Some thoughts on LLMs and software development

https://martinfowler.com/articles/202508-ai-thoughts.html

by floverfelt • 11 months ago

420 405 11 months ago

All major technological advances have come with economic bubbles, from canals and railroads to the internet.

Is this actually correct? I don't see any evidence for a "airflight bubble" or a "car bubble" or a "loom bubble" at the technologies' invention. Also the "canal bubble" wasn't about the technology, it was about the speculation on a series of big canals but we had been making canals for a long time. More importantly, even if it was correct, there are plenty of bubbles (if not significantly more) around things that didn't have value or tech that didn't matter.

mmmm2• 11 months ago

The Intelligent Investor by Graham talks about investors putting so much money into airlines and air freight that it became impossible to make a return. I don't know if you would call that a bubble, maybe just over exuberance.

sfink3• 11 months ago

Apologies for arguing from first principles, but for anything that spurs a lot of activity, the only alternative to a bubble is this: people ramp up investment and activity and enthusiasm only as much as the underlying thing can handle, then gradually taper off the increase and gently level off at the equilibrium "carrying capacity" of the new technology.

Does that sound like any human, ever, to you?

(The only time there isn't a bubble is when the thing just isn't that interesting to people and so there's never a big wave of uptake in the first place.)

krainboltgreene2• 11 months ago

If you were right then we'd have a lot more than a dozen listed bubbles on wikipedia.

That's an absurd framing for a cute quip.

Marazan• 11 months ago

The AI bubble also isn't about the technology.

tptacek13• 11 months ago

I don't know enough about the early history of the airline industry but there was very definitely a long series of huge bubbles in the railroad industry.

daveguy3• 11 months ago

I would be very interested in reading about huge bubbles in railroad, airline, or any other industry. Do you happen to have references (genuinely asking; the original article didn't include any references)?

Edits--

Found one: https://en.wikipedia.org/wiki/Panic_of_1893

Another good one:https://en.wikipedia.org/wiki/Public_Utility_Holding_Company... (from cake_robot here: https://news.ycombinator.com/item?id=45056621)

For reference, apple and spotify links to the Derek Thompson podcast in reply below (thank you!):

https://podcasts.apple.com/us/podcast/plain-english-with-der...

https://open.spotify.com/show/3fQkNGzE1mBF1VrxVTY0oo

gjsman-10002• 11 months ago

For my money, I used this analogy at work:

Before AI, we were trying to save money, but through a different technique: Prompting (overseas) humans.

After over a decade of trying that, we learned that had... flaws. So round 2: Prompting (smart) robots.

The job losses? This is just Offshoring 2.0; complete with everyone getting to re-learn the lessons of Offshoring 1.0.

the_af2• 11 months ago

Prompting (overseas) humans [...] After over a decade of trying that, we learned that had... flaws.

I think this is a US-centric point of view, and seems (though I hope it's not!) slightly condescending to those of us not in the US.

Software engineering is more than what happens to US-based businesses and their leadership commanding hundreds or thousands of overseas humans. Offshoring in software is certainly a US concern (and to a lesser extent, other nations suffer it), but is NOT a universal problem of software engineering. Software engineering happens in multiple countries, and while the big money is in the US, that's not all there is to it.

Software engineering exists "natively" in countries other than the US, so any problems with it should probably (also) be framed without exclusive reference to the US.

gjsman-10002• 11 months ago

The problem isn't that there aren't high quality offshore developers - far from it. Or even high quality AI models.

The problems are inherent with outsourcing to a 3rd party and having little oversight. Oversight is, in both cases, way harder than it appears.

CuriouslyC4• 11 months ago

I'm sure the blacksmiths and weavers will find solace in that take. Their time will return!

keeda3• 11 months ago

Funnily enough, I left a similar comment just the other day: https://news.ycombinator.com/item?id=44944717

The conclusion I reached was different, though. We learnt how to do outsourcing "properly" pretty quickly after some initial high-profile failures, which is why it has only continued to grow into such a huge industry. This also involved local talent refocusing on higher-value tasks, which is why job losses were limited. Those same lessons and outcomes of outsourcing are very relevant to "bot-sourcing'.

