While I agree that domain expertise has always been a moat, I believe the author is missing something critical: there is a big difference between being able to verify the output of a system is correct, and being able to tell a system how to generate the correct output to begin with.
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.
Anybody who has ever done programming professionally in the small scale knows this. Refining the requirements is the job.
In fact, I've never known an industry so keen on levelling its own moats as the software industry. We regularly invent things like 4GL, graphical programming and frameworks and engines such as Unity just to enable more people to do programming. People will happily teach programming for free in outreach programs (I am just one example). No other profession does this. Perhaps with the exception of mathematicians and other fields that are very close to programming.
I could teach an economist enough programming in a weeks that they could write an ERP module, but I could not learn enough economics in a week to write one. If the language was the barrier, we could invent a more effective one. We invent new languages weekly anyway. Having seen how quickly beginners can make things with Unity or Godot, I seriously doubt an LLM agent could improve much on this. Of course, if the job is writing yet another CRUD React app with a Java or Python backend, then sure, the LLM will be very effective. But compared to doing same app in something like Excel or MS Access? Not that much.
Everything everywhere does this. All human progress has been making common and accessible the rare and expensive. Universities, online courses, textbooks, etc all exist to make economics as accessible as possible, there’s just a really tight reward loop with programming so your rate of learning it is more tangible
I think you are underestimating how hard it is for average joe to learn programming basics. I remember a fellow in high school that just could not accept that = in programming is assignment not an equation (like in high school math)
>We regularly invent things like 4GL, graphical programming and frameworks and engines such as Unity just to enable more people to do programming.
You're right on the money on this.
Earlier this month I went to visit a company for a complete demo prototype of a full one-to-one train simulator trainer mostly designed and programmed by a former civil engineer using Unity engine. According to the company, they could not do it if Unity engine (or similar) is not around because it will be prohibitively expensive to develop.
In a related news, Unity recently released AI eco-system namely Unity AI Suite [1].
the 1 dimensional thinking that's perverse in the industry will be catastrophic.
we don't like doing the hard things e.g training juniors so they can be skilled seniors via good apprenticeship programs i.e on the job. now we r delegating to stochastic parrots.
in terms of systems thinking which is one skill you need to be a domain expertise - very few people are ever curious & are not willing or able to ask critical questions. hence the groupthink that's prevalent in the industry.
no wonder the quality of software never goes up - while the building blocks have gone up in quality. an analogy is like having super strong bricks but making brittle structures
>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
We have internalized more knowledge than we can explain sounds like the textbook definition of Polanyi's paradox:"Polanyi's paradox, named in honour of the British-Hungarian philosopher Michael Polanyi, is the theory that human knowledge of how the world functions and of our own capability are, to a large extent, beyond our explicit understanding" [0]
Thanks so much! I hadn't heard of Polanyi's paradox before but it is exactly what I was talking about. The Wikipedia entry even highlights the problem for AI-driven development:
> Polanyi's paradox has been widely considered to identify a major obstacle in the fields of AI and automation, since programming an automated task or system is difficult unless a complete and fully specific description of the procedure is available.
I think the answer is in the second sentence of the article. The most valuable person in this new world (using the author's own phrasing from the penultimate paragraph) is the one who is good at "building a working model of the domain in [their] head".
Depending on the domain, this may either be the domain expert themselves, or someone else trained in formal logic, data structures and organizing information into coherent hierarchies. If this is neither the domain expert nor the programmer, then it must be someone in between. In the old world, this role was called "business analyst".
I'm this person, and I do find AI to be quite helpful, though I'm mostly just playing around at this point.
I'm the daughter and granddaughter of programmers, and I learned the basics of how to code as a kid. I'm good at it and have a knack for it, but I didn't want to do it for 8+ hours a day and then spend my nights on it as well, so I didn't pursue it as a career. I did an undergraduate degree in Linguistics, which has been really helpful for having an intuitive sense for what 'language as data' can accomplish and for a strong understanding of the difference between language as data and language as meaning. I studied formal logic systems. Then I did a graduate degree in Library Science and worked in libraries for a decade and a half.
I can organize and define systems very well, and I'm trained in how to wheedle information people don't consciously know out of them without them knowing I'm doing it. I've spent enough time around actual devs to understand where my limitations are and when to loop in someone who knows more to check my work, and when it's important for the work to be super accurate versus when I can learn by fucking around. (Front end and design? Fuck around! Database structure? Fuck around but with an exceptionally robust backup system kept outside of the AI tools' purview + don't fuck around in prod. Storing credentials and people's information? Ask someone.)
The problem companies are going to have is I'm very disinclined to work for them doing this, particularly if they want us because they think we're going to be cheaper. Most people who are in this category a.) could be devs and opted not to, and there's a reason for that and/or b.) are the children, cousins, etc. of programmers. We're not stupid: we know we're just as disposable as they're trying to make devs.
I think this neatly captures the gap in the article; it’s thinking of domain expertise as static, rather than something that needs to be built/extracted.
It also goes the other way; A
a good engineer can build experiments and discover the domain even without an oracle on the team; reading protocols or manuals or specs, for example. In other words, learn to be a domain expert themselves.
Personally I find the most efficient way to learn a concept to be building it; you are immediately faced with your blind spots. AI for sure helps improve cycle time on these discoveries if you use it that way; time writing boilerplate goes to ~zero and you can spend your time figuring out how to answer the real questions.
I do think that we as a profession need to learn new architectural and craftsmanship patterns to make it easier to learn from our code; concepts like Literate Programming which were too fussy for fast-moving teams may end up accelerating in the agentic world; I need to read a lot more code than I write now. But also things like Acceptance Test Driven Design; not new concepts, just newly increased in value.
Having worked with domain experts I concur on the difficult time they have expressing the rules of their domains.
Once I built a little domain-specific language for them, that was tested against old jobs to see if they contradicted the past; it was a nifty project and since then I am convinced that DSLs are underrated as a way to encode expertise.
WRT the native grammar, consider adjective order. Few native english speakers (me included) can off the cuff name the proper order, but everyone knows the "right" order.
I have found this with (mechanical) engineers. They know what they want to see but don’t understand the underlying details which are more mathematical than the average engineer is able to work with. So the people working on engineering software are often physicists or applied mathematicians.
> 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.
This combination of requirements is the business in most domains. Software or otherwise. Codefying different rules and executing them.
I think that you raise some excellent points here. I want to dive deeper into what is meant by domain knowledge -- as a I see it. This phrase comes to mind: "Those who know, do. Those that understand, teach." (Google search tells me that it is frequently misattributed to Aristotle.) People who cannot clearly articulate rules for their domain only "do". However, those that can clearly articulate will "teach". Thus, I would say true domain knowledge is the ability to teach that domain to others.
I largely agree, but want to note that there’s the counterpoint “Those who can, do; those who can’t, teach” (from George Bernard Shaw’s 1903 play Man and Superman), which also has some nugget of truth in it.
I agree with your point that people “from” the domain aren’t automatically equipped to start AI-building software in the domain, exactly because they often lack one of the most crucial software development skills — being able to (and having the desire to) describe a complex system with a finite set of deterministic rules.
But I don’t think we should be calling these people “domain experts”. I think we should reserve that name for the other group, for the people who truly and deeply understand the domain, the whys and whats and why nots.
That ship has sailed. Anyone who works in a field is now considered a domain expert (or "SME") even if they're nothing of the sort. There should ideally be another term that's a superset, but I doubt it would ever catch on.
People you say are not experts are fully capable to explain whys and whats and why nots. In more details that you can absorb.
They will do that on examples tho. They will recognize and explain every single exception they see - but they are not able to list all exceptions up front. Because, that is highly unusual task
> 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.
Right, so the spec is derived from examples, an interactive process that doesn’t require a programmer.
Interpolating between the examples still requires understanding of the domain for the interpolation to be a sensible one. And it’s an iterative feedback process: thinking about possible interpolations leads you to cases that need clarifications from the domain experts. The domain experts won’t come up with all relevant examples by themselves, and they typically aren’t as good at thinking about interpolations like a developer who is trained to always consider all possible cases; they don’t think in terms of models the way a developer does.
The programmer skill is how to abstract the specs from all the examples. And then to formalize it. Actual coding is merely translation. And only beginners tend to focus on that.
This is the exasperating part about learning to speak Spanish using a textbook; you must guess the grammar rules because the textbook won't tell you. So, you use the English rule and hope and pray that it is the same in Spanish, and you'll be right the majority of the time but often wrong. Spanish textbooks written 100 years ago tell you the grammar rules and are more useful than recent textbooks.
While interesting, I rarely found knowing grammar rules beyond some very basic ones to help all that much for learning to speak a language, compared to language exposure and speaking practice.
I agree that it has some resemblance, but the striking thing about NP-complete problems is that an efficient solution to any of them is an efficient solution to all of them, which makes it worth trying exceptionally hard to find one.
It could be that whatever lackluster expertise you can squeeze out of an LLM is good enough to discourage investment in the real thing since unlike NP-complete problems, expertise isn't generalizable.
Agreed. But there is a new dynamic coming to place. Who can execute faster now that we have AIs? I think the combination of (Good SWE using AIs) collaborating with (Domain Expert using AIs) is the winning team. Things are accelerating on all fronts, the frontier may be jagged but AIs is making progress on all capabilites.
> 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.
This is the part I disagree with.
In a non-agentic world, the expert and the programmer are two different people. If the expert finds a bug in the software, they have to describe that bug, send it over to a programmer to fix, wait for a new release and until that scenario occurs again, realize that their description was actually wrong, send a corrected description, rinse and repeat for a few iterations until the bug is finally fixed for good.
In a world of agents, the expert finds the bug, asks the agent to fix it, realizes that the fix is incorrect because of sloppy thinking, does a few iterations until the feature works correctly, and that's it. Bug fixed; with 10 minutes of work instead of a few weeks.
> realizes that the fix is incorrect because of sloppy thinking
If the domain expert doesn’t understand the generated code, they can only discover incorrect logic by specific examples (specific inputs), which is usually impossible to do exhaustively. The programmer, on the other hand, can see incorrect logic directly, generalized over all possible inputs and states. It’s the difference between testing and proving correctness.
> In a world of agents, the expert finds the bug, asks the agent to fix it, realizes that the fix is incorrect because of sloppy thinking, does a few iterations until the feature works correctly
That would require clearly (a) knowing what you want, and (b) expressing it unambiguously and in detail, including all edge cases. Essentially producing a spec.
Most people are not able to do either. Talking to an LLM does not change that.
I had the same problem in air travel industry. Our ticketing officers were never able to communicate how the ticket commission rules from our partners should be interpreted. I ended up adding a test framework, that allowed to add examples and counterexamples of existing flights to each rule, to verify that they are applied the way they'd assign commission by hand.
I fully agree here. It's also not uncommon to have the reply for specific items be "Ahh, well that is a bit of an edge case see because of X and Y" and then you learn there are many many "special cases" to the point that they aren't that special anymore.
Some companies use product managers for this purpose. They translate the implicit domain knowledge of those experts into explicit requirements, mostly through interviewing.
> 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.
Maybe they need a build a hybrid expertise of "domain" and "software engineering". For example, robotic surgery requires expert surgeons to build sufficient expertise in robotics
Also, noticed a pretty high karma for a throwaway account.
I guess the flip side to that is the iterative conversation to develop these specifications is exactly what AI chatbots are good at.
It's also the main reason why very structured AI agent orchestration for software engineering modelled on rigid processes fails to really provide much value.
I suspect that the reason AI chatbots are not very good at developing such specifications is that they don’t build a domain model in their “head” — the only models they have are already hardcoded in their weights. A human learning a domain, on the other hand, forms new neural connections representing the model of the domain that they build in their head.
Things the domain expert on the author's example cant tell (from the top of my head):
- 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.
> Notice which way this cuts. Pre-agent, the engineer had a path the dispatcher didn’t: they could go learn the domain. Slowly, painfully, by shadowing experts and reading specs and getting things wrong in production, they would build the mental model and then they could build the system. That path was the whole career ladder in a lot of fields. The domain expert had no equivalent path, because learning to build reliable software is years of work they were never going to do.
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.
Such a good example of this I encountered recently:
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.
Product management is its own skill, and few true domain expects have it. Without some form of PM, the resulting software will end up a mess due to poor UX, too much bloat, etc.
I think AI is going to force most software engineers to pick up this skill in some form. Building is easy; knowing what to build is the hard part.
>> 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.
They can, and they are. You just don’t hear about it on places like HN because those people are not on this website. Which is why some people here make smug statements like “if LLMs are so good at programming how come we haven’t seen any useful apps made with LLMs???”
"So the most valuable person in this new world is the one who has both skills because they can verify at both layers. They know the generated code is sound and they know the answers it produces are true."
This has always been true since the dawn of programming.
Whole TFA doesn’t take into account reason why software development was actually so valuable.
Single specialist in any domain is not that valuable. You may charge $200 for hour of his time sure. But to grow company you now need N specialists.
What software was doing was not making specialist obsolete replacing a specialist by encoding his knowledge - well many tried to do so but failed even in 80’s “expert systems”.
What software was doing was making it possible to structure specialist work, make it possible for a single specialist to serve more customers at the same time, make it possible to hand over work in a structured way to junior specialists, making it easier for senior to take over edge cases and spot check work of those junior specialists.
This setup allows company to not being tied to number of specialists to grow, this setup allows company to charge less per customer but take over more of the market share.
Whole premise that now each specialist will waste time dabbling in AI software development is ludicrous, especially if each specialist would be building his own tooling somehow.
That sounds more like the extractive setup of corporations like IBM, where return/specialist must be maximized against the number of clients that can be juggled simultaneously.
Whereas in larger technology firms, yes that happens to some degree, but only with the highest level specialists such as PEs, fellows, etc.
They are already so wildly profitable and valuable by the simple nature of computing itself: it scales second only to money with regards to compounding effects. Once you have software someone can use, it scales near infinitely to more users, thus value is extracted from the work of a single specialist for all time. No need to complicate it with trying to abstract work juniors can do (and fail) and then have seniors correct. What would they be doing in non-extractive firms?
Yes, but most people (especially a large portion on HN and Reddit) do not internalize it.
A SWE who has always worked in DevTooling companies will always be preferred by DevTooling companies over a generalist. A SWE who has always worked in AdTech will always be preferred by AdTech companies over a generalist. etc etc.
Software fundamentals - though useful - are table stakes skills at this point. No business wants to deal with the headache of on-ramping employees who have never worked in a specific domain or industry because it takes too long for a generalist employee to build the intuition needed to understand that segment of the industry.
> Software fundamentals - though useful - are table stakes skills at this point.
I'm having a difficult time even seeing what we're talking about here. I see "seniors" in our industry that don't know what I would call the fundamentals of programming or software development; apparently not all fundamentals are created equal.
And therefore there will be a huge knowledge gap as companies refuse to hire anyone who hasn't worked in the field for 5+ years and people who want to work in that field but haven't don't get hired.
> No business wants to deal with the headache of on-ramping employees who have never worked in a specific domain or industry…
The usual sickness. If you don’t train people to become specialists and just expect them to fall from the sky, it’s only a question of time until you run out of specialists.
The software generalist described in this post has domain expertise as well. In software.
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.
Exactly, plus you now have a new superpower with AI — the ability to dig into and ramp up expertise in mostly any domain. I’d say the article gets it backwards.
True that AI lets you get information fast an efficiently. But it might just make you feel like you understand, even if you don't have the kind of hard won domain intuition that this article is talking about. I certainly often get delusions of grandeur when I learn a lot of something and haven't yet suffered with it in practice.
Exactly. The software engineering domain is huge. I could code just about anything when I was 16 years old after 2 years of intensive learning... It took me an additional 10 years to learn to design software that is secure, maintainable, efficient and scalable.
I find this take off. In general, LLMs collapse a bit of all knowledge work, including many domain experts. I work with domain experts often and it’s usually the most difficult part of the process. Now, I can ask an LLM the questions I need to design the system and have an agentic system help me build parts. And it works, quite well.
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.
> I work with domain experts often and it’s usually the most difficult part of the process. Now, I can ask an LLM the questions
And you will not notice the obvious errors in its answers to even basic questions, because you are no expert and should have really consulted with one.
When talking about their own area of expertise, almost everybody immediately notices the glaring faults. But then they are very impressed and take LLMs as gospel when talking about anything else. I do not get it.
Shallow generic domain knowledge is not really a helpful domain expertise, is it.
