in the long term, all moats will evaporate

In the past few months, I've used agents to brute force and reverse engineer solutions to problems I would never have economically have figured out on my own. I did it by putting agents in loops, connected to hardware and the internet, reading technical documentation, and relentlessly trying.

The code was shit. But it's much better to start with working shit and make it correct than spend weeks frustrated that nothing works.

I get that being a domain expert and instantly knowing the output is shit is important, but even if the output looks great, the code can be shit, and it takes looking at the code and knowing something about it to figure that out.

The solution to shit output is not (always, sometimes it is) just another if statement.

Even in a very well specified OSS effort, where I have some expertise, and I carefully reviewed the AI's output every goddamn step of the way, bugs slip through that the agents confidently tell me can't happen, and when shown proof they… add just another if, instead of really questioning assumptions.

You either know what you're doing, or you don't.

Not yet.

We won't be there until AI is more like a virtual person, where the domain expert trains the AI in a similar manner to training a real person.

At this point, agentic coding only eliminates the engineer when creating very simple applications. Once the application gets complex, either the domain expert needs to become an engineer, or an engineer is needed.

Yes, and the Big AI companies are currently hoarding data about all domains out there.

That person has zero skill in actually making tight automation that doesn't just fall over. And I have yet to see an AI agent that tells them "look, your requirements are contradictory, given this and that, these two cannot coexist".

Those little sycophants will just go and try to please the domain expert and placate him in all ways possible. Bend backwards rather then forcing them to reassess their assumptions.

If you have particularly specific knowledge in pretty much any domain, combining that with AI can lead to huge gains.

revelations never stop coming do they

Yes, and its price law all the way down to the metal, hasn't it always been?

I think this article is stating the obvious. In software, it has always been a requirement to learn the domain, and then capitalize on that in any way the software can be written (by hand, as a tech lead, or managing others, or lately, using ai).

My suspicion is we are still moving up along a continuum of capability.

Models didn’t used to produce coherent sentences (GPT-2 era) and now they can. Past models (GPT-3 era) made syntax errors and now models can write well structured code. Past models didn’t reliably emit correct syntax to request a tool call, or track context across multiple tool calls - and now they can.

Frontier models can’t write code without glaring security flaws, even as they already follow other best practices. So on those two criteria of code quality we are still in need of models improvements.

All these forms of correctness lie along a continuum. Today’s models can’t assess what’s needed in a domain for work to be “good” - but if current trends hold it’s just a matter of time.

So far the evidence seems to be pointing to a different adage, Sutton's Bitter Lesson, which (generalized) says to not bring human expertise to a problem that can be "solved" with unfathomable volumes of data. Because the latter has historically slaughtered the former for decades. But somehow people believe this time it's different?

I will counter there is one thing that is a persistent moat, and it's not domain expertise; it's sales. Convincing other humans to part with their money. Humans have shown they will trust a person/human touch to part with their money more than an AI.

But I'm not convinced today's AI or tomorrow's won't be able to replicate domain expertise in domain X for any X.

It takes a LOT of time to validate every path, possibly an infinite amount of time, depending on the complexity of the domain.

Successful software results from the intersection of expertise in two domains: the application domain, and software engineering.

I'm not sure that even that will remain as valuable or work as a viable moat.

We live in an era of corporate consolidation and absolutely, positively having to meet the revenue target. We also have invested literal trillions of dollars into the AI technologies that made the first skill the author mentions less valuable. However, the result just isn't there. Like the author says, there's a need for domain expertise.

However, you had a bunch of investors plow that trillions of dollars into the current AI boom with the understanding that they could, at the very least, take anyone and have them create what used to take an experienced software engineer, and in far less time and cost, and they invested thinking that the corporate oligopoly would deliver this. They'll now do anything to get that money back. Anything.

If that means telling the corporate oligopoly to tell customers that they need to expect less in the way of domain expertise from the models, well, they'll do that. And since there are relatively few players (the literal meaning of oligopoly) and they all have incestuous financial relationships with each other, they have incentive to hold that line as an entire industry. Development of better tools to create better domain expertise models would take even more money, which the investors don't want to provide, and, short of soaking the public investment markets, can't even find the cash for. Thus, the customer has to lower their expectations if the investors are to not lose their asses on the AI bet.

Something has to break.

Domain expert can develop working code, but they will not be able to ensure above.

Bad luck for the remaining ones.

What’s the truth, though? Are we still relevant?

My experience is that three years ago, when this kind of AI work first started becoming usable, I had to talk to the AI a lot. I had to review a lot. I had to change a lot.

These days, I talk to the AI less and fix less, while the amount and quality of the output we make together has gone up and the time required has gone down.

That suggests to me that AI is like a coworker coming up through the ranks. At first, it was like a capable, hard-working junior: useful, maybe even like a small team, but still making lots of mistakes and needing a lot of communication. Now it’s mostly on board. It almost always knows what I’m talking about, but not always. I have to fix less, but taste, architectural judgment, and domain knowledge still matter.