However, I do feel concerned that AI is gaining skill-levels much faster than the rate at which people can upskill themselves.

sebnukem2• 11 months ago

hallucinations aren’t a bug of LLMs, they are a feature. Indeed they are the feature. All an LLM does is produce hallucinations, it’s just that we find some of them useful.

Nice.

tptacek13• 11 months ago

In that framing, you can look at an agent as simply a filter on those hallucinations.

th0ma52• 11 months ago

Yes yes, with yet to be discovered holes

Lionga2• 11 months ago

Isn't an "agent" not just hallucinations layered on top of other random hallucinations to create new hallucinations?

armchairhacker• 11 months ago

This vaguely relates to a theory about human thought: that our subconscious constantly comes up with random ideas, then filters the unreasonable ones, but in people with delusions (e.g. schizophrenia) the filter is broken.

Salience (https://en.wikipedia.org/wiki/Salience_(neuroscience)), "the property by which some thing stands out", is something LLMs have trouble with. Probably because they're trained on human text, which ranges from accurate descriptions of reality to nonsense.

keeda3• 11 months ago

More of a error-correcting feedback loop rather than a filter, really. Which is very much what we do as humans, apparently. One recent theory of neuroscience that is becoming influential is Predictive Processing --https://en.wikipedia.org/wiki/Predictive_coding -- this postulates that we also constantly generate a "mental model" of our environment (a literal "prediction") and use sensory inputs to correct and update it.

So the only real difference between "perception" and a "hallucination" is whether it is supported by physical reality.

ninetyninenine4• 11 months ago

Nah I don't agree with this characterization. The problem is, the majority of those hallucinations are true. What was said would make more sense if the majority of the responses were, in fact, false, but this is not the case.

xmprt2• 11 months ago

I think you're both correct but have different definitions of hallucinations. You're judging it as a hallucination based on the veracity of the output. Whereas Fowler is judging it based on the method by which the output is achieved. By that judgement, everything is a hallucination because the user cannot differentiate between when the LLM is telling the truth and isn't.

This is different from human hallucinations where it makes something up because of something wrong with the mind rather than some underlying issue with the brain's architecture.

nine_k• 11 months ago

I'd rather say that LLMs live in a world that consists entirely of stories, nothing but words and their combinations. Thy have no other reality. So they are good at generating more stories that would sit well with the stories they already know. But the stories are often imprecise, and sometimes contradictory, so they have to guess. Also, LLMs don't know how to count, but they know that two usually follows one, and three is usually said to be larger than two, so they can speak in a way that mostly does not contradict this knowledge. They can use tools to count, like a human who knows digits would use a calculator.

But much more than an arithmetic engine, the current crop of AI needs an epistemic engine, something that would help follow logic and avoid contradictions, to determine what is a well-established fact, and what is a shaky conjecture. Then we might start trusting the AI.

gnerd00• 11 months ago

this was true, but then it wasn't... the research world several years ago, had a moment when the machinery could reliably solve multi-step problems.. there had to be intermediary results; and machinery could solve problems in a domain where they were not trained specifically.. this caused a lot of excitement, and several hundred billion dollars in various investments.. Since no one actually knows how all of it works, not even the builders, here we are.

chadcmulligan• 11 months ago

One night, I asked it to write me some stories, it did seem happy doing that. I just kept saying do what you want when it asked me for a choice, its a fun little way to spend a couple of hours.

aitchnyu2• 11 months ago

I've asked LLMs to modify 5 files at a time and mark a checklist. Also image generators no longer draw 6 fingered hands.

anthem20256• 11 months ago

Isn’t that why people argue against calling them hallucinations?

It implies that some parts of the output aren’t hallucinations, when the reality is that none of it has any thought behind it.

awesome_dude2• 11 months ago

I have a very similar (probably unoriginal) thought about some human mental illnesses.