Lets take banks - knowing how banks work in general means little for that one specific bank who is paying you, which has different portfolio than most, different processes because everything is homebrew over decades of doing business and they differentiate from rest of the market in many ways and thats their key proposition, different data infra, set of internal apps, protocols and so on. There is no general llm knowledge you can distill in few minutes that would help you much.
You need to know your specific company, no way around it. In fact, to be proficient in those deliveries, you need to know those internal politics, processes, their quirks and so on, 100x more than some code fast delivery since this is majority of any bigger project. You need some reputation to get things done effectively.
This, for some time, won't be something llm can massively improve, unless those companies change themselves.
>> You need to know your specific company, no way around it
Of course there are ways around it. For example you can build the foundation yourself (which will cover the 80% that is shared across every company in that industry) and give users the ability to build the rest themselves inside the software. This type of abstraction-based architecture is how platforms like Salesforce took off and became ubiquitous.
> So the most valuable person in this new world is the one who has both skills
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).
Very well articulated for sure. But, I do think the word _'expert'_ always tends to do a lot of heavy lifting.
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?
Whenever I read articles like this one that appear to be very generic and give advice about dealing with AI, I remind myself that he software industry is like the construction industry.
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.
As a doctor who learned how to program, I started by writing useless Chrome extensions. One thing I learned early was that programmers generally do not like learning the nuances of medicine, and many are quite open about how little they want to learn it. I have tried convincing my fellow residents to learn a simple language like Lua or even just Python, but the resistance is even greater, despite the fact that they constantly express how they have always wanted to learn programming like I did.
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.
My friend is an electrical engineer and just passed a FIDE chess rating of 2000. Has played for 30 years, started the chess club in high school. Knows a little programming from the stuff he had to do with microcontrollers in college.
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?
I think a charitable interpretation is that from the perspective of AI, some domains are shallow (like chess), and some are deep (you can fill in the blank here).
What would fill in the blank be? Because this was actually kind of a test for me to address the question of "does AI just amplify domain knowledge?" In this case, it seems it didn't.
There's no chess spec, it's not "solved" like it's just a matter of implementation. Chess bot programming wouldn't be a thing otherwise. I think it's really quite funny that you think this super complicated game is just some trivial programming problem. Try it.
I think this might be one of the worse examples of the dynamic, for the reasons already mentioned by others (programming a chess bot is really more of a programming exercise than a chess exercise), but it’s food for thought, so thanks for posting it. Some IRL domains are definitely more chess-y than others.
Everything is a programming exercise. Can a person with domain knowledge and NOT programming logic succeed. I thought that's what this was about. Ok, sure, AI is good at programming.
what does actually playing chess have to do with writing an efficient game tree search algorithm beyond a few simple principles? You challenged him to a programming contest and won, as the vastly more experienced programmer. Even though he could use AI, your domain knowledge here proved to be the deciding factor.
I had never tried to make a chess bot before though, we both started at the same spot. There are obvious things you can search for to make one. We both had the same information there. The domain was chess, and his expertise didn't help him win. If you are a chef, shouldn't you be able to make better recipes with AI? If you are a fitness trainer, better routines? Etc? Or is AI only for programmers?
A terrible example, because chess is very, very simple - deterministic, rules fully specified in a few pages. We're talking about how you operate in a "games" which, among other complexities, involve the economy and human social dynamics. Billions of other agents. Find a plumber and try to beat them when someone calls you with a clogged toilet. Find a teacher and try to beat them when a student is repeatedly acting out.
"the binding constraint has moved from can you build it to can you tell whether it’s right."
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
Not just domain expertise. The hard part has always been marketing, distribution, risk appetite, motivation, grit, and patience.
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.
But there's nothing stopping AI from developing domain expertise. If you fine tune a model based on the records of all previous work (effectively "shadowing" the existing workers) then it can easily learn this domain knowledge. The only difference is that AI companies have gone after programming domain knowledge first. Others will come later.
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.
Domain expertise is hard but not that hard compared to the insane mental discipline required to write efficient scalable code.
Not all code has to be efficient scalable code. I know some domain experts that were not programmers. They picked up enough Python or Swift + UIKit (this was pre-LLMs) and their applications are now widely used in their domain. In some cases, they contracted some software companies in the past to do the work. However, they did not understand the problem domain well, requiring the domain expert to iterate with them over and over. In the end it was more efficient to let the domain expert to learn enough programming do it with some guidance from experienced programmers.
Also remember that a lot of domain-specific apps do not need to be huge. E.g. I was once at a factory in the 90ies and they needed to do time-consuming calculations for a particular expensive machine. So, someone who worked at the factory wrote a small program in Excel + I think VBA. This accelerated their work extremely and probably saved them 10,000s or 100,000s in manual labor. It was easy for a non-programmer to write, because they knew all the details of the machine and the calculations, since they had done it over and over again. But it's not a enterprise web app or anything and it does not matter if the program runs in 1 or 4 seconds.
By the way, I also consider efficiency to be a specific knowledge domain, or architecting a very large project. So in the end it really depends on how narrow or wide your definition of 'programming' or 'coding' is.
Right. If you're spending your time among developers to whom the code part is second nature, the differentiating thing becomes domain knowledge. If you spend your time among people of that domain who have no coding skill, to them it will appear to be the opposite.
I think there is some prestige factor in the background too. Being seen like some kind of code monkey is to be avoided like the plague. Business people really don't value the math savant engineer nerd archetype who just codes and codes.
Highest prestige is always taste and judgment, not correctness and skill. This is why people will also talk about how business soft skills, communication etc are more important than hard skills like programming, math, etc. I think one should be careful with this. On the one hand it's true that career progression is quite dependent on soft social skills, but there are also really hard kinds of software jobs where cognitive horsepower and technical experience are absolutely crucial and soft skills won't save you.
> Domain expertise is hard but not that hard compared to the insane mental discipline required to write efficient scalable code
"efficient scalable code" is just as vague as good code. How are you going to know your code is scalable if you don't understand your domain? Scalability is not something you sprinkle onto code.
IDK, there's a tipping point where at best domain expertise without skill leads to a LOT of tech debt.
In fact about half of my career has been dealing with 'domain knowledge at least present enough to get the ticket/epic closed but leads to a lot of tech debt'.
i.e. a good portion of my jobs have involved a lot of a good amount of:
- Review PRs with a fine tooth comb because despite domain knowledge, people are human and can either don't know any better, make mistakes, or willingly refuse to integrate feedback, or worst refuse to double check what the coding agent wrote for them.
- 'refactor this thing because it was technically correct but written so poorly that it leads to timeouts and/or a Manager/DBA is screaming' [0]
> 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.
A truly good software engineer is able and willing to learn the domain, but there has to be a way for them to learn. I say that because I've been at shops where various levels did that (i.e. sometimes the company itself, sometimes the team, sometimes colleagues) and I've been at shops where everything is lip service and at best you can only glean from what's in the JIRAs and what you can glean from what people outside of IT say in meetings you are in.
> 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.
I think a big paradigm shift especially in the past 5 years has been that most companies are expecting folks to work to the bone, and it winds up being counterproductive because it prevents anyone from being able to have the important conversations.
Culture is a huge factor in this, I've worked at shops where at the very least you could easily have a side conversation or a meeting, and shops where you might as well sign a change.org petition to request time to talk about it properly.
Still, you are right at the crux; Requirements matter more than code at the end of the day. I've been at shops where a person's definition of 'Correct' meant a feature got delayed despite all requirements being met, because they didn't like they way it was written after they were gone the whole time it got implemented and the rest of the team approved all design decisions.
[0] - Next thing you know you learn about a 'batch process' has %numberOfRecord%*10 inserts, possibly with additional fetches given a poorly designed data model to where it is doing SQL upserts in the most wrong way (i.e. doing a get from the DB and then adding a record to be inserted if not present.) and they keep doing more and more questionable things to 'improve performance' rather than rethinking the data layer's query pattern. Seen it more than once in my career.
Tech debt is what most orgs/managers ask for. If nobody is complaining, there's no problem. Nobody but you knows what's behind the scenes until they ask for something new that the tech debt makes take longer than you think it should. But they don't perceive it that way, because they're probably not software people. They just see it as a new ask that will take a bit longer than expected.
Nobody cares about quality, if they can't perceive it. They want practical results. AI will give that to them, to some degree. Most people don't see software as deterministic anyways, so sometimes not working doesn't register as the monumental, show stopping, failure that it would me or you.
I work as an analyst, and our group has roughly 20% analysts with strong technical (software engineering) skills, and the rest are more traditional analysts / domain experts.
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.
Our group is slowly replacing tableau with self built tools, with HUGE performance gains. I think these BI companies are in deep trouble, especially ones like tableau that make drawing something simple, like a histogram, near impossible.
Kernel developer is not the job same as a game developer or an ERP integrator...
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.
This sort of thing has Microsoft Access vibes about it. All AI is enabling is domain experts to spin up their own software systems with no understanding of how the systems should be put together.
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.
I recently reviewed an app built mostly with vibe coding. The owner said it was almost ready to launch and just needed a quick check.
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.
Where I am headed, I think, is to basically be a platform engineer. The job is to create the guardrails, validation, prompt library, and both agent and manual reviews; that keeps the domain experts safe when they start using coding agents.
It's a little bit like being T2/T3 customer support [or support engineer], but internal. You're there to catch the dangerous spots, the weird edge cases, and to make sure that everything is set up correctly, rather than to solve 100% of the routine problems yourself.
There's also plenty of room for cross-cutting-concerns, of course
Eventually infrastructure will be more simple to orchestrate too without faults I suspect from well developed devops harnesses. The risk and scale companies are willing to accept will still fall on humans for some time even then. I don't see most people vibe coding a million user app that has deeper needs than the basics we see now.
Don’t forget context. Basically I have 2 billion input and 1 million output. Every prompt you do, sends back the whole thing again and again. Let’s say you have 500k context used, you send 10 messages is 5 million. 100 messages 50 million.
Use 5 threats is 250 million.
Honestly, this is my experience as well. LLMs make it easier to explore other domains, but they do not make you the master of one; you still need expert domain knowledge.
That said, they do make excellent tools to quickly try out new ideas and dive into them; they can even be great learning accelerators if you have a curious mind.
I agree that a consistent QA mindset is rare, but I'm not sure even if present if it's enough to replace an SDE.
I very recently looked at the codebase of a vibe-coded app made by someone with domain expertise but no software dev experience.
It was very clear to me that he had described it from his POV to an AI, and the AI had implemented features in a manner that technically worked, but made future maintenance or expansion extremely tricky, which is why he was now looking for a dev.
For example, in his data schema, for every item on a menu, instead of simply having an array property like so for ingredients:
This technically worked and passed tests from his POV at an MVP level. But added a lot of complications when actually trying to build more features or when a new menu item had ingredients the founder hadn't thought to include in the schema beforehand.
I totally get how he ended up where he did though. While describing it to the AI, he probably said something like "store info on each menu item's ingredients, they might have milk or coffee or sugar", and the AI created individual flags for them and he didn't think to question it, because he didn't know what's "right" or "wrong", but then as he kept building the AI stuck with keeping individual flags instead of swapping it out with an array mechanism, and he couldn't have known the correct way to implement it.
Only a dev with experience would know how to describe the system to an AI model to get an output that works well, and how to assess the quality of its output beyond what can be assessed through the basic UI. This wasn't a QA failure, it was a design failure.
I disagree. At some point of complexity, building it yourself is faster, better and (as we're finding out) cheaper. And more fun, although that varies person to person.
Wrestling with a code generator also creates a sunk cost fallacy where progress grinds to a halt but you still try and use the tools to fix the problems the tools created. Or you go in and fix things yourself, in a codebase you don't truly understand. A single developer can recreate the contextual nightmare miasma of a large corporation all by themselves.
There's also an emerging market consideration: MVP are easy to build so time to market is no longer hard to achieve. It's not a differentiator.
X was built in 3 days but is slow and riddled with bugs and security errors. There are also A, B, C, D and E which are effectively the same thing built just as fast.
Z was built over six months and is rock solid and performant.
You can't test quality into a product. Regardless of how much of a "QA mindset" you have, you can only ever find a fraction of defects and technical debt through external testing. This can be good enough for a throwaway app that will only be used by a limited customer base for a limited time. But that approach quickly bogs down if you try to scale it into a product that will be used indefinitely by a huge set of external customers. At some point velocity drops to near zero because the code base is such a mess that it becomes impossible to add new features without causing regression defects or breaking backward compatibility.
I don’t think so. Most things are sufficiently complicated enough to require multiple domain experts working together to achieve a goal.
The dunning kruger effect is in full swing as people think AI replaces the domain expert need.
Most of the value in the expert isnt the 80% but the tail 20% or 10% where AI fails. For a one of personal app or website, 80% is plenty but only 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.
Agentic coding favors senior generalists with a broad experience in many things rather than narrow experience in one or two things. I no longer think of myself as a backed engineer. That's what I was. I can do it all now. I've been building products and doing CTO jobs for a few decades now. I'm not a specialist in all layers of the software but I know enough about all layers of the stack to be able to do things with an agentic coding tool.
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.
> Agentic AI severed the link between the two. You can now produce the software without ever building the model, and that breaks an assumption the whole profession was organized around.
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.
Nice idea, but engineers are engineers for a reason.
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.
From my experience what domain experts are often missing - and at least currently this is also an area where LLMs fail - is the ability to model data and interfaces in a sustainable way and factor in team and domain boundaries.
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.
I'm such a generalist. During all my career, from support to decades of C-level, I was equally challenged by domain and IT people for not being deep in everything. But whenever they couldn't come up with a compromise, I was the person who always offered it.
Doing purely consulting work now, mostly implementation. And this "little bit of everything" can be strongly multiplied by AI tools, since I already know what exactly I want to achieve, I just need speed and a cross-check.
How much pontificating needs to be done before people acknowledge nobody has any idea what to do with AI on an individual level?
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.
An idea that's beginning to solidify for me is that AI tools make software development harder.
It's harder because they dramatically raise the bar for what's possible to do. An individual developer can take on significantly more challenging projects now, because the ultimate constraint has always been time and AI can help you get more done in the time available.
But the stuff you can get done with that time is a whole lot harder. You have to understand lots more things, and get radically outside your pre-AI comfort zone.
It used to be acceptable to spend several days refactoring a codebase, or figuring out how to ship a small feature because it's in a part of the system you hadn't worked in before or involved learning a new library in order to build it.
Coding agents mean you can climb those curves a whole lot faster, but you still need to climb them - and the volume of information coming your way is much higher.
If you're worried about non-technical vibe coders taking your job, the correct response is to be much better at building software than those vibe coders. That means you need more skill, more ambition, and more experience. It's hard!
From that perspective, development has always been harder since I started. I left college with a copy of K&R and remembering courses that applied to real life immediately, because data structures and such were just what we had. In my first job, I ended up writing a code generator to help serialize a large number of data structures, straight from a compiler design class.. which right now you don't need to know a thing about, because serialization and languages with introspection are everywhere. The knowledge you need to be a professional engineer just kept going up through the last 30 years, while most of the basics became far less relevant, because the libraries just did it.
AI raises the bar again, as its probably at least as good as me, if not better, at anything I learned in college. I've spent years living off of random trivia from the last 30 years, as I saw computing grow with me. How do you know this?! Because everything built on top of it didn't exist when I was your age, so I had to learn it! But well, nowadays the AI is better at that trivia too.
The world moves, we do what we can with what we kno. It's not just programming, but what innovation and automation has done to the vast majority of things humans have done to be productive for each other since humans are people. We'll have to cope, like the guy that bred oxes to pull the plows.
> If you're worried about non-technical vibe coders taking your job, the correct response is to be much better at building software than those vibe coders. That means you need more skill, more ambition, and more experience. It's hard!
This is a false fear. The real risk isn't that some 19 year-old vibe coder is going to replace you, it's that there's simply less need for more experienced engineers. The market is shrinking.
Also, even if the premise behind the SaaSpocalypse is naive and oversimplified (companies aren't going to replace all their SaaSes with internally vibe-coded replacements), it looks reasonable that net-net AI will have a negative impact on the value of software.
I worry this is looking at where the ball is now instead of where it's going. The recent disproof of an Erdos conjecture should put to rest the idea that LLMs will reach a skill ceiling before they reach superintelligence.
I believe we are headed for a world of superintelligent AI where LLMs are much better at logical thinking than humans, the same way that chess engines are much better at chess than humans.
In that world there's really nothing humans can offer in terms of logical thinking other than their humanity itself. An 8 year old with Stockfish can beat Magnus Carlsen, and an 8 year old with Codex (and daddy's credit card) will be able to beat me at software engineering.