I’m aware of the value of my domain knowledge in browser instrumentation, and the nuances of CDP commands that may never have been documented anywhere. The commands are documented, but their quirks, behaviors, and the way you can combine them to create a working system are not. I can still suggest things to agents that help them.

I don’t know if that gap is closing. I do know that I’m learning less new domain knowledge because I don’t have to be in the code as much. But I also know my hard-won technical nuance and architectural lessons still matter. Maybe agents will eventually be able to hit iteration repeatedly until they figure all of that out. That seems more likely as they get more capable. But that’s still a hypothesis. I haven’t seen it directly yet, just a vague sense of where the capability is going.

With advances in memory and the models themselves, I don’t see why they don’t end up with something like that. And I agree with the top comments: the goalposts are always moving for the people trying to redefine their own relevance in a changing world.

The main pattern I’ve noticed in myself is that I spent years, really a decade, chasing down random bugs in the web platform, JavaScript frameworks, and browser instrumentation. I was very deep in that for a long time. That helped me build the products I built.

But over the last three years, I’ve started growing in a new direction: big-picture business, go-to-market, sales, and marketing. I guess that’s adaptation. You spend a decade building technical IP assets, and then you can build more of the same because you have the domain knowledge, while working with agents to massively increase the speed of production.

The situation feels analogous to having hired a small team of capable juniors three years ago who have now grown into A-players. If that had happened, we’d have the capability we’re operating at today. It’s just that we’re using AI and paying a lot less for it.

That’s my experience building a set of large, highly nuanced technical tools around the web platform.

AI changed the shape of my company. I migrated into a role where I’m not just doing the taxes every year or writing all the code myself, but thinking seriously about marketing, GTM strategy, and sales. For me personally, my evolution is in that direction now.

That doesn’t mean I’m not still growing in product sense or technical judgment, but it’s very different from the deep technical stuff I was in before. Now I’m freed up to focus on other parts of the business.

A fun benefit is that I get more time to rapidly build things that interest me: little side bets that may just be fun, or may actually become cool products.

The transition into sales and marketing is new to me, but I welcome it in 2026.

I think AI may be easier to deal with if you have your own small company than if you’re watching it affect your job inside a workplace. I’m not sure. I’ve heard people say good things about that too.

I’m not making an argument. I’m not trying to convince anyone. I’m just sharing my experience.

If you ask me and a logistics dispatcher the task of building logistics dispatching software (whatever that is), I will get there first.

Knowing the caveats and pitfalls of this through years of (often-painful) experience is what, at least for me, allows me to preempt a lot of the sloppy assumptions or omissions that even the frontier models make when working on systems at scale. This means I can leverage my domain expertise on these high-level areas while delegating the grunt work that is harder to screw up to the agents. I find this enables me to work faster while avoiding the slop making its way into critical engineering decisions.

so it takes a domain expert to remove unnecessary things, similar to how stone carvers create by removing material, not adding

But that was never the hard part!

Come now.

After twenty plus years as a professional software developer I can name two hard problems, not more. One is related to the article, the other is not:

1. Getting that clear idea out of a stakeholder's brain. Traditionally this would be a specification but doesn't need to be that formal. Remember, remember the first panel of https://i.redd.it/i2aeyrivmjoz.jpg An LLM doesn't help here because it doesn't push back. It'll do whatever you tell it to do even if it's not what you really wanted. The software developer here operates very similarly as a translator and it always has been true a translator who speaks both sides well will be able to do the highest quality work. This is not at all new. It always has been the advice that if you know things like, say, logistics and software or any such pair then you'll be well off financially either because you can do this translation well or because you realize what's missing and can do a product for it.

2. The other problem, of course, is debugging. Since LLMs fundamentally work from a training set any debugging problem not blatantly obvious to a sr developer is hopeless for them.

They really do want to know the ins-and-outs of the HVAC service business, for example, because they hope their agents will be handling it in a few years.

I mean, they could ask an LLM "what does this code do, and will it always X when Y", but that's just nesting the verification problem inside another verification problem.

1) Problem Domain Knowledge: This is what people generally mean when they say "domain expertise". This has always been and always will be the moat with/without AI. Simply because this is what understanding and modeling a problem is all about. It abstracts the key concepts/ideas and their relationships in the problem domain and builds a coherent model. This model embodies a set of functionalities with bounded scope and clear assumptions.

2) Solution Domain Knowledge: This is the implementation domain for the above problem. The model arrived at above gives the requirements which must be mapped to concepts/ideas and their relationships in the solution domain. When our implementation domain is a computer system, this takes the form of architecture, algorithms and data structures. The probability of a good solution here is directly proportional to how good a model we were able to construct in the problem domain above.

Albert Einstein;

"The mere formulation of a problem is far more essential than its solution, which may be merely a matter of mathematical or experimental skills."