So, we VALUE creativity, we claim that it helps us solve problems, improves our understanding of the universe, etc.

BUT people with some mental illnesses, their brain is so creative that they lose the understanding of where reality is and where their imagination/creativity takes over.

eg. Hearing voices? That's the brain conjuring up a voice - auditory and visual hallucinations are the easy example.

But it goes further, depression is where people's brains create scenarios where there is no hope, and there's no escape. Anxiety too, the brain is conjuring up fears of what's to come

malloryerik• 11 months ago

You may like to check out Iain McGilchrist's take on schizophrenia, which essentially he says is a relative excess of rationality ("if then else" thinking) and a deficit of reasonableness (as in sensible context inhabiting).

keeda3• 11 months ago

I've prefered to riff off of the other quote:

"All (large language) model outputs are hallucinations, but some are useful."

Some astonishingly large proportion of them, actually. Hence the AI boom.

jama21114• 11 months ago

I find it a bit of a reductive way of looking at it personally

rancar2• 11 months ago

My favorite quote to borrow: “Furthermore I think anyone who says they know what this future will be is talking from an inappropriate orifice.”

koolba• 11 months ago

Reminds me of the classic Yogi Berra, “It's tough to make predictions, especially about the future”.

th0ma52• 11 months ago

To me, it is more specific to say that many futurists don't take into account the social and economic network effects of changes taking place. Many just act as if the future will continue on completely unchallenged in this current state. But if you look at someone like Kurzweil, you can see the very narrow and specific focus of a prediction, which has proved to be more informative to me as a high bar of futurism.

Towaway69• 11 months ago

Predicting the future isn't about being correct tomorrow, rather it’s about selling something to someone today.

An insight I picked up along the way…

atleastoptimal• 11 months ago

There are only 3 things that I have strong empirical evidence for with respect to LLMs

1. Routinely some task or domain of work that some expert claims that LLM’s will able to do, LLM’s start being able to reliably perform that task within 6 months to a year, if they haven’t already

2. Whenever AI gets better, people move the goalposts regarding what “intelligence” counts as

3. Still, LLM’s reveal that there is an element to intelligence that is not orthogonal to the ability to do well on tests or benchmarks

lubujackson• 11 months ago

I like the idea of AI usage comes down to a measurement of "tolerances". With enough specificity, LLMs will 100% return what you want. The goal is to find the happy tolerance between "acceptable" and "I did it myself" via prompts.

manmal2• 11 months ago

With enough specificity, LLMs will 100% return what you want.

By now I’m sure it won’t. Even if you provide the expected code verbatim, LLMs might go on a side quest to “improve” something.

bko• 11 months ago

Certainly if we ever ask a hallucination engine for a numeric answer, we should ask it at least three times, so we get some sense of the variation.

This works on people as well!

Cops do this when interrogating. You tell the same story three times, sometimes backwards. It's hard to keep track of everything if you're lying or you don't recall clearly so you can get a sense of confidence. Also works on interviews, ask them to explain a subject in three different ways to see if they truly understand.

chistev• 11 months ago

Who remembers that scene on Better Call Saul between Lalo, Saul, and Kim?

Terr_• 11 months ago

This works on people as well!

Only within certain conditions or thresholds that we're still figuring out. There are many cases where the more someone recalls and communicates their memory, the more details get corrupted.

Cops do this when interrogating.

Sometimes that's not to "get sense of the variation" but to deliberately encourage a contradiction to pounce upon it. Ask me my own birthday enough times in enough ways and formats, and eventually I'll say something incorrect.

Care must also be taken to ensure that the questioner doesn't change the details, such as by encouraging (or sometimes forcing) the witness/suspect to imagine things which didn't happen.

inerte• 11 months ago

Triple modular redundancy. I remember reading that's how Nasa space shuttles calculate things because a processor / memory might have been affected by space radiation https://llis.nasa.gov/lesson/18803

anon7725• 11 months ago

Triple redundancy works because you know that under nominal conditions each computer would produce the correct result independently. If 2 out of 3 computers agree, you have high confidence that they are correct and the 3rd one isn’t.