That's true but in a way it's also more fun and engaging because the tedious stuff just gets worked through leaving you to think about the bigger picture items.
Though I'll say I don't buy the stuff about AI "democratizing" development since making it much more capital-intensive kind of has the opposite effect for anybody doing dev work at home.
> If you're worried about non-technical vibe coders taking your job
I'm not worried about non-technical vibe coders taking my job. I'm worried about psychotic VCs and CEOs putting me on the street in the name of "optimization" of "lower value human capital".
I had the same thought recently, I've had it happen to myself.
I've been working on something relatively large and greenfield recently.
A big chunk of my time is spent thinking about the hard parts. The raw information processing rate needed to keep up with the state of the project is high.
It feels almost like mental athleticism, whereas coding used to be a rather chill activity.
developers now are expected to randomly jump around projects and ship without friction. For employers it means they can move us around like pawns. Lot of companies have not reorged themselves to this new type of workforce thats much more malleable.
it used to be that i pay your due at some enterpise and learn some corner of codebase really well and become go to person. that would give you job security.
The vibe coders have a key advantage you don’t: they don’t give a fuck.
They blow through a task and move onto the next one. Management sees this as progress, and the vibe coders are rewarded.
When shit breaks later on down the line, and fires have to be put out and things rewritten, the vibe coders do NOT get the blame. They do NOT get punished. Most engineering teams operate on a blameless culture. If code was approved for production, then it should have been good enough. Vibe coders will keep on doing what they do, and skilled experts will be left cleaning up their messes.
For anyone who actually cares, it’s over. You are not steering development anymore.
Not really. The primary stopper was never time or effort. It was need (and wisdom). If a project was important enough, you’d do it. If it’s not, it falls on the wayside.
Now with LLM tools, what you got is a slew of projects their creators aren’t even interested in. It’s theater.
LLMs are an additional tool to add to your arsenal. They are not omnipotent and need care, just like any other tool.
My best effort, so far, at an analogy is a modern drill driver compared to a screw driver/brace and bit/etc:
You can get some remarkable results in a very short time compared to the "old school" gear.
You can get some "amazing" anecdotes eg "I screwed down an entire floor at 16" x 1" c/c within an hour instead of an entire day and I took loads of fag breaks" (I could have used a nail gun instead in half the time but I'll never raise that floor easily in the future, and probably done at twice the cost)
I have several on prem LLMs and access to the rest and I'm pretty sure I'll be extending my analogy to ... brand, eventually.
What I do not expect to be doing is looking for a new job. A drill driver is not a carpenter/site labourer/useful without a person!
Remember the OOP Hype 20 years ago? I'm still cleaning shit up from then in our codebase when everyone used patterns after skimming through the GoF book without even knowing why ....
My prediction is in 20 years I will clean up the shit that was co-authored by Claude ...
The idea that a lot of people have in their heads is that if the code doesn't fit the need, you'll just start over completely with a fresh codebase, because it's so fast to generate it.
In other words, there won't be 20-year-old code in this future.
We'll see if that comes to pass, but there's a lot of money betting that will happen; enough to where people will disregard what the downsides are.
It all feels to me similar to how spectators or laypeople judge pro sports.
Don’t quote me on this, just trying to make a point:
They’ll say you need perfect symmetry to do well in sports, which is highly correlated to development stability in the womb; higher symmetry = perfect development.
Then after some years, news will come: Bruce Lee’s one leg is shorter than the other by a significant amount, and Usain Bolt has a similar asymmetrical development.
Then they’ll flip-flop around their initial argument by claiming that they are outliers so the general rule need not apply.
brother just build what you find interesting and it may work :)
It's well established that injuries occur at much higher rates when you have asymmetries/imbalances. Not even just physical "deformities" but just when your body is shifted or twisted in some way such that you don't move with balance.
Haven’t you heard? If you don’t adapt now you’ll be left behind, never to be able to work again! Copilot? That’s so last year. Agentic engineering? You’re already late!
jibecoding is the latest thing: you insult the LLM's bloodline providing it with greater motivation to complete the code you won't review without bugs.
> 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.
Little thing to keep in mind about AI: a technology is only called AI while it doesn’t work yet. Once it works reliable, we give it a proper name and something else becomes AI.
I think it's more: AI assisted engineering is a new skill people are trying to develop and we're on this collective experimentation process, working out how to use AI for engineering with varying degrees of success.
If that's true, any statements defining what is necessary to do should be ignored in that context. I'm still interested in hearing about what people tried, what results they think they saw, and then trying to apply those findings to my own processes.
Which is to say I don't think the pontificating is pointless, but as statements of Real Truth, I agree they're likely wrong. We're too early in the game.
Being a domain expert has been more valuable than being an excellent software developer before the ai.
In 2018 I witnessed one guy with no prior coding experience who built a tool that after a month of coding was making very decent money (more than me), just because he was aware of a particular niche.
He showed me parts of his code and it was as bad as my first program, but his was solving a real life problem.
Overall I agree with this, though I do think that there will be a trend to hoard/keep-secret domain knowledge by professions. Like plumbers will try and make it a trade-secret or protected intellectual property how to change a pipe fitting.
For basic residential plumbing work the moat is not knowledge. There are already books and YouTube videos that will teach you everything you need to know. Professional plumbers can't stop that. The real moat is that most people don't have time, don't want to buy tools, and don't want to get shit on their hands.
For new construction and commercial work the moat is a contractor's license. They don't allow LLMs to take the licensing exam yet.
How trades gate keep is time. You can’t just become a plumber or electrician on your own. You have to be an apprentice for years no matter your knowledge or skill. This is how it works in trades and unions and where the term “pay your dues” comes from. Like you have to literally pay($$) dues for years before you can move up.
There is a difference between something being a hidden/gate kept trade secret and something being easier for person X to do than person Y through a combination of real world experience and practice.
Taste, architecture, new innovations. These are all streams of tokens which are subject to the same scaling laws as code, language, and basic classification.
We are going to see a new generation of models which effectively “solves” these problems for most businesses. Likely within the next two years- then we’ll talk about some other problems which limit adoption.
I'd like to believe that stable state ends in a pair-programming structure, with a systems thinker/engineer and a domain expert.
Someone needs to spot when a linked list is better than a map. And the other needs to spot when clinical trial coding should happen before claims, audits, or patient outreach.
As software engineers we’ve now suddenly become a sort of “god of the gaps” - our existence is only justified in the (fewer and fewer) situations where the AI can’t do the job just as well on its own
Grifters are trying to get their 5 minutes of fame by posting some unhinged shower thought that they have. It's just that you're noticing it now, because it pops up in the feed. Previously they'd say some shit about Agile, OOP/Functional programming or some other bullshit, and it would be swept under the rug.
So far AI has been a (genuinely) massive improvement for...
Search
It's reading my requests more clearly than (for example) Google's search input ever did, and it's got (some) understanding of how close the result (or fragments of results) are to what I want.
I can ask it about things I know about, and it can answer with strategies I hadn't thought of.
HOWEVER - I still need to understand the results AND AI can overreach - it can say (figuratively) "Oh you are searching for Event handling, therefore I will write a orchestration saga" - which, if I am not across, can get us both in trouble.
Further, we KNOW that AI has no (real) understanding of the responses - it's just token adjacency - and it fails basic logic tests
Current AI is just awesome natural language processing, but it's still got a ways to go to where I would say "It can replace people"
Edit: LLMs demonstrate (almost perfectly) the difference between correlation and causality. LLMs identify correlative patterns, but the job still needs (us) to make the causative judgments.
Thank you!
That's exactly what it is. Instead of presenting you 1000 results (or 0) which you have to manually skim through, it generates 1 result as a summary. And even if there is no actual search result, it will hallucinate you 1 result (full of BS).
But because the result sounds right (and in cases with good data it actually is) people tend to trust it. I do not dismiss the potential, but for me the line is crossed when you take the result for granted without verifying and while I'm sure many here think that is implied, I bet you, at large, it is not and will be even less so in the future.
> It's reading my requests more clearly than (for example) Google's search input ever did
I see this take a lot and it puzzles me.
While I think LLMs provide some advantages over traditional search in some modestly nontrivial contexts, they tend to be inferior to traditional search at its peak. I attribute this attitude to two things: the broad progressive enshittification and productization of search, and the fact that (re)search is a skill that most people tend to be bad at. Without massaging, LLMs spit out the most utterly braindead boneheaded queries, which are fine in cases where the problem is very well understood with minimum uncertainty or critical nuance. If your problem has either, God help you. But perhaps those queries are at least as good as the average human generated query
All of those things matter. One needs to be able to judge the solution in order to make a judgement if it is fine, or not. Why yes, and why no. No matter who you export the typing process to. LLMs are just tools speeding up the typing process.
I think what matters is.... the person being intelligent. I do not mean this as an us vs them thing, but a LOT of companies have some not very smart people in and running them. It's never about exact skills or roles.
My observation on AI is that some frankly less intelligent folks think they don't need smart people any more because AI makes them smarter. I disagree.
The article says that domain expertise is more valuable now because agents can code well, but not understand the domain well.
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.
Good post! Also, in my opinion, domain expertise is actually more interesting than pure coding ability. Coding, for me, has always been a means to an end. I'm equally happy with a spreadsheet if it solves my problem, and in fact I hate most apps.
I’ve been an engineer for a while now, where we have a mix of EE, ME, SysE, SWE, and the programmatic folks, lots of software/hardware integration at pretty “low” levels for each discipline, really fun.
One of the things I say is when I’m on my soapbox is: we are all engineers. We have different tools in our toolbox to solve problems. We get paid to solve problems, not (for example) write software. Software is just a tool.
There is a separate aspect of having domain expertise in that it open paths for a software engineer to make a lateral switch to a Business Analyst role. That in turn opens paths to management track on the business side as one gains deeper knowledge. If you have business expertise AND direct SDLC experience, that is a different kind of value you bring to the table.
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.
That's not true at all, sure CRUD might not have been that difficult, but absolutely there is extremely complicated software out there that is really difficult to write in a performant and correct manner.
Yes. Audio, video encoders, decoders etc, 3D modeling, rendering etc.
That too is "domain" even it feels like it is NOT. Domain of signal processing, Euclidian spaces, information theory and what not. Thar too is all "domain" and that "domain" part is difficult to write.
"sure CRUD might not have been that difficult, but absolutely there is extremely complicated software out there that is really difficult to write in a performant and correct manner."
I was a developer for 20 years, before I pivoted to cybersecurity. My hobby projects were always more complex than the software I wrote at my day job.
The majority of software developers are writing some type of CRUD code or glue code for business processes. A small minority are writing complex code at big tech companies.
AI will most likely replace the need for many software developers.
Writing software for a domain is itself a domain of expertise, with a similar learning curve. It isn't enough to know the programming language.
For example, it takes years to develop the knowledge and idioms required to effectively write high-performance systems code, which is separate from the language the code is written in. You can have decades of experience in a systems language and zero experience writing modern systems code in that language. Same with embedded code, supercomputing code, etc.
Writing software is only "not difficult" if you've already learned how to write it.
The type of software the average hacker news commentator writes is not difficult. That does not mean there aren't industries where the code is legitimately difficult. Games, embedded, etc.
Agree on this one. As a Civil engineer, I can smell which software ( or some part of) was developed by computer engineers without much understating of the "domain". The worst offenders are software needing multi-domain expertise in the first place. Crude Ex. Payroll (Finance) and Leaves (HR) are 2 seperate domain, and they need to account for each other.
I think you're putting the analogy in the wrong place. In this context, civil engineering isn't a "domain" any more than software engineering. Both are exercised in support of the "actual" domains relevant to business or society.
The more analogous question is: as a civil engineer, could you tell which structure was designed with the help of a civil engineer, and which was designed by a domain expert (e.g. a transportation administrator) with automated civil engineering?
Although the idea of the main promise of the article is correct - that teaching domain expert to use AI seems to be faster than teaching senior developer the domain, there is and will be a learning curve to learn AI. It still sounds like this article is trying to sell vibe coding in a fancy wording.
However, domain expert working in team with senior developer that utilizes AI will be the ultimate combo.
> If you’re an experienced engineer betting on where to spend the next few years, this is the bet. The mechanical skill you sweated for, turning a clear idea into clean code, has gotten dramatically less valuable. The thing that’s still scarce is a deep, verified model of some real domain. Go get one. Pick an industry, an instrument, a regulatory regime, a physical process, and learn it the way you once learned a programming language or framework. That’s the part the agent can’t do for you, and it’s the part that’s now worth the most.
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.
In my own experience this is 180 degrees from reality. As a generalist, feeling out the depths of a single domain (something I've been forced to do at least 50 times in my career, to the point that I'm probably a global expert in at least 2-3 things I don't actually care about, but are poorly documented and not especially lucrative on their own) is something that's basically a bunch of Google searches, reading source code, and writing/running tests manually, none of which I really care about short of getting to "the right solution."
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.
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.
While agents are able to take over a large part of code writing, software development skills themselves are still a moat. How to design systems for the future, for team collaboration, for cost efficiency, hosting strategies, security… these are decisions that will still have to be made by a software expert.
Sure, I think the article is basically correct, as things stand right now ... but the problem is that that wipes out software development as a career: software becomes just a tool that domain experts can spin up to make their jobs easier.
Software engineering is a domain on its own, just a technical, not a business one. Good luck with looking for a once in a million data race or deadlock bugs. Good luck with synchronizing system distributed over a whole globe. And good luck with keeping your domain knowledge up-to-date while developing and maintaining your vibecoded system. Assuming you can even provide enough details and deterministic specification to AI agent, because understanding business and having knowledge is not equal to being able to write down specific and concise rules to make a system out of it.
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?
I feel like this aspect has been discussed on HN many times. The thing that resonates with me strongly is that there’s rarely a clear or fixed set of requirements to begin with - at least from my work experiences. Then, domain expertise helps with being able to proactively call out when they are missing and support in defining them. Still I am of the view that with enough context AI could also replace the best engineers with the best domain expertise.
Maybe, but that would require domain experts and stakeholders to write clear specs, and that's never gonna happen in my experience.
I am still bothered that domain experts still keep confusing closing orders with generating a delivery note, or stopping to say articles when they mean a product or a product when they mean an item.
Writing good specs require lots of domain knowledge but a very engineeristic approach these people just don't have.
I thought real moat of software is requiring virtually no extensive knowledge/experience of both system and domain. Copying taste and network effect is significantly difficult. The fact is venture backed startups with plenty of talents and resources has been rarely making their positions in the market before vibe coding. That's why people at 20s can compete with experts in various areas. Current backlash is due to creation of guys with just x years experiences in the industry we often see in other mature enterprises.
While the article is interesting, it makes assumptions that are not explicitly stated. And the one strong assumption that makes no sense to me is that they discuss team size = 1. Sure, a domain expert now can use an agent. But he might as well work with a development team, or a developer that uses an agent. That's pretty standard I would say.
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.
Maximal Point of View that is harder and harder to disagree with: Sharing your domain expertise is asking to get yourself automated away. Already, if any company hasn't begun trying to maximally collect data on every aspect of their employee's work, they are asking to get wiped by future automated competition from companies that learned everything they could from their employees, then replaced them.
Open Science? Open Source Code? Shared your art online? We were all suckers, and might be the first to go.
As someone who has jumped from one industry to another: I'm sorry, but domain expertise isn't much of a moat. Yes, it takes a while to learn a particular industry/business, but it doesn't take that long, and worse still: one advantage that LLMs have over us humans is they have such a broader inventory of human knowledge at their disposal. I've literally used LLMs as product managers for new domains, and while I see the rough edges that LLMs have, they always have significant domain expertise.
> Agentic tools collapsed one of those paths and not the other. The engineer’s advantage, the ability to translate a domain model into working code, is now cheap. The domain expert’s advantage, knowing what right looks like, is not.
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.
I have never yet had a set of words that I have grown to hate so much - “taste” and “moat” being at the top of them.
It is almost always coming from people who have or know about neither.
(Agree with the article’s general sentiment - but just wanted to make this tangential comment)
> There’s no skill file that contains the tacit knowledge of a person who has reconciled a thousand payrolls.
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.
One little detail. The models are already pre trained on similar system implementations. Likely whatever you are building has been built in some form or shape in the past and the ambiguity is resolved by someone in the training set.
In my experience “we need so create something for this regulatory change / new industry trend” is more common than we need to redo a working piece of software that’s been done before.
Like so many posts that end up on HN, I just want to say "you've got a decent idea, but tone it the fuck down."