"If I had an hour to solve a problem, I'd spend fifty-five minutes thinking about the problem and five minutes thinking about solutions."

With my sincere apologies to the author if I'm wrong, I'm pretty darn sure this was written by AI.

Guys, c'mon. I don't get it. It's one thing to have an AI write code for you, because code is ultimately functional. At least in the general case, the primary purpose isn't to express an idea.

Prose is different. Your writing represents what you think. You are your writing. Why would you outsource that?

I don't get it! Unless you're a (cheating) student, or you're writing marketing drivel.... what is the point? Just don't write the blog post. It's okay. Telling the robot to write the blog post doesn't accomplish anything. I don't care what a robot thinks!

I'm sorry, I'm just getting really tired of AI generated articles on Hacker News. Please, please don't outsource your own speech.

My take is much less charitable. I think a lot of senior devs are lonely and enjoy talking to chatbots all day. Saying it amplifies their productivity is a justification.

But they know nothing about the scaling, performance or maintenance of a system that will inevitably come up in production.

They also can't tell if the code created is maintainable, or unmaintainable sphagetti code.

What happens if there is a race condition, or a memory leak?

So AI can easily replace the domain knowledge of software engineers but not of evey other profession?

Coding is not engineering but I'm glad that we will finally be able to prove that definitively thanks to AI. It's going to be a bumpy ride.

Any software engineer who has built software to solve domain problems in multiple industries knows that the engineering domain knowledge and systems thinking approach is far more difficult to attain than industry-specific domain knowledge... This is why there are software consulting firms which can work across multiple domains. Understanding the problem domain is not that difficult.

The work product probably offends real software engineers in the way that a normal home cooked meal would offend a Michelin star chef. Yet, before last summer, these people never contemplated the ability to cook their own meals before. The fact that they can do this now is a very big deal.

Now these skills don't matter as much because LLM's/Cloud/Java abstract out these problems.

What makes domain expertise a different category itself that lends it to be not automated out by LLM? Example: Why can't I go to into an agri-startup and become better than anyone else by querying an LLM even when I have no domain expertise? Much the same way I beat the dev who was good at DB internals?

The only moat is that there is so much more work for domain experts since they and many of the bureaucratic processes in between aren't the bottleneck anymore

I think it's important to be clear on what's really happening. Companies were accomplishing 5% of their annual plans, and now they're taking a realistic swing at all 100% to likely reach 20-25%. It's a crazy amount of work, for the same specialists and more human workers.

I’m tired of these endless articles on HN about software engineers trying to reinvent their identity while trying not to lose touch with reality.

One way of dealing with LLMs is to deny the skill level of LLMs. Claim they can’t code as well as you. This excuse works to a certain extent but it also fails because not only are their multitudes of cases where the LLM IS intrinsically worse than me… but there are multitudes of cases where it is better. So this excuse cannot be universally true.

The other way is to claim software engineering was never the hard part of engineering and that other things were harder and that was always where your primary skill was located. This excuse is also idiotic. First, Software engineering is hard. It is genuinely not something that anyone can pick up very quickly. Second, all those other “skills” like “domain expertise” are STILL targets for the LLM. It’s not like the LLM exclusively is only good at software.

Just face the goddamn truth. AI is on a trajectory to dominate. That’s what all the trendlines say. It’s not currently dominating, but it’s close, and the trajectory points to an endgame where it is fundamentally better. The trendline could be wrong but the trendline is the best quantitative predictor we have and it’s been trumping all the half baked theories on HN where people were claiming self driving cars would never happen and AI could never code. HN was historically wrong… the trendlines and the VCs who made those bets have been right. So who’s the bigger idiot? Those VCs creating the AI bubble or HNers who have been continuously wrong about everything? (Minus crypto, HNers were right about crypto).

If the trendline is true our skills as engineers not just the software part is on track to being dominated by an artificial intelligence. The tools trivialize your skills until all the moats are gone. Not only that… AI is becoming better at art. Poetry, writing, paintings, music… AI shows us how trivially reproduceable all of it is. That is the truth. We aren’t not unique and all the meaning behind being human is just an algorithm. It’s all reproducible. Even your self delusional attempt to deny and delude yourself away from these truths is predictable. I can see someone formulating a retort right now.

Edit: Yes "expert" was too strong a word. Proficient would be better. A lot of the barrier to entry in a field is just not understanding the domain.

AI is, at best, as useful as those masses. Actual discoveries, actual novel software, actual human advancement is beyond AI and the domain of the same humans who've always advanced technology.

So yeah, AI is ok for copy-pasting the same shit that we used to plug together web frameworks for, it's fine for internet research (Gemini for me is like a supercharged Google with no ads or SEO garbage), it's fine for repetitive emails and making my "fuck you" emails sound professional, but actual expertise isn't going away any time soon.

Also, I disagree that software engineers can "just learn" non-software domains. If there's one thing I've found about most people who call themselves "engineers", it's that their thinking is way too rigid for many other domains.