With LLMs you have no such guarantee or expectation.

jama21114• 11 months ago

It works to confuse people and make them sound like they’re lying when they’re not, too. Gotta be careful with this.

hnbad2• 11 months ago

If you're trying to hit quotas rather than find out the truth, that sounds like a feature.

catigula• 11 months ago

I’m often asked, “what is the future of programming?” Should people consider entering software development now? Will LLMs eliminate the need for junior engineers? Should senior engineers get out of the profession before it’s too late? My answer to all these questions is “I haven’t the foggiest”

I just want to point out that this answer implicitly means that, at the very least, the profession is at least questionably uncertain which isn't a good sign for people with a long future orientation such as students.

tomku• 11 months ago

It has never been anywhere close to certain, we just had 20 years of wild, unsustainable growth that encouraged people to cover their eyes and pretend the ride would go on forever. 20 years of telling everyone under the age of 30 that of course they should learn to code and that CS was the new medical or legal degree. 20 years of smugly acting like we are the inevitable future when we are, in fact, subject to the same ups and downs as every other career.

mvieira38• 11 months ago

(slightly off-topic)

Students shouldn't ever have become so laser-focused on a single career path anyway, and even worse than that is how colleges have become glorified trade schools in our minds. Students should focus on studying for their classes, getting their electives and extracurriculars in, getting into clubs... Then depending on which circles they end up in, they shape their career that way. The thought that getting a Computer Science major would guarantee students a spot in the tech industry was always ridiculous, because the industry was just never structured that way, it's always been hacker groups, study groups, open source, etc. bringing out the best minds

muldvarp• 11 months ago

The answers are even kind of contradictory. If the answer to "will we ever need software engineers again in the future?" is "we don't know" then the answer to "should I spend time and money to enter software engineering?" should be "no".

crawshaw• 11 months ago

A bubble is asset prices systematically diverging from reasonable expectations of future cash flows. Bubbles are driven by financial speculation.

The claim in the blog post that all technology leads to speculative asset bubbles I find hard to believe. Where was the electricity bubble? The steel bubble? The pre-war aviation bubble? (The aviation bubble appeared decades later due to changes in government regulation.)

Is this an AI bubble? I genuinely don't know! There is a lot of real uncertainty about future cash flows. Uncertainty is not the foundation of a bubble.

I knew dot-com was a bubble because you could find evidence, even before it popped. (A famous case: a company held equity in a bubble asset, and that company had a market cap below the equity it held, because the bubble did not extend to second-order investments.)

cake_robot• 11 months ago

Just taking your first example, yes I believe you could characterize a lot of the investments in electrification as speculative bubbles even though there was underlying value that was borne out.

https://en.wikipedia.org/wiki/Public_Utility_Holding_Company...

insane_dreamer2• 11 months ago

Besides electricity (answered above), there were bubbles related to steel production in its early days intertwined with the railroad bubble that drove huge investments in steel production for railroad development; when the railroad companies crashed it brought down steel as well; ultimately both survived in a more subdued form (as with the dot coms).

avation: likewise there was the "Lindberg Boom" https://en.wikipedia.org/wiki/Lindbergh_Boom which led to overspeculation and the crash of many early aviation companies

utyop227• 11 months ago

I want to re-phrase your definition.

To me a bubble reflects a market disconnect from fundamentals - wherein prices go up steeply, with no help from the fundamentals (expected growth in base year cash flows and risk).

Indeed there is a subtle difference between a bubble and speculation of what could come of a technology. But the two are connected because the effects of technology are reflected by investors in asset prices.

ares6234• 11 months ago

Other forms of engineering have to take into account the variability of the world.
Maybe LLMs mark the point where we join our engineering peers in a world on non-determinism.

Those other forms of engineering have no choice due to the nature of what they are engineering.