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.
Domain expertise has always been the path to advancement in most SWE jobs. You’ve always had to understand the domain and have judgment to not be stuck as a “code monkey” in almost every company barring the big tech outliers where you can be on a generic framework or library team.
So "the real moat" are BIs and PMs equipped with LLMs? Man will I enjoy seeing this tower of babel crashing down on some heads. Unfortunately, software has tendency to survive for a long time even the shittiest conditions. Taking into account average turnover of 2 years, we're at least 3-5 generations before it collapses, so the people who started this madness will most likely be elsewhere and won't see the reckoning.
These kind of posts always miss the point that in a team of 10 not everyone needs to be a domain expert, and that now the same work can be delivered with a team of five or less.
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).
> the binding constraint has moved from can you build it to can you tell whether it’s right.
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.
Knowing the correct answer to one million instances of a+b, and validating this, does not guarantee that an oracle will use the + operation that's correct over a domain billions of times larger, which you can't exhaustively check.
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.
There's a lot of people agreeing and disagreeing with the article, but on what grounds? How do you know "domain expertise" is a "moat"? Vibes? Has there been ongoing, persistent attacks by AI on domain expertise where we can say the moat holds, economically speaking? So far it seems quite the opposite.
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.
> Has there been ongoing, persistent attacks by AI on domain expertise where we can say the moat holds, economically speaking? So far it seems quite the opposite.
What do you mean by this? Most human white collar workers still have their jobs. I can't see the future, but yes, so far, human expertise is doing ok.
there is a big difference between seeing that pairs of input and output are correct, and knowing that the system is correct. there are invinitely many in and output pairs and only checking some while you vibe code your tool is never going to be a reliable method
Sure, producing code has become cheap. Yet again the taste matters and LLMs do not have taste - they will apply patterns that are unnecessary or not extendible, producing unmaintainable systems that nobody understands. Capturing domain knowledge was the crux of development process, but so was verifying, documenting, ensuring that multiple systems work together, maintaining uniformity. I don't know where the assumptions, done by developers, that they only need to produce code that just works or goes brrr fast comes from.
Domain expert can develop working code, but they will not be able to ensure above.
This is not entirely wrong, but oddly describes the major flaw in its own argument: software engineering has tacit knowledge just like every other domain of expertise! And just as you can't become a doctor by just reading textbooks, or an architect by just looking at plans, you can't become a software engineer by just reading a bunch of code.
Successful software results from the intersection of expertise in two domains: the application domain, and software engineering.
To me, the bottleneck feels like it's shifted. It's no longer 'can we build this?' but 'should we build this, and why?' Strategy might be the last thing AI can't do for us at least for now
Network effect is the moat not domain expertise. Domain expertise build on network effect (for instance the knowledge of the network) yes, but just simple domain expertise is only a training round way.
These guys live in their heads, so when the world changes, they invent reasons why they’re still relevant.
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.
Highly agree with much of the article. IMO, this is why many engineers who learned to code in the post-2010 'new hot framework every week' era but before LLM coding took hold are able to get much better results from AI assisted coding than those on either end of that sweet spot. The domain expertise in this case is constantly having to adapt to learning the latest flavor of the week DB or JS framework and adapt existing patterns and paradigms to new ones. Agentic coding itself is, in this case, one of those new paradigms.
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.
Perhaps the good news is that even the best spreadsheet-slinging accountant in the west would still going to need some programming experience to do their verification.
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.
This is why Google is pushing SEOs to get their clients to codify and publish their domain expertise: while it gives them a way to filter signal from noise/slop right now (supposedly helping to "improve search experiences"), it also simultaneously extracts that experience into a consumable form for later training.
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.
There are two different type of domains to be aware of here;
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."
> The mechanical skill you sweated for, turning a clear idea into clean code, has gotten dramatically less valuable.
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.
I mean if you ask the AI to make something to manage the inventory in a warehouse without any detail about how the warehouses operate then you're going to get a worse result than a domain expert talking to the AI.
The problem is that more and more people are getting convinced by the AI's that they're domain experts when they're really not.
> A logistics dispatcher, a clinical coder, an actuary. [...] They know the correct outputs for a given set of inputs because they’ve spent ten years living in those inputs and outputs. Hand them an agent and they are startlingly effective, because the thing they’re missing, the ability to produce code, is exactly the thing the agent supplies. What they bring is the thing the agent can’t: the ground truth.
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.
The standard take, including my own from last year, is that these tools amplify senior developers because senior developers have judgment.
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.
The problem with these kinds of discussions is that they act like experienced software engineers themselves don't bring domain expertise to software products.
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.
What some people here are missing, is that domain experts or hobbyists are mostly vibe coding tools for themselves -- not as a SAAS product. They tend to work good enough to do the thing they want it to do. It runs locally or on some VPS, and is held together by string and duct tape.
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.
In the past, an engineer who deeply understood the internals of a DB and how memory management worked in Java would be indispensable.
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?
> In the past, an engineer who deeply understood the internals of a DB and how memory management worked in Java would be indispensable.
That engineer still is indispensable. Any organization foolish enough to replace such a person with an LLM is going to find itself in deep water when the pile of hallucinations becomes too much to endure.
I disagree because we're buying up companies and training models, creating skills and agentic workflows on individual domain expert's 30 years of notes and prior projects
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.
But that’s not true though. You still have to convince humans. You still have to deal with what people in power feel (clients, leads,etc)
This still adds wall time. I saw 0 serious analysis confirming 5x productivity gains.
In the coding part? Maybe, and that’s not even certain. But pure coding is only one order of making software, or solving problems with software
and 0 analysts were saying agentic workflows actually worked 6 months ago, they were completely disillusioned and yet here we are
if you’re actually building these things, you know they and the CEOs they’re hearing from are all 6 months behind. the executive’s frantic pivot to shove “AI” down everyone’s throat didn’t pan out in one quarter and had nothing to do with the actual concept at all
that, and every industry is different. I wouldn’t listen to analysts, I’m in an industry that even Anthropic thinks wont be touched by AI (even though they can read ours and everyone else’s sessions)
all public discourse is just flat wrong, and just like every week this year, you’re just going to wake up seeing a new AI capability headline that makes you question your role in society. So play devil’s advocate all you want, the silver lining is that there’s more work to do than ever before and more of it can be tackled at once
“The hard part of writing software has never been the writing.”
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.
Have you considered becoming a residential electrician? Good job, pays well, lots of problem solving, and it won’t be replaced with an AI. I’m serious!
If a real AI job apocalypse hits there will be no escaping it for anyone.
Even people whose job can't be done by AI will be impacted because there will be far less demand for their services (everyone whose job IS directly AI replaceable will be a brokie) and there will also be far more supply of people moving into their field to escape all the jobs AI does directly replace.
"Join the trades" is the new "learn to code" in terms of seeming like good advice but having a very short shelf life.
A lot of the trades involve physical work, can be seasonal, and ride the construction cycle up and down. The employers tend to be small, and many are family owned, so they are "off the radar" of OSHA and EEOC. You may be at the mercy of bias and nepotism.
The trades are great, but not a panacea. Maybe emigrate to a country with better conditions for the working class.
I’d go with physical therapy! Or something else that’s closer to humans and health. “Problem solving” becomes that much more tangible and directly meaningful to another person
Good comment but I think the timelines are not clear. Humans are an algorithm. This was true before AI. In certain domains (playing chess e.g.) machines have already surpassed humans. Humans still play chess though and chess is more popular than ever.
I still drive my car and self driving cars have yet to displace human drivers. I think the sentiment on HN and other places when Google started talking self driving cars circa 2009 is that it's harder than it looks. Typically the first 80% of progress is easy and the rest isn't as easy. We're almost 20 years after a "pretty good self driving car" and we're still not at "self driving cars outperform humans under all situations".
Today humans use AI. You can't fire up Claude and ask it "what do you want to work on today". The amount of context we have as humans is vastly larger than the context LLMs have. If you give LLMs vague context they're completely lost. They are mind blowing in many ways but they are not anywhere close to AGI. They're also not close to being able to build complex software only guided by someone who has no idea what software is and how computers work. They can do some of that but I've yet to see any major successful piece of software built that way. They also consume vast physical resources to get the job done.
Before LLMs I think it was a given that at some point we would have AGI that's smarter than us. Machines we build aren't constrained by the biological constraints we are subject to and can evolve faster than we have. But when that's gonna happen, whether that's actually LLM-like in architecture, and what things will look like once that happens, are fairly open questions at this point. In the mean time LLMs can certainly generate a lot of code and we can use them to build more stuff.
>Good comment but I think the timelines are not clear. Humans are an algorithm. This was true before AI. In certain domains (playing chess e.g.) machines have already surpassed humans. Humans still play chess though and chess is more popular than ever.
It was always true even before AI, AI just makes it more evident since Transformers are LITERALLY an algorithm that produces content nearly identical to content humans produce.
There's certainly a whole lot of "it was never the coding that was our value" articles about right now. I agree that they represent a degree of self delusion to an extent. But it's also a useful examination of where your value might lie in this new AI age. I think there will be a role for humans in it - where exactly it lies is obviously up in the air.
This article is wrong. LLM's encode all the domain knowledge you could possibly want. As a software dev I can query an LLM, become a domain expert in a short amount of time, and then code up a solution. If people think their niche is safe from automation, think again. Even the people who think theyre the masterminds at the top.
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.
I won't over-generalize here, because maybe your statement is true in some cases, but I will provide a counterpoint: this is not true (in my experience) in real estate title insurance and escrow services.
I've consulted for and led large teams for real estate title insurance and escrow companies for many years, and the domain expertise is so incredibly deep, nuanced, and multivariate (especially depending on jurisdiction) that building valuable and viable products in the space is incredibly difficult - before LLMs, and even now, with LLMs.
Without getting too deep into it, I'm pretty bullish on AI (and have been very close to it and deep in it for a long time, while also very apprehensive about the effects it'll have on society), and I can tell you, from extensive attempts from myself and many on my teams to leverage the latest frontier LLMs to bring deep domain experience to bear to help drive valuable products: we have not yet seen success. It's not helping engineering folks, it's not helping product folks. It's creating a ton of questionable output and hasn't resulted in real ROI, and it's not capable of accurately answering deep domain questions without hallucinations or assuming what works in one jurisdiction works in all.
I've seen success in many other areas, but not this domain - and, importantly, the regulatory environment in which title insurance operates is incredibly complex and strict, meaning you can't just YOLO LLM output into production (as much as we'd love to try so we can learn at a faster clip).
And the kicker: we've found the way for us to build the best products is still going out into the field, sitting with escrow and title folks, watching them work, asking them questions, and designing for the real world, the regulatory nuances, the local client nuances, etc. You can't get that from an LLM.
Agree with you there. What you are working on and the commenter below talking about surgery, they are all valid counter examples where the degree of expertise is quite extreme. But most people are not living on the edge of domain expertise. Im guessing 80% of the domain knowledge out there is up for grabs. For example: I dont have to go get a job at a security software company to figure out how security camera systems work and the principles involved, I can ask probing questions of an LLM and get most of it. The domain knowledge is embedded in the model.
Once someone taught me "you can do xyz reading a book, but you cant do surgery by reading a book". Now replace the book with LLM. This is what "domain expertise" look like for some domain.
We have put lots of effort at documenting the domain, creating precise unambiguous language, glossaries, E2Es written as user stories etc, etc.
And still models are simply not able to translate Jira tasks to clear specs, even for this well understood and common use case.
Also, they don't understand how changes in one part of the business domain will impact other parts. They can get it right 9 times out of 10, but even that is too little and compounds to deeply wrong implementations.
And they don't understand or know the people involved in these processes and what they REALLY care for or what the real priorities are. Very often political.
And that's not even mentioning the code, that ends up with the lack of proper abstractions or harness.
Or the lack of push back against bad ideas at business or code level.
The problem is, discoveries that advance humanity are made by 0.01% of humans or less. The majority of people don't ever discover anything new, they just build or consume what already exists.
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.
While I agree that domain expertise has always been a moat, I believe the author is missing something critical: there is a big difference between being able to verify the output of a system is correct, and being able to tell a system how to generate the correct output to begin with.
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.
Anybody who has ever done programming professionally in the small scale knows this. Refining the requirements is the job.
In fact, I've never known an industry so keen on levelling its own moats as the software industry. We regularly invent things like 4GL, graphical programming and frameworks and engines such as Unity just to enable more people to do programming. People will happily teach programming for free in outreach programs (I am just one example). No other profession does this. Perhaps with the exception of mathematicians and other fields that are very close to programming.
I could teach an economist enough programming in a weeks that they could write an ERP module, but I could not learn enough economics in a week to write one. If the language was the barrier, we could invent a more effective one. We invent new languages weekly anyway. Having seen how quickly beginners can make things with Unity or Godot, I seriously doubt an LLM agent could improve much on this. Of course, if the job is writing yet another CRUD React app with a Java or Python backend, then sure, the LLM will be very effective. But compared to doing same app in something like Excel or MS Access? Not that much.
Everything everywhere does this. All human progress has been making common and accessible the rare and expensive. Universities, online courses, textbooks, etc all exist to make economics as accessible as possible, there’s just a really tight reward loop with programming so your rate of learning it is more tangible
I think you are underestimating how hard it is for average joe to learn programming basics. I remember a fellow in high school that just could not accept that = in programming is assignment not an equation (like in high school math)
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>We regularly invent things like 4GL, graphical programming and frameworks and engines such as Unity just to enable more people to do programming.
You're right on the money on this.
Earlier this month I went to visit a company for a complete demo prototype of a full one-to-one train simulator trainer mostly designed and programmed by a former civil engineer using Unity engine. According to the company, they could not do it if Unity engine (or similar) is not around because it will be prohibitively expensive to develop.
In a related news, Unity recently released AI eco-system namely Unity AI Suite [1].
[1] Unity AI Suite:
https://unity.com/features/ai
the 1 dimensional thinking that's perverse in the industry will be catastrophic.
we don't like doing the hard things e.g training juniors so they can be skilled seniors via good apprenticeship programs i.e on the job. now we r delegating to stochastic parrots.
in terms of systems thinking which is one skill you need to be a domain expertise - very few people are ever curious & are not willing or able to ask critical questions. hence the groupthink that's prevalent in the industry.
no wonder the quality of software never goes up - while the building blocks have gone up in quality. an analogy is like having super strong bricks but making brittle structures
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>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
We have internalized more knowledge than we can explain sounds like the textbook definition of Polanyi's paradox:"Polanyi's paradox, named in honour of the British-Hungarian philosopher Michael Polanyi, is the theory that human knowledge of how the world functions and of our own capability are, to a large extent, beyond our explicit understanding" [0]
[0] https://en.wikipedia.org/wiki/Polanyi%27s_paradox
Thanks so much! I hadn't heard of Polanyi's paradox before but it is exactly what I was talking about. The Wikipedia entry even highlights the problem for AI-driven development:
> Polanyi's paradox has been widely considered to identify a major obstacle in the fields of AI and automation, since programming an automated task or system is difficult unless a complete and fully specific description of the procedure is available.
cf https://en.wikipedia.org/wiki/Dual_process_theory as a causative agent behind the paradox.
I think the answer is in the second sentence of the article. The most valuable person in this new world (using the author's own phrasing from the penultimate paragraph) is the one who is good at "building a working model of the domain in [their] head".
Depending on the domain, this may either be the domain expert themselves, or someone else trained in formal logic, data structures and organizing information into coherent hierarchies. If this is neither the domain expert nor the programmer, then it must be someone in between. In the old world, this role was called "business analyst".
I'm this person, and I do find AI to be quite helpful, though I'm mostly just playing around at this point.
I'm the daughter and granddaughter of programmers, and I learned the basics of how to code as a kid. I'm good at it and have a knack for it, but I didn't want to do it for 8+ hours a day and then spend my nights on it as well, so I didn't pursue it as a career. I did an undergraduate degree in Linguistics, which has been really helpful for having an intuitive sense for what 'language as data' can accomplish and for a strong understanding of the difference between language as data and language as meaning. I studied formal logic systems. Then I did a graduate degree in Library Science and worked in libraries for a decade and a half.
I can organize and define systems very well, and I'm trained in how to wheedle information people don't consciously know out of them without them knowing I'm doing it. I've spent enough time around actual devs to understand where my limitations are and when to loop in someone who knows more to check my work, and when it's important for the work to be super accurate versus when I can learn by fucking around. (Front end and design? Fuck around! Database structure? Fuck around but with an exceptionally robust backup system kept outside of the AI tools' purview + don't fuck around in prod. Storing credentials and people's information? Ask someone.)