Software engineers already have a way to introduce determinism into the systems they build! We’re going backwards!

delusional• 11 months ago

I would rather say it like this: Very good, very hardworking engineers spent years of their lives building the machine that raised us from the non-determinism of messy physical reality. The technology that brought us perfect, replicable, and reliable math from sending electrons through rocks has been deeply underappreciated in the "software revolution".

The engineers at TSMC, Intel, Global Foundries, Samsung, and others have done us an amazing service, and we are throwing all that hard work away.

sodapopcan3• 11 months ago

As pertaining to software development, I agree. I've been hearing accounting (online and from coworkers) of using LLMs to do deterministic stuff. And yet, instead of at least prompting once to "write a script to do X," they just keep prompting "do X" over and over again. Seems incredibly wasteful. It feels like there is this thought of "We are not making progress if we aren't getting the LLM to do everything. Having it write a script we can review and tweak is anti-progress." No one has said that outright, but it's a gut feeling (and it wouldn't surprise me if people have said this out loud).

tptacek13• 11 months ago

This is the 2025 equivalent of the people who once wrote 2000 word blog posts about how bad it was to use "cat" instead of just shell redirection.

bongodongobob2• 11 months ago

I am one of these people. For my scripting needs, it would probably take me longer to find the script I saved rather than just asking it again and getting an answer in 15 seconds. I haven't saved a smallish script in a year.

didericis2• 11 months ago

Part of what got me into software was this: no matter how complex or impressive the operation, with enough time and determination, you could trace each step and learn how a tap on a joystick lead to the specific pixels on a screen changing.

There’s a beautiful invitation to learn and contribute baked into a world where each command is fully deterministic and spec-ed out.

Yes, there have always been poorly documented black boxes, but I thought the goal was to minimize those.

People don’t understand how much is going to be lost if that goal is abandoned.

pton_xd• 11 months ago

Agreed. The beauty of programming is that you're creating a "mathematical artifact." You can always drill down and figure out exactly what is going on and what is going to happen with a given set of inputs. Now with things like concurrency that's not exactly true, but, I think the sentiment still holds.

The more practical question is though, does that matter? Maybe not.

girvo2• 11 months ago

People don’t understand how much is going to be lost if that goal is abandoned.

Ah but you see, imagine the shareholder value we can generate for a quarter or two in the meanwhile!

Editors note: please read that as absolutely dripping with disdain and sarcasm.

whartung• 11 months ago

"Computers are deterministic!"

If I wanted to plumb together badly documented black boxes, I'd have become an EE.

tptacek13• 11 months ago

'potatolicious says we're going forwards: https://news.ycombinator.com/item?id=44978319

ants_everywhere6• 11 months ago

adding to this, software deals with non-determinism all the time.

For example, web requests are non-deterministic. They depend, among other things, on the state of the network. They also depend on the load of the machine serving the request.

One way to think about this is: how easy is it for you to produce byte-for-byte deterministic builds of the software you're working on? If it's not trivial there's more non-determinism than is obvious.

makeitdouble2• 11 months ago

That was an interesting take, but "probabilistic" is to me different from "random". In particular other field get error tolerances, LLMs give us nothing like that.

We're introducing chaos monkeys, not just variability.

AaronAPU2• 11 months ago

It was forward when Newton discovered the beautiful simple determinism of physics.

Was it going backwards when the probabilistic nature of quantum mechanics emerged?

Viliam1234• 11 months ago

Two words: many-world interpretation.

More seriously, this is not a fair comparison. Adding LLM output to your source code is not analogical to quantum physics; it is analogical to letting your 5 years old child transcribe the experimentally measured values without checking and accepting that many of them will be transcribed wrong.

BlueTemplar3• 11 months ago

Hopefully even Newton already had some awareness of "deterministic chaos" (or whatever terms they would have used back then) ?

And on the side hand, no transistors without quantum mechanics.

positron26• 11 months ago

It's uncertainty, not non-determinism. I don't design a rocket with no idea about the chain of events. I have inscrutable uncertainty everywhere, from manufacture to flight.

dionian• 11 months ago

I’ve often heard, with decent reason, an LLM compared to a junior colleague. But I find LLMs are quite happy to say “all tests green”, yet when I run them, there are failures. If that was a junior engineer’s behavior, how long would it be before H.R. was involved?

A junior engineer can't write code anywhere nearly as fast. It's apples vs oranges. I can have the LLm rewrite the code 10 times until its correct and its much cheaper than hiring an obsequious jr engineer

xmprt2• 11 months ago

Junior engineers get better and learn from their mistakes. An LLM will happily make then same mistake 10 times if you try asking it to do something similar in 3 months. In the long term, I don't think LLMs actually save you time considering the amount of extra time spent reviewing/verifying its code and fixing tech debt.

swagasaurus-rex• 11 months ago

If an AI can’t write the code after two attempts, I’ve never had success trying ten times

nomilk2• 11 months ago

We should ask the LLM the question more than once

For any macOS users, I highly recommend an Alfred workflow so you just press command + space then type 'llm <prompt>' and it opens tabs with the prompt in perplexity, (locally running) deepseek, chatgpt, claude and grok, or whatever other LLMs you want to add.

This approach satisfies Fowler's recommendation of cross referencing LLM responses, but is also very efficient and over time gives you a sense of which LLMs perform better for certain tasks.

_mu2• 11 months ago

What would such a workflow look like? I have Alfred but mainly just use the clipboard feature. I've tried to get into automation but struggled for inspiration. This one seems good!

Are you just opening a browser tab?

nomilk2• 11 months ago

Go to the 'Workflows' tab, make a new one with keyword of your choice (e.g. llm), and map it to open these urls in your default browser:

http://localhost:3005/?q={query}

https://www.perplexity.ai/?q={query}

https://x.com/i/grok?text={query}

https://chatgpt.com/?q={query}&model=gpt-5

https://claude.ai/new?q={query}

Modify to your taste. Example: https://github.com/stevecondylios/alfred-workflows/tree/main (you should be able to download the .alfredworkflow file and double click on it to import it straight into alfred, but creating your own shouldn't take long, maybe 5-10 mins if it's your first workflow)

skhameneh6• 11 months ago

There are many I've worked with that idolize Martin Fowler and have treated his words as gospel. That is not me and I've found it to be a nuisance, sometimes leading me to be overly critical of the actual content. As for now, I'm not working with such people and can appreciate the article shared without clouded bias.

I like this article, I generally agree with it. I think the take is good. However, after spending ridiculous amounts of time with LLMs (prompt engineering, writing tokenizers/samplers, context engineering, and... Yes... Vibe coding) for some periods 10 hour days into weekends, I have come to believe that many are a bit off the mark. This article is refreshing, but I disagree that people talking about the future are talking "from another orifice".

I won't dare say I know what the future looks like, but the present very much appears to be an overall upskilling and rework of collaboration. Just like every attempt before, some things are right and some are simply misguided. e.g. Agile for the sake of agile isn't any more efficient than any other process.

We are headed in a direction where written code is no longer a time sink. Juniors can onboard faster and more independently with LLMs, while seniors can shift their focus to a higher level in application stacks. LLMs have the ability to lighten cognitive loads and increase productivity, but just like any other productivity enhancing tool doing more isn't necessarily always better. LLMs make it very easy to create and if all you do is create [code], you'll create your own personal mess.

When I was using LLMs effectively, I found myself focusing more on higher level goals with code being less of a time sink. In the process I found myself spending more time laying out documentation and context than I did on the actual code itself. I spent some days purely on documentation and health systems to keep all content in check.

I know my comment is a bit sparse on specifics, I'm happy to engage and share details for those with questions.

manmal2• 11 months ago

written code is no longer a time sink

It still is, and should be. It’s highly unlikely that you provided all the required info to the agent at first try. The only way to fix that is to read and understand the code thoroughly and suspiciously, and reshaping it until we’re sure it reflects the requirements as we understand them.

skhameneh6• 11 months ago

Vibe coding is not telling an agent what to do and checking back. It's an active engagement and best results are achieved when everything is planned and laid out in advance — which can also be done via vibe coding.