The problem companies are going to have is I'm very disinclined to work for them doing this, particularly if they want us because they think we're going to be cheaper. Most people who are in this category a.) could be devs and opted not to, and there's a reason for that and/or b.) are the children, cousins, etc. of programmers. We're not stupid: we know we're just as disposable as they're trying to make devs.
I think this neatly captures the gap in the article; it’s thinking of domain expertise as static, rather than something that needs to be built/extracted.
It also goes the other way; A a good engineer can build experiments and discover the domain even without an oracle on the team; reading protocols or manuals or specs, for example. In other words, learn to be a domain expert themselves.
Personally I find the most efficient way to learn a concept to be building it; you are immediately faced with your blind spots. AI for sure helps improve cycle time on these discoveries if you use it that way; time writing boilerplate goes to ~zero and you can spend your time figuring out how to answer the real questions.
I do think that we as a profession need to learn new architectural and craftsmanship patterns to make it easier to learn from our code; concepts like Literate Programming which were too fussy for fast-moving teams may end up accelerating in the agentic world; I need to read a lot more code than I write now. But also things like Acceptance Test Driven Design; not new concepts, just newly increased in value.
Having worked with domain experts I concur on the difficult time they have expressing the rules of their domains.
Once I built a little domain-specific language for them, that was tested against old jobs to see if they contradicted the past; it was a nifty project and since then I am convinced that DSLs are underrated as a way to encode expertise.
Can you give an example (even if contrived) of how that would have looked ? I’m very curious !
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Scala to the rescue, then! :)
WRT the native grammar, consider adjective order. Few native english speakers (me included) can off the cuff name the proper order, but everyone knows the "right" order.
https://dictionary.cambridge.org/us/grammar/british-grammar/...
Another one: tekamolo, for German and adverbs this time.
Temporal, kausal, Modal, Lokal
https://www.olesentuition.co.uk/single-post/tekamolo-how-to-...
Great example
I have found this with (mechanical) engineers. They know what they want to see but don’t understand the underlying details which are more mathematical than the average engineer is able to work with. So the people working on engineering software are often physicists or applied mathematicians.
> 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.
This combination of requirements is the business in most domains. Software or otherwise. Codefying different rules and executing them.
I think that you raise some excellent points here. I want to dive deeper into what is meant by domain knowledge -- as a I see it. This phrase comes to mind: "Those who know, do. Those that understand, teach." (Google search tells me that it is frequently misattributed to Aristotle.) People who cannot clearly articulate rules for their domain only "do". However, those that can clearly articulate will "teach". Thus, I would say true domain knowledge is the ability to teach that domain to others.
I largely agree, but want to note that there’s the counterpoint “Those who can, do; those who can’t, teach” (from George Bernard Shaw’s 1903 play Man and Superman), which also has some nugget of truth in it.
I agree with your point that people “from” the domain aren’t automatically equipped to start AI-building software in the domain, exactly because they often lack one of the most crucial software development skills — being able to (and having the desire to) describe a complex system with a finite set of deterministic rules.
But I don’t think we should be calling these people “domain experts”. I think we should reserve that name for the other group, for the people who truly and deeply understand the domain, the whys and whats and why nots.
That ship has sailed. Anyone who works in a field is now considered a domain expert (or "SME") even if they're nothing of the sort. There should ideally be another term that's a superset, but I doubt it would ever catch on.
You're doing a bit of a "true Scotsman fallacy" here.
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Agree. If you can't explain it to someone else, you don't really understand it yourself. These people are practitioners not experts.
People you say are not experts are fully capable to explain whys and whats and why nots. In more details that you can absorb.
They will do that on examples tho. They will recognize and explain every single exception they see - but they are not able to list all exceptions up front. Because, that is highly unusual task
> 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.
Right, so the spec is derived from examples, an interactive process that doesn’t require a programmer.
Interpolating between the examples still requires understanding of the domain for the interpolation to be a sensible one. And it’s an iterative feedback process: thinking about possible interpolations leads you to cases that need clarifications from the domain experts. The domain experts won’t come up with all relevant examples by themselves, and they typically aren’t as good at thinking about interpolations like a developer who is trained to always consider all possible cases; they don’t think in terms of models the way a developer does.
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The programmer skill is how to abstract the specs from all the examples. And then to formalize it. Actual coding is merely translation. And only beginners tend to focus on that.
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This is the exasperating part about learning to speak Spanish using a textbook; you must guess the grammar rules because the textbook won't tell you. So, you use the English rule and hope and pray that it is the same in Spanish, and you'll be right the majority of the time but often wrong. Spanish textbooks written 100 years ago tell you the grammar rules and are more useful than recent textbooks.
While interesting, I rarely found knowing grammar rules beyond some very basic ones to help all that much for learning to speak a language, compared to language exposure and speaking practice.
Yes. The author stumbled on the p vs np problem but with humans. Verification is easy. Solving is the millennium prize question.
I agree that it has some resemblance, but the striking thing about NP-complete problems is that an efficient solution to any of them is an efficient solution to all of them, which makes it worth trying exceptionally hard to find one.
It could be that whatever lackluster expertise you can squeeze out of an LLM is good enough to discourage investment in the real thing since unlike NP-complete problems, expertise isn't generalizable.
Agreed. But there is a new dynamic coming to place. Who can execute faster now that we have AIs? I think the combination of (Good SWE using AIs) collaborating with (Domain Expert using AIs) is the winning team. Things are accelerating on all fronts, the frontier may be jagged but AIs is making progress on all capabilites.
Domain expertise matters more than most bootcamp grads want to hear. Knowing how to phrase a query for a specific domain is half the battle.
> 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.
This is the part I disagree with.
In a non-agentic world, the expert and the programmer are two different people. If the expert finds a bug in the software, they have to describe that bug, send it over to a programmer to fix, wait for a new release and until that scenario occurs again, realize that their description was actually wrong, send a corrected description, rinse and repeat for a few iterations until the bug is finally fixed for good.
In a world of agents, the expert finds the bug, asks the agent to fix it, realizes that the fix is incorrect because of sloppy thinking, does a few iterations until the feature works correctly, and that's it. Bug fixed; with 10 minutes of work instead of a few weeks.
> realizes that the fix is incorrect because of sloppy thinking
If the domain expert doesn’t understand the generated code, they can only discover incorrect logic by specific examples (specific inputs), which is usually impossible to do exhaustively. The programmer, on the other hand, can see incorrect logic directly, generalized over all possible inputs and states. It’s the difference between testing and proving correctness.
> In a world of agents, the expert finds the bug, asks the agent to fix it, realizes that the fix is incorrect because of sloppy thinking, does a few iterations until the feature works correctly
That would require clearly (a) knowing what you want, and (b) expressing it unambiguously and in detail, including all edge cases. Essentially producing a spec.
Most people are not able to do either. Talking to an LLM does not change that.
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I had the same problem in air travel industry. Our ticketing officers were never able to communicate how the ticket commission rules from our partners should be interpreted. I ended up adding a test framework, that allowed to add examples and counterexamples of existing flights to each rule, to verify that they are applied the way they'd assign commission by hand.
You described reasoning by analogy, which two thirds of the population does.
Please explain explicitly what you are implying, because I don't get it
Who are the 1/3 of the population that does not reason by analogy?
This is basically the gap BDD tries to close I guess. It assumes that domain experts can't fully articulate the rules upfront.
I fully agree here. It's also not uncommon to have the reply for specific items be "Ahh, well that is a bit of an edge case see because of X and Y" and then you learn there are many many "special cases" to the point that they aren't that special anymore.
Some companies use product managers for this purpose. They translate the implicit domain knowledge of those experts into explicit requirements, mostly through interviewing.
> 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.
Maybe they need a build a hybrid expertise of "domain" and "software engineering". For example, robotic surgery requires expert surgeons to build sufficient expertise in robotics
Also, noticed a pretty high karma for a throwaway account.
Are we thinking about the wrong side here? Wouldn't the LLM be a super-intelligent financial expert as well as a super-intelligent coder?
It is neither.
I guess the flip side to that is the iterative conversation to develop these specifications is exactly what AI chatbots are good at.
It's also the main reason why very structured AI agent orchestration for software engineering modelled on rigid processes fails to really provide much value.
I suspect that the reason AI chatbots are not very good at developing such specifications is that they don’t build a domain model in their “head” — the only models they have are already hardcoded in their weights. A human learning a domain, on the other hand, forms new neural connections representing the model of the domain that they build in their head.
Things the domain expert on the author's example cant tell (from the top of my head):
- 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.
> Notice which way this cuts. Pre-agent, the engineer had a path the dispatcher didn’t: they could go learn the domain. Slowly, painfully, by shadowing experts and reading specs and getting things wrong in production, they would build the mental model and then they could build the system. That path was the whole career ladder in a lot of fields. The domain expert had no equivalent path, because learning to build reliable software is years of work they were never going to do.
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.
Such a good example of this I encountered recently:
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.
Product management is its own skill, and few true domain expects have it. Without some form of PM, the resulting software will end up a mess due to poor UX, too much bloat, etc.
I think AI is going to force most software engineers to pick up this skill in some form. Building is easy; knowing what to build is the hard part.
>> 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.
They can, and they are. You just don’t hear about it on places like HN because those people are not on this website. Which is why some people here make smug statements like “if LLMs are so good at programming how come we haven’t seen any useful apps made with LLMs???”
"So the most valuable person in this new world is the one who has both skills because they can verify at both layers. They know the generated code is sound and they know the answers it produces are true."
This has always been true since the dawn of programming.
Cannot agree.
Whole TFA doesn’t take into account reason why software development was actually so valuable.
Single specialist in any domain is not that valuable. You may charge $200 for hour of his time sure. But to grow company you now need N specialists.
What software was doing was not making specialist obsolete replacing a specialist by encoding his knowledge - well many tried to do so but failed even in 80’s “expert systems”.
What software was doing was making it possible to structure specialist work, make it possible for a single specialist to serve more customers at the same time, make it possible to hand over work in a structured way to junior specialists, making it easier for senior to take over edge cases and spot check work of those junior specialists.
This setup allows company to not being tied to number of specialists to grow, this setup allows company to charge less per customer but take over more of the market share.
Whole premise that now each specialist will waste time dabbling in AI software development is ludicrous, especially if each specialist would be building his own tooling somehow.
That sounds more like the extractive setup of corporations like IBM, where return/specialist must be maximized against the number of clients that can be juggled simultaneously.
Whereas in larger technology firms, yes that happens to some degree, but only with the highest level specialists such as PEs, fellows, etc.
They are already so wildly profitable and valuable by the simple nature of computing itself: it scales second only to money with regards to compounding effects. Once you have software someone can use, it scales near infinitely to more users, thus value is extracted from the work of a single specialist for all time. No need to complicate it with trying to abstract work juniors can do (and fail) and then have seniors correct. What would they be doing in non-extractive firms?
Yes, but most people (especially a large portion on HN and Reddit) do not internalize it.
A SWE who has always worked in DevTooling companies will always be preferred by DevTooling companies over a generalist. A SWE who has always worked in AdTech will always be preferred by AdTech companies over a generalist. etc etc.
Software fundamentals - though useful - are table stakes skills at this point. No business wants to deal with the headache of on-ramping employees who have never worked in a specific domain or industry because it takes too long for a generalist employee to build the intuition needed to understand that segment of the industry.
> Software fundamentals - though useful - are table stakes skills at this point.
I'm having a difficult time even seeing what we're talking about here. I see "seniors" in our industry that don't know what I would call the fundamentals of programming or software development; apparently not all fundamentals are created equal.
And therefore there will be a huge knowledge gap as companies refuse to hire anyone who hasn't worked in the field for 5+ years and people who want to work in that field but haven't don't get hired.
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> No business wants to deal with the headache of on-ramping employees who have never worked in a specific domain or industry…
The usual sickness. If you don’t train people to become specialists and just expect them to fall from the sky, it’s only a question of time until you run out of specialists.
The software generalist described in this post has domain expertise as well. In software.
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.
Exactly, plus you now have a new superpower with AI — the ability to dig into and ramp up expertise in mostly any domain. I’d say the article gets it backwards.
True that AI lets you get information fast an efficiently. But it might just make you feel like you understand, even if you don't have the kind of hard won domain intuition that this article is talking about. I certainly often get delusions of grandeur when I learn a lot of something and haven't yet suffered with it in practice.
Expertise is largely tacit knowledge, reading AI slop isn't expertise.
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Exactly. The software engineering domain is huge. I could code just about anything when I was 16 years old after 2 years of intensive learning... It took me an additional 10 years to learn to design software that is secure, maintainable, efficient and scalable.
I find this take off. In general, LLMs collapse a bit of all knowledge work, including many domain experts. I work with domain experts often and it’s usually the most difficult part of the process. Now, I can ask an LLM the questions I need to design the system and have an agentic system help me build parts. And it works, quite well.
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.
> I work with domain experts often and it’s usually the most difficult part of the process. Now, I can ask an LLM the questions
And you will not notice the obvious errors in its answers to even basic questions, because you are no expert and should have really consulted with one.
When talking about their own area of expertise, almost everybody immediately notices the glaring faults. But then they are very impressed and take LLMs as gospel when talking about anything else. I do not get it.
How would you know the agent is correct in domain expertise if you’re not possessing domain expertise yourself?
Shallow generic domain knowledge is not really a helpful domain expertise, is it.
Lets take banks - knowing how banks work in general means little for that one specific bank who is paying you, which has different portfolio than most, different processes because everything is homebrew over decades of doing business and they differentiate from rest of the market in many ways and thats their key proposition, different data infra, set of internal apps, protocols and so on. There is no general llm knowledge you can distill in few minutes that would help you much.
You need to know your specific company, no way around it. In fact, to be proficient in those deliveries, you need to know those internal politics, processes, their quirks and so on, 100x more than some code fast delivery since this is majority of any bigger project. You need some reputation to get things done effectively.
This, for some time, won't be something llm can massively improve, unless those companies change themselves.
>> You need to know your specific company, no way around it
Of course there are ways around it. For example you can build the foundation yourself (which will cover the 80% that is shared across every company in that industry) and give users the ability to build the rest themselves inside the software. This type of abstraction-based architecture is how platforms like Salesforce took off and became ubiquitous.
> So the most valuable person in this new world is the one who has both skills
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).
Very well articulated for sure. But, I do think the word _'expert'_ always tends to do a lot of heavy lifting.
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?
Whenever I read articles like this one that appear to be very generic and give advice about dealing with AI, I remind myself that he software industry is like the construction industry.
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.
As a doctor who learned how to program, I started by writing useless Chrome extensions. One thing I learned early was that programmers generally do not like learning the nuances of medicine, and many are quite open about how little they want to learn it. I have tried convincing my fellow residents to learn a simple language like Lua or even just Python, but the resistance is even greater, despite the fact that they constantly express how they have always wanted to learn programming like I did.
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.
My friend is an electrical engineer and just passed a FIDE chess rating of 2000. Has played for 30 years, started the chess club in high school. Knows a little programming from the stuff he had to do with microcontrollers in college.
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?
Chess is a precisely defined mathematical problem in a fairly simple artificial world. Most domains aren’t like that.
I think a charitable interpretation is that from the perspective of AI, some domains are shallow (like chess), and some are deep (you can fill in the blank here).
What would fill in the blank be? Because this was actually kind of a test for me to address the question of "does AI just amplify domain knowledge?" In this case, it seems it didn't.
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Writing software for which a full spec is available before you begin — in this case the rules of chess — was an easy problem even before AI.
There's no chess spec, it's not "solved" like it's just a matter of implementation. Chess bot programming wouldn't be a thing otherwise. I think it's really quite funny that you think this super complicated game is just some trivial programming problem. Try it.
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I think this might be one of the worse examples of the dynamic, for the reasons already mentioned by others (programming a chess bot is really more of a programming exercise than a chess exercise), but it’s food for thought, so thanks for posting it. Some IRL domains are definitely more chess-y than others.
Everything is a programming exercise. Can a person with domain knowledge and NOT programming logic succeed. I thought that's what this was about. Ok, sure, AI is good at programming.
what does actually playing chess have to do with writing an efficient game tree search algorithm beyond a few simple principles? You challenged him to a programming contest and won, as the vastly more experienced programmer. Even though he could use AI, your domain knowledge here proved to be the deciding factor.