No, written code is no longer a time sink. Vibe coding is >90% building without writing any code.

The written code and actions are literally presented in diffs as they are applied, if one so chooses.

sfink3• 11 months ago

We are headed in a direction where written code is no longer a time sink.

Written code has never been a time sink. The actual time that software developers have spent actually writing code has always been a very low percentage of total time.

Figuring out what code to write is a bigger deal. LLMs can help with part of this. Figuring out what's wrong with written code, and figuring out how to change and fix the code, is also a big deal. LLMs can help with a smaller part of this.

Juniors can onboard faster and more independently with LLMs,

Color me very, very skeptical of this. Juniors previously spent a lot more of their time writing code, and they don't have to do that anymore. On the other hand, that's how they became not-juniors; the feedback loop from writing code and seeing what happened as a result is the point. Skipping part of that breaks the loop. "What the computer wrote didn't work" or "what the computer wrote is too slow" or even to some extent "what the computer wrote was the wrong thing" is so much harder to learn from.

Juniors are screwed.

LLMs have the ability to lighten cognitive loads and increase productivity,

I'm fascinated to find out where this is true and where it's false. I think it'll be very unevenly distributed. I've seen a lot of silver bullets fired and disintegrate mid-flight, and I'm very doubtful of the latest one in the form of LLMs. I'm guessing LLMs will ratchet forward part of the software world, will remove support for other parts that will fall back, and it'll take us way too long to recognize which part is which and how to build a new system atop the shifted foundation.

skhameneh6• 11 months ago

Figuring out what code to write is a bigger deal. LLMs can help with part of this. Figuring out what's wrong with written code, and figuring out how to change and fix the code, is also a big deal. LLMs can help with a smaller part of this.

I found exactly this is what LLMs are great at assisting with.

But, it also requires context to have guiding points for documentation. The starting context has to contain just enough overview with points to expand context as needed. Many projects lack such documentation refinement, which causes major gaps in LLM tooling (thus reducing efficacy and increasing unwanted hallucinations).

Juniors are screwed.

Mixed, it's like saying "if you start with Python, you're going to miss lower level fundamentals" which is true in some regards. Juniors don't inherently have to know the inner workings, they get to skip a lot of the steps. It won't inherently make them worse off, but it does change the learning process a lot. I'd refute this by saying I somewhat naively wrote a tokenizer, because the >3MB ONNX tokenizer for Gemma written in JS seemed absurd. I went in not knowing what I didn't know and was able to learn what I didn't know through the process of building with an LLM. In other words, I learned hands on, at a faster pace, with less struggle. This is pretty valuable and will create more paths for juniors to learn.

Sure, we may see many lacking fundamentals, but I suppose that isn't so different from the criticism I heard when I wrote most of my first web software in PHP. I do believe we'll see a lot more Python and linguistic influenced development in the future.

I'm guessing LLMs will ratchet forward part of the software world, will remove support for other parts that will fall back, and it'll take us way too long to recognize which part is which and how to build a new system atop the shifted foundation.

I entirely agree, in fact I think we're seeing it already. There is so much that's hyped and built around rough ideas that's glaringly inefficient. But FWIW inefficiency has less of an impact than adoption and interest. I could complain all day about the horrible design issues of languages and software that I actually like and use. I'd wager this will be no different. Thankfully, such progress in practice creates more opportunities for improvement and involvement.

osullivj2• 11 months ago

Written code is an auditable entity in regulated businesses, like banks. Fowler suggests we get more comfortable with uncertainty because other eng domains have developed good risk mgmt. Cart before horse. Other engineering domains strive to mitigate the irreducible uncertainty of the physical world. AI adds uncertainty to any system. And there are many applications where increased uncertainty will lead to an increase in human suffering.

iLoveOncall• 11 months ago

One of the big problems with these surveys is that they aren’t taking into account how people are using the LLMs. From what I can tell the vast majority of LLM usage is fancy auto-complete, often using co-pilot.