I had never tried to make a chess bot before though, we both started at the same spot. There are obvious things you can search for to make one. We both had the same information there. The domain was chess, and his expertise didn't help him win. If you are a chef, shouldn't you be able to make better recipes with AI? If you are a fitness trainer, better routines? Etc? Or is AI only for programmers?
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A terrible example, because chess is very, very simple - deterministic, rules fully specified in a few pages. We're talking about how you operate in a "games" which, among other complexities, involve the economy and human social dynamics. Billions of other agents. Find a plumber and try to beat them when someone calls you with a clogged toilet. Find a teacher and try to beat them when a student is repeatedly acting out.
"the binding constraint has moved from can you build it to can you tell whether it’s right."
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
Not just domain expertise. The hard part has always been marketing, distribution, risk appetite, motivation, grit, and patience.
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.
> risk appetite, motivation, grit, and patience.
This is the moat. It was before AI and still is.
But there's nothing stopping AI from developing domain expertise. If you fine tune a model based on the records of all previous work (effectively "shadowing" the existing workers) then it can easily learn this domain knowledge. The only difference is that AI companies have gone after programming domain knowledge first. Others will come later.
It was never about the code.
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.
You are wrong. Writing programs (atleast efficiently) is not the easiest part. It just appears to be once you get accustomed.
Domain expertise is hard but not that hard compared to the insane mental discipline required to write efficient scalable code.
Domain expertise is valuable and hard but I don't get this "domain expertise is harder than disciplined coding" mentality.
Domain expertise is hard but not that hard compared to the insane mental discipline required to write efficient scalable code.
Not all code has to be efficient scalable code. I know some domain experts that were not programmers. They picked up enough Python or Swift + UIKit (this was pre-LLMs) and their applications are now widely used in their domain. In some cases, they contracted some software companies in the past to do the work. However, they did not understand the problem domain well, requiring the domain expert to iterate with them over and over. In the end it was more efficient to let the domain expert to learn enough programming do it with some guidance from experienced programmers.
Also remember that a lot of domain-specific apps do not need to be huge. E.g. I was once at a factory in the 90ies and they needed to do time-consuming calculations for a particular expensive machine. So, someone who worked at the factory wrote a small program in Excel + I think VBA. This accelerated their work extremely and probably saved them 10,000s or 100,000s in manual labor. It was easy for a non-programmer to write, because they knew all the details of the machine and the calculations, since they had done it over and over again. But it's not a enterprise web app or anything and it does not matter if the program runs in 1 or 4 seconds.
By the way, I also consider efficiency to be a specific knowledge domain, or architecting a very large project. So in the end it really depends on how narrow or wide your definition of 'programming' or 'coding' is.
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> Writing programs (atleast efficiently) is not the easiest part. It just appears to be once you get accustomed.
Programming is the easiest part! (c) decade+ experienced senior developers
Right. If you're spending your time among developers to whom the code part is second nature, the differentiating thing becomes domain knowledge. If you spend your time among people of that domain who have no coding skill, to them it will appear to be the opposite.
I think there is some prestige factor in the background too. Being seen like some kind of code monkey is to be avoided like the plague. Business people really don't value the math savant engineer nerd archetype who just codes and codes.
Highest prestige is always taste and judgment, not correctness and skill. This is why people will also talk about how business soft skills, communication etc are more important than hard skills like programming, math, etc. I think one should be careful with this. On the one hand it's true that career progression is quite dependent on soft social skills, but there are also really hard kinds of software jobs where cognitive horsepower and technical experience are absolutely crucial and soft skills won't save you.
> Domain expertise is hard but not that hard compared to the insane mental discipline required to write efficient scalable code
"efficient scalable code" is just as vague as good code. How are you going to know your code is scalable if you don't understand your domain? Scalability is not something you sprinkle onto code.
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“Teaching and engineer to understand the minutia of fund accounting well enough to build software for these firms is tough.”
Sounds like you need a senior familiar with finance/accounting and a family to feed. Email me!
Hah very clever :)
Am available.
Maybe we can grab a coffee.
IDK, there's a tipping point where at best domain expertise without skill leads to a LOT of tech debt.
In fact about half of my career has been dealing with 'domain knowledge at least present enough to get the ticket/epic closed but leads to a lot of tech debt'.
i.e. a good portion of my jobs have involved a lot of a good amount of:
- Review PRs with a fine tooth comb because despite domain knowledge, people are human and can either don't know any better, make mistakes, or willingly refuse to integrate feedback, or worst refuse to double check what the coding agent wrote for them.
- 'refactor this thing because it was technically correct but written so poorly that it leads to timeouts and/or a Manager/DBA is screaming' [0]
> 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.
A truly good software engineer is able and willing to learn the domain, but there has to be a way for them to learn. I say that because I've been at shops where various levels did that (i.e. sometimes the company itself, sometimes the team, sometimes colleagues) and I've been at shops where everything is lip service and at best you can only glean from what's in the JIRAs and what you can glean from what people outside of IT say in meetings you are in.
> 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.
I think a big paradigm shift especially in the past 5 years has been that most companies are expecting folks to work to the bone, and it winds up being counterproductive because it prevents anyone from being able to have the important conversations.
Culture is a huge factor in this, I've worked at shops where at the very least you could easily have a side conversation or a meeting, and shops where you might as well sign a change.org petition to request time to talk about it properly.
Still, you are right at the crux; Requirements matter more than code at the end of the day. I've been at shops where a person's definition of 'Correct' meant a feature got delayed despite all requirements being met, because they didn't like they way it was written after they were gone the whole time it got implemented and the rest of the team approved all design decisions.
[0] - Next thing you know you learn about a 'batch process' has %numberOfRecord%*10 inserts, possibly with additional fetches given a poorly designed data model to where it is doing SQL upserts in the most wrong way (i.e. doing a get from the DB and then adding a record to be inserted if not present.) and they keep doing more and more questionable things to 'improve performance' rather than rethinking the data layer's query pattern. Seen it more than once in my career.
Tech debt is what most orgs/managers ask for. If nobody is complaining, there's no problem. Nobody but you knows what's behind the scenes until they ask for something new that the tech debt makes take longer than you think it should. But they don't perceive it that way, because they're probably not software people. They just see it as a new ask that will take a bit longer than expected.
Nobody cares about quality, if they can't perceive it. They want practical results. AI will give that to them, to some degree. Most people don't see software as deterministic anyways, so sometimes not working doesn't register as the monumental, show stopping, failure that it would me or you.
I work as an analyst, and our group has roughly 20% analysts with strong technical (software engineering) skills, and the rest are more traditional analysts / domain experts.
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.
Our group is slowly replacing tableau with self built tools, with HUGE performance gains. I think these BI companies are in deep trouble, especially ones like tableau that make drawing something simple, like a histogram, near impossible.
Kernel developer is not the job same as a game developer or an ERP integrator...
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.
This sort of thing has Microsoft Access vibes about it. All AI is enabling is domain experts to spin up their own software systems with no understanding of how the systems should be put together.
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.
I recently reviewed an app built mostly with vibe coding. The owner said it was almost ready to launch and just needed a quick check.
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.
Where I am headed, I think, is to basically be a platform engineer. The job is to create the guardrails, validation, prompt library, and both agent and manual reviews; that keeps the domain experts safe when they start using coding agents.
It's a little bit like being T2/T3 customer support [or support engineer], but internal. You're there to catch the dangerous spots, the weird edge cases, and to make sure that everything is set up correctly, rather than to solve 100% of the routine problems yourself.
There's also plenty of room for cross-cutting-concerns, of course
Eventually infrastructure will be more simple to orchestrate too without faults I suspect from well developed devops harnesses. The risk and scale companies are willing to accept will still fall on humans for some time even then. I don't see most people vibe coding a million user app that has deeper needs than the basics we see now.
Can you elaborate more on this type of role? Stack? Etc.
> I used billions of tokens last month alone.
I use Claude Code (Opus 4.6 at max effort) all day long, and I genuinely don't understand how this is possible. Is that usage paying off?
This is very likely due to my lack of understanding, but... how?
Long codex sessions lead to a lot of cached token hits, esp when you resume them after a few hours.
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Vibe coded a simple game (10,000 tokens of source code) with two popular coding agents. (Once each, to compare.)
One spent 200,000 tokens, to produce 10,000.
The other spent 1.9 million.
It could have been a single LLM call (10k tokens). lmao
(I note that the latter was designed by a company whose main source of revenue is token spend...)
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Don’t forget context. Basically I have 2 billion input and 1 million output. Every prompt you do, sends back the whole thing again and again. Let’s say you have 500k context used, you send 10 messages is 5 million. 100 messages 50 million. Use 5 threats is 250 million.
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Honestly, this is my experience as well. LLMs make it easier to explore other domains, but they do not make you the master of one; you still need expert domain knowledge.
That said, they do make excellent tools to quickly try out new ideas and dive into them; they can even be great learning accelerators if you have a curious mind.
Domain expertise combined with a QA mindset could replace SWE, but consistent QA mindset is rare
I agree that a consistent QA mindset is rare, but I'm not sure even if present if it's enough to replace an SDE.
I very recently looked at the codebase of a vibe-coded app made by someone with domain expertise but no software dev experience.
It was very clear to me that he had described it from his POV to an AI, and the AI had implemented features in a manner that technically worked, but made future maintenance or expansion extremely tricky, which is why he was now looking for a dev.
For example, in his data schema, for every item on a menu, instead of simply having an array property like so for ingredients:
He had individual flags for every item for every possible ingredient it could have or not have:
This technically worked and passed tests from his POV at an MVP level. But added a lot of complications when actually trying to build more features or when a new menu item had ingredients the founder hadn't thought to include in the schema beforehand.
I totally get how he ended up where he did though. While describing it to the AI, he probably said something like "store info on each menu item's ingredients, they might have milk or coffee or sugar", and the AI created individual flags for them and he didn't think to question it, because he didn't know what's "right" or "wrong", but then as he kept building the AI stuck with keeping individual flags instead of swapping it out with an array mechanism, and he couldn't have known the correct way to implement it.
Only a dev with experience would know how to describe the system to an AI model to get an output that works well, and how to assess the quality of its output beyond what can be assessed through the basic UI. This wasn't a QA failure, it was a design failure.
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I disagree. At some point of complexity, building it yourself is faster, better and (as we're finding out) cheaper. And more fun, although that varies person to person.
Wrestling with a code generator also creates a sunk cost fallacy where progress grinds to a halt but you still try and use the tools to fix the problems the tools created. Or you go in and fix things yourself, in a codebase you don't truly understand. A single developer can recreate the contextual nightmare miasma of a large corporation all by themselves.
There's also an emerging market consideration: MVP are easy to build so time to market is no longer hard to achieve. It's not a differentiator.
X was built in 3 days but is slow and riddled with bugs and security errors. There are also A, B, C, D and E which are effectively the same thing built just as fast.
Z was built over six months and is rock solid and performant.
Who wins the market share?
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Personally my ability to understand atrophies / is reduced when compared to writing code ‘myself’ rather than fully being a reviewer.
Probably similar to hand writing notes (while digesting + synthesizing and not just being a scribe) vs reading notes somebody else took.
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> Domain expertise combined with a QA mindset could replace SWE, but consistent QA mindset is rare
I've heard this story at least 3 times already:
- Domain expertise combined with outsource could replace expensive US SWE
- Domain expertise combined with SWE could replace QA
- Domain expertise combined with SWE could replace infra engineers
Why is everyone so preoccupied with replacing someone with someone instead of doing their fucking job?
You can't test quality into a product. Regardless of how much of a "QA mindset" you have, you can only ever find a fraction of defects and technical debt through external testing. This can be good enough for a throwaway app that will only be used by a limited customer base for a limited time. But that approach quickly bogs down if you try to scale it into a product that will be used indefinitely by a huge set of external customers. At some point velocity drops to near zero because the code base is such a mess that it becomes impossible to add new features without causing regression defects or breaking backward compatibility.
The engineering part of software engineering is the hard part for LLMs. How is that replaceable with these skills?
I don’t think so. Most things are sufficiently complicated enough to require multiple domain experts working together to achieve a goal.
The dunning kruger effect is in full swing as people think AI replaces the domain expert need.
Most of the value in the expert isnt the 80% but the tail 20% or 10% where AI fails. For a one of personal app or website, 80% is plenty but only that.
Totally agree.
I disagree.
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.
Agentic coding favors senior generalists with a broad experience in many things rather than narrow experience in one or two things. I no longer think of myself as a backed engineer. That's what I was. I can do it all now. I've been building products and doing CTO jobs for a few decades now. I'm not a specialist in all layers of the software but I know enough about all layers of the stack to be able to do things with an agentic coding tool.
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.
> Agentic coding favors senior generalists with a broad experience in many things rather than narrow experience in one or two things.
Is it? Agents are coming for generalists first.
> Agentic AI severed the link between the two. You can now produce the software without ever building the model, and that breaks an assumption the whole profession was organized around.
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.
Nice idea, but engineers are engineers for a reason.
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.
From my experience what domain experts are often missing - and at least currently this is also an area where LLMs fail - is the ability to model data and interfaces in a sustainable way and factor in team and domain boundaries.
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.
I'm such a generalist. During all my career, from support to decades of C-level, I was equally challenged by domain and IT people for not being deep in everything. But whenever they couldn't come up with a compromise, I was the person who always offered it. Doing purely consulting work now, mostly implementation. And this "little bit of everything" can be strongly multiplied by AI tools, since I already know what exactly I want to achieve, I just need speed and a cross-check.
How much pontificating needs to be done before people acknowledge nobody has any idea what to do with AI on an individual level?
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.
An idea that's beginning to solidify for me is that AI tools make software development harder.
It's harder because they dramatically raise the bar for what's possible to do. An individual developer can take on significantly more challenging projects now, because the ultimate constraint has always been time and AI can help you get more done in the time available.
But the stuff you can get done with that time is a whole lot harder. You have to understand lots more things, and get radically outside your pre-AI comfort zone.
It used to be acceptable to spend several days refactoring a codebase, or figuring out how to ship a small feature because it's in a part of the system you hadn't worked in before or involved learning a new library in order to build it.
Coding agents mean you can climb those curves a whole lot faster, but you still need to climb them - and the volume of information coming your way is much higher.
If you're worried about non-technical vibe coders taking your job, the correct response is to be much better at building software than those vibe coders. That means you need more skill, more ambition, and more experience. It's hard!
From that perspective, development has always been harder since I started. I left college with a copy of K&R and remembering courses that applied to real life immediately, because data structures and such were just what we had. In my first job, I ended up writing a code generator to help serialize a large number of data structures, straight from a compiler design class.. which right now you don't need to know a thing about, because serialization and languages with introspection are everywhere. The knowledge you need to be a professional engineer just kept going up through the last 30 years, while most of the basics became far less relevant, because the libraries just did it.
AI raises the bar again, as its probably at least as good as me, if not better, at anything I learned in college. I've spent years living off of random trivia from the last 30 years, as I saw computing grow with me. How do you know this?! Because everything built on top of it didn't exist when I was your age, so I had to learn it! But well, nowadays the AI is better at that trivia too.
The world moves, we do what we can with what we kno. It's not just programming, but what innovation and automation has done to the vast majority of things humans have done to be productive for each other since humans are people. We'll have to cope, like the guy that bred oxes to pull the plows.
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> If you're worried about non-technical vibe coders taking your job, the correct response is to be much better at building software than those vibe coders. That means you need more skill, more ambition, and more experience. It's hard!
This is a false fear. The real risk isn't that some 19 year-old vibe coder is going to replace you, it's that there's simply less need for more experienced engineers. The market is shrinking.
Also, even if the premise behind the SaaSpocalypse is naive and oversimplified (companies aren't going to replace all their SaaSes with internally vibe-coded replacements), it looks reasonable that net-net AI will have a negative impact on the value of software.
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I worry this is looking at where the ball is now instead of where it's going. The recent disproof of an Erdos conjecture should put to rest the idea that LLMs will reach a skill ceiling before they reach superintelligence.
I believe we are headed for a world of superintelligent AI where LLMs are much better at logical thinking than humans, the same way that chess engines are much better at chess than humans.
In that world there's really nothing humans can offer in terms of logical thinking other than their humanity itself. An 8 year old with Stockfish can beat Magnus Carlsen, and an 8 year old with Codex (and daddy's credit card) will be able to beat me at software engineering.
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That's true but in a way it's also more fun and engaging because the tedious stuff just gets worked through leaving you to think about the bigger picture items.
Though I'll say I don't buy the stuff about AI "democratizing" development since making it much more capital-intensive kind of has the opposite effect for anybody doing dev work at home.
That's a roundabout way of saying it makes software development easier. Perhaps even a 180.
But yes - once it's that easy you have to step up your ambitions.
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> If you're worried about non-technical vibe coders taking your job
I'm not worried about non-technical vibe coders taking my job. I'm worried about psychotic VCs and CEOs putting me on the street in the name of "optimization" of "lower value human capital".
I had the same thought recently, I've had it happen to myself.
I've been working on something relatively large and greenfield recently.
A big chunk of my time is spent thinking about the hard parts. The raw information processing rate needed to keep up with the state of the project is high.
It feels almost like mental athleticism, whereas coding used to be a rather chill activity.
developers now are expected to randomly jump around projects and ship without friction. For employers it means they can move us around like pawns. Lot of companies have not reorged themselves to this new type of workforce thats much more malleable.
it used to be that i pay your due at some enterpise and learn some corner of codebase really well and become go to person. that would give you job security.
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You will never beat the vibe coders.
The vibe coders have a key advantage you don’t: they don’t give a fuck.
They blow through a task and move onto the next one. Management sees this as progress, and the vibe coders are rewarded.
When shit breaks later on down the line, and fires have to be put out and things rewritten, the vibe coders do NOT get the blame. They do NOT get punished. Most engineering teams operate on a blameless culture. If code was approved for production, then it should have been good enough. Vibe coders will keep on doing what they do, and skilled experts will be left cleaning up their messes.
For anyone who actually cares, it’s over. You are not steering development anymore.
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Not really. The primary stopper was never time or effort. It was need (and wisdom). If a project was important enough, you’d do it. If it’s not, it falls on the wayside.
Now with LLM tools, what you got is a slew of projects their creators aren’t even interested in. It’s theater.
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That’s the trick - always stay hard!
LLMs are an additional tool to add to your arsenal. They are not omnipotent and need care, just like any other tool.
My best effort, so far, at an analogy is a modern drill driver compared to a screw driver/brace and bit/etc:
You can get some remarkable results in a very short time compared to the "old school" gear.
You can get some "amazing" anecdotes eg "I screwed down an entire floor at 16" x 1" c/c within an hour instead of an entire day and I took loads of fag breaks" (I could have used a nail gun instead in half the time but I'll never raise that floor easily in the future, and probably done at twice the cost)
I have several on prem LLMs and access to the rest and I'm pretty sure I'll be extending my analogy to ... brand, eventually.
What I do not expect to be doing is looking for a new job. A drill driver is not a carpenter/site labourer/useful without a person!
But a modern drill absolutely 100% removes the need for a brace and bit. An LLM doesn't replace any existing tools.
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Remember the OOP Hype 20 years ago? I'm still cleaning shit up from then in our codebase when everyone used patterns after skimming through the GoF book without even knowing why .... My prediction is in 20 years I will clean up the shit that was co-authored by Claude ...
https://mastodon.gamedev.place/@JeremiahFieldhaven/116654345...
OOP and GoF are not the same thing - conflating the two has been the biggest mistake everyone has made (detractors and proponents alike)
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The idea that a lot of people have in their heads is that if the code doesn't fit the need, you'll just start over completely with a fresh codebase, because it's so fast to generate it.
In other words, there won't be 20-year-old code in this future.
We'll see if that comes to pass, but there's a lot of money betting that will happen; enough to where people will disregard what the downsides are.
my roommate at the time was trying to implment all the patterns from gof book at work. it was ridiculous but hype was ridiculous.
It all feels to me similar to how spectators or laypeople judge pro sports.
Don’t quote me on this, just trying to make a point:
They’ll say you need perfect symmetry to do well in sports, which is highly correlated to development stability in the womb; higher symmetry = perfect development.
Then after some years, news will come: Bruce Lee’s one leg is shorter than the other by a significant amount, and Usain Bolt has a similar asymmetrical development.
Then they’ll flip-flop around their initial argument by claiming that they are outliers so the general rule need not apply.
brother just build what you find interesting and it may work :)
It's well established that injuries occur at much higher rates when you have asymmetries/imbalances. Not even just physical "deformities" but just when your body is shifted or twisted in some way such that you don't move with balance.
Haven’t you heard? If you don’t adapt now you’ll be left behind, never to be able to work again! Copilot? That’s so last year. Agentic engineering? You’re already late!
jibecoding is the latest thing: you insult the LLM's bloodline providing it with greater motivation to complete the code you won't review without bugs.
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Do you have a link to your paid content that will put me ahead of the curve so my career will be bulletproof? /s
> 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.
Little thing to keep in mind about AI: a technology is only called AI while it doesn’t work yet. Once it works reliable, we give it a proper name and something else becomes AI.
"AI is whatever hasn't been done yet" - Larry Tesler https://en.wikipedia.org/wiki/AI_effect
I think it's more: AI assisted engineering is a new skill people are trying to develop and we're on this collective experimentation process, working out how to use AI for engineering with varying degrees of success.
If that's true, any statements defining what is necessary to do should be ignored in that context. I'm still interested in hearing about what people tried, what results they think they saw, and then trying to apply those findings to my own processes.
Which is to say I don't think the pontificating is pointless, but as statements of Real Truth, I agree they're likely wrong. We're too early in the game.
Being a domain expert has been more valuable than being an excellent software developer before the ai.
In 2018 I witnessed one guy with no prior coding experience who built a tool that after a month of coding was making very decent money (more than me), just because he was aware of a particular niche.
He showed me parts of his code and it was as bad as my first program, but his was solving a real life problem.
Overall I agree with this, though I do think that there will be a trend to hoard/keep-secret domain knowledge by professions. Like plumbers will try and make it a trade-secret or protected intellectual property how to change a pipe fitting.
For basic residential plumbing work the moat is not knowledge. There are already books and YouTube videos that will teach you everything you need to know. Professional plumbers can't stop that. The real moat is that most people don't have time, don't want to buy tools, and don't want to get shit on their hands.
For new construction and commercial work the moat is a contractor's license. They don't allow LLMs to take the licensing exam yet.
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How trades gate keep is time. You can’t just become a plumber or electrician on your own. You have to be an apprentice for years no matter your knowledge or skill. This is how it works in trades and unions and where the term “pay your dues” comes from. Like you have to literally pay($$) dues for years before you can move up.
There is a difference between something being a hidden/gate kept trade secret and something being easier for person X to do than person Y through a combination of real world experience and practice.
Taste, architecture, new innovations. These are all streams of tokens which are subject to the same scaling laws as code, language, and basic classification.
We are going to see a new generation of models which effectively “solves” these problems for most businesses. Likely within the next two years- then we’ll talk about some other problems which limit adoption.
I'd like to believe that stable state ends in a pair-programming structure, with a systems thinker/engineer and a domain expert.
Someone needs to spot when a linked list is better than a map. And the other needs to spot when clinical trial coding should happen before claims, audits, or patient outreach.
As software engineers we’ve now suddenly become a sort of “god of the gaps” - our existence is only justified in the (fewer and fewer) situations where the AI can’t do the job just as well on its own
I write software that makes money and AI helps me write software that makes more money.
That's a rare niche; the vast majority of software makes no money whatsoever.
What type of software do you produce?
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Grifters are trying to get their 5 minutes of fame by posting some unhinged shower thought that they have. It's just that you're noticing it now, because it pops up in the feed. Previously they'd say some shit about Agile, OOP/Functional programming or some other bullshit, and it would be swept under the rug.
> nobody has any idea what to do with AI on an individual level?
I appreciate the frustration, but some of us are actually successfully using these tools.
you completely misread that statement
Several things can be true at once.
>How much pontificating needs to be done before people acknowledge nobody has any idea what to do with AI on an individual level?
if nobody has any idea what to do, talking about it is the right approach
So far AI has been a (genuinely) massive improvement for...
Search
It's reading my requests more clearly than (for example) Google's search input ever did, and it's got (some) understanding of how close the result (or fragments of results) are to what I want.
I can ask it about things I know about, and it can answer with strategies I hadn't thought of.
HOWEVER - I still need to understand the results AND AI can overreach - it can say (figuratively) "Oh you are searching for Event handling, therefore I will write a orchestration saga" - which, if I am not across, can get us both in trouble.
Further, we KNOW that AI has no (real) understanding of the responses - it's just token adjacency - and it fails basic logic tests
Current AI is just awesome natural language processing, but it's still got a ways to go to where I would say "It can replace people"
Edit: LLMs demonstrate (almost perfectly) the difference between correlation and causality. LLMs identify correlative patterns, but the job still needs (us) to make the causative judgments.
Thank you! That's exactly what it is. Instead of presenting you 1000 results (or 0) which you have to manually skim through, it generates 1 result as a summary. And even if there is no actual search result, it will hallucinate you 1 result (full of BS).
But because the result sounds right (and in cases with good data it actually is) people tend to trust it. I do not dismiss the potential, but for me the line is crossed when you take the result for granted without verifying and while I'm sure many here think that is implied, I bet you, at large, it is not and will be even less so in the future.
Brave New World!
> It's reading my requests more clearly than (for example) Google's search input ever did
I see this take a lot and it puzzles me.
While I think LLMs provide some advantages over traditional search in some modestly nontrivial contexts, they tend to be inferior to traditional search at its peak. I attribute this attitude to two things: the broad progressive enshittification and productization of search, and the fact that (re)search is a skill that most people tend to be bad at. Without massaging, LLMs spit out the most utterly braindead boneheaded queries, which are fine in cases where the problem is very well understood with minimum uncertainty or critical nuance. If your problem has either, God help you. But perhaps those queries are at least as good as the average human generated query
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Hype folks need to move those goal posts to justify all the money and time invested into something we are starting to realize is a liability.
All of those things matter. One needs to be able to judge the solution in order to make a judgement if it is fine, or not. Why yes, and why no. No matter who you export the typing process to. LLMs are just tools speeding up the typing process.
I think what matters is.... the person being intelligent. I do not mean this as an us vs them thing, but a LOT of companies have some not very smart people in and running them. It's never about exact skills or roles.
My observation on AI is that some frankly less intelligent folks think they don't need smart people any more because AI makes them smarter. I disagree.
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The article says that domain expertise is more valuable now because agents can code well, but not understand the domain well.
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.
> Highly regulated domains like finance have entire books around how they should work.
Ironically, LLMs are much better at understanding those than humans.
Good post! Also, in my opinion, domain expertise is actually more interesting than pure coding ability. Coding, for me, has always been a means to an end. I'm equally happy with a spreadsheet if it solves my problem, and in fact I hate most apps.
I’ve been an engineer for a while now, where we have a mix of EE, ME, SysE, SWE, and the programmatic folks, lots of software/hardware integration at pretty “low” levels for each discipline, really fun.
One of the things I say is when I’m on my soapbox is: we are all engineers. We have different tools in our toolbox to solve problems. We get paid to solve problems, not (for example) write software. Software is just a tool.
There is a separate aspect of having domain expertise in that it open paths for a software engineer to make a lateral switch to a Business Analyst role. That in turn opens paths to management track on the business side as one gains deeper knowledge. If you have business expertise AND direct SDLC experience, that is a different kind of value you bring to the table.
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.
This is such a sane take. It is THE reality we have been always ignoring.
Writing software has never been difficult. It is the domain that has been the issue. Always.
> Writing software has never been difficult.
That's not true at all, sure CRUD might not have been that difficult, but absolutely there is extremely complicated software out there that is really difficult to write in a performant and correct manner.
Yes. Audio, video encoders, decoders etc, 3D modeling, rendering etc.
That too is "domain" even it feels like it is NOT. Domain of signal processing, Euclidian spaces, information theory and what not. Thar too is all "domain" and that "domain" part is difficult to write.
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"sure CRUD might not have been that difficult, but absolutely there is extremely complicated software out there that is really difficult to write in a performant and correct manner."
I was a developer for 20 years, before I pivoted to cybersecurity. My hobby projects were always more complex than the software I wrote at my day job.
The majority of software developers are writing some type of CRUD code or glue code for business processes. A small minority are writing complex code at big tech companies.
AI will most likely replace the need for many software developers.
Writing software for a domain is itself a domain of expertise, with a similar learning curve. It isn't enough to know the programming language.
For example, it takes years to develop the knowledge and idioms required to effectively write high-performance systems code, which is separate from the language the code is written in. You can have decades of experience in a systems language and zero experience writing modern systems code in that language. Same with embedded code, supercomputing code, etc.
Writing software is only "not difficult" if you've already learned how to write it.
The type of software the average hacker news commentator writes is not difficult. That does not mean there aren't industries where the code is legitimately difficult. Games, embedded, etc.
Agree on this one. As a Civil engineer, I can smell which software ( or some part of) was developed by computer engineers without much understating of the "domain". The worst offenders are software needing multi-domain expertise in the first place. Crude Ex. Payroll (Finance) and Leaves (HR) are 2 seperate domain, and they need to account for each other.
I think you're putting the analogy in the wrong place. In this context, civil engineering isn't a "domain" any more than software engineering. Both are exercised in support of the "actual" domains relevant to business or society.
The more analogous question is: as a civil engineer, could you tell which structure was designed with the help of a civil engineer, and which was designed by a domain expert (e.g. a transportation administrator) with automated civil engineering?
Although the idea of the main promise of the article is correct - that teaching domain expert to use AI seems to be faster than teaching senior developer the domain, there is and will be a learning curve to learn AI. It still sounds like this article is trying to sell vibe coding in a fancy wording. However, domain expert working in team with senior developer that utilizes AI will be the ultimate combo.
> If you’re an experienced engineer betting on where to spend the next few years, this is the bet. The mechanical skill you sweated for, turning a clear idea into clean code, has gotten dramatically less valuable. The thing that’s still scarce is a deep, verified model of some real domain. Go get one. Pick an industry, an instrument, a regulatory regime, a physical process, and learn it the way you once learned a programming language or framework. That’s the part the agent can’t do for you, and it’s the part that’s now worth the most.
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.
In my own experience this is 180 degrees from reality. As a generalist, feeling out the depths of a single domain (something I've been forced to do at least 50 times in my career, to the point that I'm probably a global expert in at least 2-3 things I don't actually care about, but are poorly documented and not especially lucrative on their own) is something that's basically a bunch of Google searches, reading source code, and writing/running tests manually, none of which I really care about short of getting to "the right solution."
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.
> The engineer’s advantage, the ability to translate a domain model into working code, is now cheap.
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.
I think this misses something.
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.
While agents are able to take over a large part of code writing, software development skills themselves are still a moat. How to design systems for the future, for team collaboration, for cost efficiency, hosting strategies, security… these are decisions that will still have to be made by a software expert.
Sure, I think the article is basically correct, as things stand right now ... but the problem is that that wipes out software development as a career: software becomes just a tool that domain experts can spin up to make their jobs easier.
Software engineering is a domain on its own, just a technical, not a business one. Good luck with looking for a once in a million data race or deadlock bugs. Good luck with synchronizing system distributed over a whole globe. And good luck with keeping your domain knowledge up-to-date while developing and maintaining your vibecoded system. Assuming you can even provide enough details and deterministic specification to AI agent, because understanding business and having knowledge is not equal to being able to write down specific and concise rules to make a system out of it.
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?
I feel like this aspect has been discussed on HN many times. The thing that resonates with me strongly is that there’s rarely a clear or fixed set of requirements to begin with - at least from my work experiences. Then, domain expertise helps with being able to proactively call out when they are missing and support in defining them. Still I am of the view that with enough context AI could also replace the best engineers with the best domain expertise.
Maybe, but that would require domain experts and stakeholders to write clear specs, and that's never gonna happen in my experience.
I am still bothered that domain experts still keep confusing closing orders with generating a delivery note, or stopping to say articles when they mean a product or a product when they mean an item.
Writing good specs require lots of domain knowledge but a very engineeristic approach these people just don't have.
This is exactly my experience vibe coding as a security architect of 20 years https://open.substack.com/pub/rakkhi/p/vibe-coding-as-securi...
I thought real moat of software is requiring virtually no extensive knowledge/experience of both system and domain. Copying taste and network effect is significantly difficult. The fact is venture backed startups with plenty of talents and resources has been rarely making their positions in the market before vibe coding. That's why people at 20s can compete with experts in various areas. Current backlash is due to creation of guys with just x years experiences in the industry we often see in other mature enterprises.
While the article is interesting, it makes assumptions that are not explicitly stated. And the one strong assumption that makes no sense to me is that they discuss team size = 1. Sure, a domain expert now can use an agent. But he might as well work with a development team, or a developer that uses an agent. That's pretty standard I would say.
> inputs and outputs
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.
> Domain Expertise Has Always Been the Real Moat
Yes, and the Big AI companies are currently hoarding data about all domains out there.
This is why we have product managers!
Maximal Point of View that is harder and harder to disagree with: Sharing your domain expertise is asking to get yourself automated away. Already, if any company hasn't begun trying to maximally collect data on every aspect of their employee's work, they are asking to get wiped by future automated competition from companies that learned everything they could from their employees, then replaced them. Open Science? Open Source Code? Shared your art online? We were all suckers, and might be the first to go.
As someone who has jumped from one industry to another: I'm sorry, but domain expertise isn't much of a moat. Yes, it takes a while to learn a particular industry/business, but it doesn't take that long, and worse still: one advantage that LLMs have over us humans is they have such a broader inventory of human knowledge at their disposal. I've literally used LLMs as product managers for new domains, and while I see the rough edges that LLMs have, they always have significant domain expertise.
I don't think that's the moat.
Domain knowledge is not the moat it might appear to be, especially in industries that heavily document
Using AI to more rapidly learn a domain will help in the short term
But in the long term, all moats will evaporate
> Agentic tools collapsed one of those paths and not the other. The engineer’s advantage, the ability to translate a domain model into working code, is now cheap. The domain expert’s advantage, knowing what right looks like, is not.
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.
I have never yet had a set of words that I have grown to hate so much - “taste” and “moat” being at the top of them. It is almost always coming from people who have or know about neither.
(Agree with the article’s general sentiment - but just wanted to make this tangential comment)
> There’s no skill file that contains the tacit knowledge of a person who has reconciled a thousand payrolls.
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.
One little detail. The models are already pre trained on similar system implementations. Likely whatever you are building has been built in some form or shape in the past and the ambiguity is resolved by someone in the training set.
That “likely” is doing a lot of work, especially in mission critical software.
In my experience “we need so create something for this regulatory change / new industry trend” is more common than we need to redo a working piece of software that’s been done before.
Like so many posts that end up on HN, I just want to say "you've got a decent idea, but tone it the fuck down."
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.
"to build software you need someone who can make requirements and someone who can build systems"
revelations never stop coming do they
More generally, I think it’s more specialized knowledge.
If you have particularly specific knowledge in pretty much any domain, combining that with AI can lead to huge gains.
Domain expertise has always been the path to advancement in most SWE jobs. You’ve always had to understand the domain and have judgment to not be stuck as a “code monkey” in almost every company barring the big tech outliers where you can be on a generic framework or library team.
Brilliant framing. If you understand a domain and understand software architecture then you are in an extremely strong position right now.
So "the real moat" are BIs and PMs equipped with LLMs? Man will I enjoy seeing this tower of babel crashing down on some heads. Unfortunately, software has tendency to survive for a long time even the shittiest conditions. Taking into account average turnover of 2 years, we're at least 3-5 generations before it collapses, so the people who started this madness will most likely be elsewhere and won't see the reckoning.
These kind of posts always miss the point that in a team of 10 not everyone needs to be a domain expert, and that now the same work can be delivered with a team of five or less.
Bad luck for the remaining ones.
"Go get one."
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).
I started an agentic experience app in domaining , people don’t seem to take it that well , they are afraid of letting AI run on its own.
> the binding constraint has moved from can you build it to can you tell whether it’s right.
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.
Knowing the correct answer to one million instances of a+b, and validating this, does not guarantee that an oracle will use the + operation that's correct over a domain billions of times larger, which you can't exhaustively check.
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.
There's a lot of people agreeing and disagreeing with the article, but on what grounds? How do you know "domain expertise" is a "moat"? Vibes? Has there been ongoing, persistent attacks by AI on domain expertise where we can say the moat holds, economically speaking? So far it seems quite the opposite.
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.
You think we won't have ai sales agents that are better than humans at selling?
They are already significantly better than humans at persuasion (according to a study from Princeton).
> Has there been ongoing, persistent attacks by AI on domain expertise where we can say the moat holds, economically speaking? So far it seems quite the opposite.
What do you mean by this? Most human white collar workers still have their jobs. I can't see the future, but yes, so far, human expertise is doing ok.
We'll see what happens in 2027, and 2028, and...
I'm in growth. When I hire in sales roles, I prioritize domain expertise.
I bet there were textile workers who would have written articles like this if the internet had existed back then.
you're right, and i hate that the word you are looking for is "cope."
anthropic is making billions of dollars proving how little domain expertise matters.
the philosophical route towards understanding how little domain expertise matters would take paragraphs to write...
Domain expertise is neccessary but not sufficient.
It takes a LOT of time to validate every path, possibly an infinite amount of time, depending on the complexity of the domain.
there is a big difference between seeing that pairs of input and output are correct, and knowing that the system is correct. there are invinitely many in and output pairs and only checking some while you vibe code your tool is never going to be a reliable method
It's not just domain expertise. Sustainbly great work is done by people with expertise _and_ ownership/accountability.
Sure, producing code has become cheap. Yet again the taste matters and LLMs do not have taste - they will apply patterns that are unnecessary or not extendible, producing unmaintainable systems that nobody understands. Capturing domain knowledge was the crux of development process, but so was verifying, documenting, ensuring that multiple systems work together, maintaining uniformity. I don't know where the assumptions, done by developers, that they only need to produce code that just works or goes brrr fast comes from.
Domain expert can develop working code, but they will not be able to ensure above.
This is not entirely wrong, but oddly describes the major flaw in its own argument: software engineering has tacit knowledge just like every other domain of expertise! And just as you can't become a doctor by just reading textbooks, or an architect by just looking at plans, you can't become a software engineer by just reading a bunch of code.
Successful software results from the intersection of expertise in two domains: the application domain, and software engineering.
Wouldn't the model have as much domain expertise as pretty much any human? I'm assuming the average project manager isn't reading the entire internet
I have bad news about reading the internet...
This is a nice theory, but is it true in practice? (At scale; I’m sure at least 10% of domain experts will find they enjoy writing software too)
To me, the bottleneck feels like it's shifted. It's no longer 'can we build this?' but 'should we build this, and why?' Strategy might be the last thing AI can't do for us at least for now
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Network effect is the moat not domain expertise. Domain expertise build on network effect (for instance the knowledge of the network) yes, but just simple domain expertise is only a training round way.
These guys live in their heads, so when the world changes, they invent reasons why they’re still relevant.
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.
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I have the opposite take. Because Claude is also a domain expert at most things, and you can unit test your way to making things "just work".
If you ask me and a logistics dispatcher the task of building logistics dispatching software (whatever that is), I will get there first.
Highly agree with much of the article. IMO, this is why many engineers who learned to code in the post-2010 'new hot framework every week' era but before LLM coding took hold are able to get much better results from AI assisted coding than those on either end of that sweet spot. The domain expertise in this case is constantly having to adapt to learning the latest flavor of the week DB or JS framework and adapt existing patterns and paradigms to new ones. Agentic coding itself is, in this case, one of those new paradigms.
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.
LLMs are the best domain experts, but the curse is that they know too much.
so it takes a domain expert to remove unnecessary things, similar to how stone carvers create by removing material, not adding
Encyclopedic knowledge does not equal expertise, much like raw intellect does not equal wisdom.
Knowing "too much" and not knowing what belongs to the core and what is a secondary detail is exactly a lack of domain expertise.
Perhaps the good news is that even the best spreadsheet-slinging accountant in the west would still going to need some programming experience to do their verification.
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.
This is why Google is pushing SEOs to get their clients to codify and publish their domain expertise: while it gives them a way to filter signal from noise/slop right now (supposedly helping to "improve search experiences"), it also simultaneously extracts that experience into a consumable form for later training.
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.
There are two different type of domains to be aware of here;
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."
> The mechanical skill you sweated for, turning a clear idea into clean code, has gotten dramatically less valuable.
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.
the idea that llm's can't help if you're missing domain knowledge is crazy.
I mean if you ask the AI to make something to manage the inventory in a warehouse without any detail about how the warehouses operate then you're going to get a worse result than a domain expert talking to the AI.
The problem is that more and more people are getting convinced by the AI's that they're domain experts when they're really not.
> A logistics dispatcher, a clinical coder, an actuary. [...] They know the correct outputs for a given set of inputs because they’ve spent ten years living in those inputs and outputs. Hand them an agent and they are startlingly effective, because the thing they’re missing, the ability to produce code, is exactly the thing the agent supplies. What they bring is the thing the agent can’t: the ground truth.
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.
The standard take, including my own from last year, is that these tools amplify senior developers because senior developers have judgment.
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.
blablabla AI usage has to be regulated. end of story
A domain expert might know if a system produces correct results.
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?
Just say you do not know.
The problem with these kinds of discussions is that they act like experienced software engineers themselves don't bring domain expertise to software products.
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.
What some people here are missing, is that domain experts or hobbyists are mostly vibe coding tools for themselves -- not as a SAAS product. They tend to work good enough to do the thing they want it to do. It runs locally or on some VPS, and is held together by string and duct tape.
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.
In the past, an engineer who deeply understood the internals of a DB and how memory management worked in Java would be indispensable.
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?
> In the past, an engineer who deeply understood the internals of a DB and how memory management worked in Java would be indispensable.
That engineer still is indispensable. Any organization foolish enough to replace such a person with an LLM is going to find itself in deep water when the pile of hallucinations becomes too much to endure.
I disagree because we're buying up companies and training models, creating skills and agentic workflows on individual domain expert's 30 years of notes and prior projects
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.
But that’s not true though. You still have to convince humans. You still have to deal with what people in power feel (clients, leads,etc) This still adds wall time. I saw 0 serious analysis confirming 5x productivity gains. In the coding part? Maybe, and that’s not even certain. But pure coding is only one order of making software, or solving problems with software
and 0 analysts were saying agentic workflows actually worked 6 months ago, they were completely disillusioned and yet here we are
if you’re actually building these things, you know they and the CEOs they’re hearing from are all 6 months behind. the executive’s frantic pivot to shove “AI” down everyone’s throat didn’t pan out in one quarter and had nothing to do with the actual concept at all
that, and every industry is different. I wouldn’t listen to analysts, I’m in an industry that even Anthropic thinks wont be touched by AI (even though they can read ours and everyone else’s sessions)
all public discourse is just flat wrong, and just like every week this year, you’re just going to wake up seeing a new AI capability headline that makes you question your role in society. So play devil’s advocate all you want, the silver lining is that there’s more work to do than ever before and more of it can be tackled at once
This is just generated slop from Claude.
“The hard part of writing software has never been the writing.”
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.
Have you considered becoming a residential electrician? Good job, pays well, lots of problem solving, and it won’t be replaced with an AI. I’m serious!
If a real AI job apocalypse hits there will be no escaping it for anyone.
Even people whose job can't be done by AI will be impacted because there will be far less demand for their services (everyone whose job IS directly AI replaceable will be a brokie) and there will also be far more supply of people moving into their field to escape all the jobs AI does directly replace.
"Join the trades" is the new "learn to code" in terms of seeming like good advice but having a very short shelf life.
A lot of the trades involve physical work, can be seasonal, and ride the construction cycle up and down. The employers tend to be small, and many are family owned, so they are "off the radar" of OSHA and EEOC. You may be at the mercy of bias and nepotism.
The trades are great, but not a panacea. Maybe emigrate to a country with better conditions for the working class.
I’d go with physical therapy! Or something else that’s closer to humans and health. “Problem solving” becomes that much more tangible and directly meaningful to another person
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I have. Or general well rounded construction worker who knows how to build all aspects of a house. A full stack builder.
Have you?
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Good comment but I think the timelines are not clear. Humans are an algorithm. This was true before AI. In certain domains (playing chess e.g.) machines have already surpassed humans. Humans still play chess though and chess is more popular than ever.
I still drive my car and self driving cars have yet to displace human drivers. I think the sentiment on HN and other places when Google started talking self driving cars circa 2009 is that it's harder than it looks. Typically the first 80% of progress is easy and the rest isn't as easy. We're almost 20 years after a "pretty good self driving car" and we're still not at "self driving cars outperform humans under all situations".
Today humans use AI. You can't fire up Claude and ask it "what do you want to work on today". The amount of context we have as humans is vastly larger than the context LLMs have. If you give LLMs vague context they're completely lost. They are mind blowing in many ways but they are not anywhere close to AGI. They're also not close to being able to build complex software only guided by someone who has no idea what software is and how computers work. They can do some of that but I've yet to see any major successful piece of software built that way. They also consume vast physical resources to get the job done.
Before LLMs I think it was a given that at some point we would have AGI that's smarter than us. Machines we build aren't constrained by the biological constraints we are subject to and can evolve faster than we have. But when that's gonna happen, whether that's actually LLM-like in architecture, and what things will look like once that happens, are fairly open questions at this point. In the mean time LLMs can certainly generate a lot of code and we can use them to build more stuff.
>Good comment but I think the timelines are not clear. Humans are an algorithm. This was true before AI. In certain domains (playing chess e.g.) machines have already surpassed humans. Humans still play chess though and chess is more popular than ever.
It was always true even before AI, AI just makes it more evident since Transformers are LITERALLY an algorithm that produces content nearly identical to content humans produce.
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There's certainly a whole lot of "it was never the coding that was our value" articles about right now. I agree that they represent a degree of self delusion to an extent. But it's also a useful examination of where your value might lie in this new AI age. I think there will be a role for humans in it - where exactly it lies is obviously up in the air.
This article is wrong. LLM's encode all the domain knowledge you could possibly want. As a software dev I can query an LLM, become a domain expert in a short amount of time, and then code up a solution. If people think their niche is safe from automation, think again. Even the people who think theyre the masterminds at the top.
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.
I won't over-generalize here, because maybe your statement is true in some cases, but I will provide a counterpoint: this is not true (in my experience) in real estate title insurance and escrow services.
I've consulted for and led large teams for real estate title insurance and escrow companies for many years, and the domain expertise is so incredibly deep, nuanced, and multivariate (especially depending on jurisdiction) that building valuable and viable products in the space is incredibly difficult - before LLMs, and even now, with LLMs.
Without getting too deep into it, I'm pretty bullish on AI (and have been very close to it and deep in it for a long time, while also very apprehensive about the effects it'll have on society), and I can tell you, from extensive attempts from myself and many on my teams to leverage the latest frontier LLMs to bring deep domain experience to bear to help drive valuable products: we have not yet seen success. It's not helping engineering folks, it's not helping product folks. It's creating a ton of questionable output and hasn't resulted in real ROI, and it's not capable of accurately answering deep domain questions without hallucinations or assuming what works in one jurisdiction works in all.
I've seen success in many other areas, but not this domain - and, importantly, the regulatory environment in which title insurance operates is incredibly complex and strict, meaning you can't just YOLO LLM output into production (as much as we'd love to try so we can learn at a faster clip).
And the kicker: we've found the way for us to build the best products is still going out into the field, sitting with escrow and title folks, watching them work, asking them questions, and designing for the real world, the regulatory nuances, the local client nuances, etc. You can't get that from an LLM.
Agree with you there. What you are working on and the commenter below talking about surgery, they are all valid counter examples where the degree of expertise is quite extreme. But most people are not living on the edge of domain expertise. Im guessing 80% of the domain knowledge out there is up for grabs. For example: I dont have to go get a job at a security software company to figure out how security camera systems work and the principles involved, I can ask probing questions of an LLM and get most of it. The domain knowledge is embedded in the model.
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You might /think/ you've become a domain expert, but you haven't.
This guy has clearly never asked an LLM whether New York City is entirely south of the state of Oregon.
Once someone taught me "you can do xyz reading a book, but you cant do surgery by reading a book". Now replace the book with LLM. This is what "domain expertise" look like for some domain.
> become a domain expert in a short amount of time
how does that work exactly?
I don't believe this.
I work in e-commerce and warehouse management.
We have put lots of effort at documenting the domain, creating precise unambiguous language, glossaries, E2Es written as user stories etc, etc.
And still models are simply not able to translate Jira tasks to clear specs, even for this well understood and common use case.
Also, they don't understand how changes in one part of the business domain will impact other parts. They can get it right 9 times out of 10, but even that is too little and compounds to deeply wrong implementations.
And they don't understand or know the people involved in these processes and what they REALLY care for or what the real priorities are. Very often political.
And that's not even mentioning the code, that ends up with the lack of proper abstractions or harness.
Or the lack of push back against bad ideas at business or code level.
The problem is, discoveries that advance humanity are made by 0.01% of humans or less. The majority of people don't ever discover anything new, they just build or consume what already exists.
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.
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interesting