This is a completely wrong assumption and negates a bunch of the points of the article...

epolanski6• 11 months ago

How is it wrong?

Do you have any hard number and data to state that tab/auto complete are less popular than agentic coding?

daveguy3• 11 months ago

I would expect it to be almost tautologically true. It's the easiest to implement, and the most deployed. It has to be the most used, even if a lot of people don't know they're using it.

It was the original success of LLMs -- code autocomplete. All of the agentic coding have autocomplete under the hood. Autocomplete is almost literally what LLMs do + post-processing.

daviding2• 11 months ago

I get a lot of productivity out of LLMs so far, which for me is a simple good sign. I can get a lot done in a shorter time and it's not just using them as autocomplete. There is this nagging doubt that there's some debt to pay one day when it has too loose a leash, but LLMs aren't alone in that problem.

One thing I've done with some success is use a Test Driven Development methodology with Claude Sonnet (or recently GPT-5). Moving forward the feature in discrete steps with initial tests and within the red/green loop. I don't see a lot written or discussed about that approach so far, but then reading Martin's article made me realize that the people most proficient with TDD are not really in the Venn Diagram intersection of those wanting to throw themselves wholeheartedly into using LLMs to agent code. The 'super clippy' autocomplete is not the interesting way to use them, it's with multiple agents and prompt techniques at different abstraction levels - that's where you can really cook with gas. Many TDD experts have great pride in the art of code, communicating like a human and holding the abstractions in their head, so we might not get good guidance from the same set of people who helped us before. I think there's a nice green field of 'how to write software' lessons with these tools coming up, with many caution stories and lessons being learnt right now.

edit: heh, just saw this now, there you go - https://news.ycombinator.com/item?id=45055439

tra3• 11 months ago

It feels like Tdd/llm connection is implied — “and also generate tests”. Thought it’s not cannonical tdd of course. I wonder if it’ll turn the tide towards tech that’s easier to test automatically, like maybe ssr instead of react.

daviding2• 11 months ago

Yep, it's great for generating tests and so much of that is boilerplate that it feels great value. As a super lazy developer it's great as the burden of all that mechanical 'stuff' being spat out is nice. Test code being like baggage feels lighter when it's just churned out as part of the process, as in no guilt just to delete it all when what you want to do changes. That in itself is nice. Plus of course MCP things (Playwright etc) for integration things is great.

But like you said, it was meant more TDD as 'test first' - so a sort of 'prompt-as-spec' that then produces the test/spec code first, and then go iterate on that. The code design itself is different as influenced by how it is prompted to be testable. So rather than go 'prompt -> code' it's more an in-between stage of prompting the test initially and then evolve, making sure the agent is part of the game of only writing testable code and automating the 'gate' of passes before expanding something. 'prompt -> spec -> code' repeat loop until shipped.

rvz• 11 months ago

It feels like Tdd/llm connection is implied — “and also generate tests”.

That sounds like an anti-pattern and not true TDD to get LLMs to generate tests for you if you don't know what to test for.

It also reduces your confidence in knowing if the generated test does what it says. Thus, you might as well write it yourself.

Otherwise you will get these sort of nasty incidents. [0] Even when 'all tests passed'.

[0] https://sketch.dev/blog/our-first-outage-from-llm-written-co...

Scubabear683• 11 months ago

"Hallucinations aren’t a bug of LLMs, they are a feature. Indeed they are the feature".

I used to avidly read all his stuff, and I remember 20ish years ago he decided to rename Inversion of Control to Dependency Injection. In doing so, and his accompany blog, he showed he didn't actually understand it at a deep level (and hence his poor renaming).

This feels similar. I know what he's trying to say, but he's just wrong. He's trying to say the LLM is hallucinating everything, but Fowler is missing is that Hallucination in LLM terms refers to a very specific negative behavior.

ares6234• 11 months ago

As far as an LLM is concerned, there is no difference between "negative" hallucination and a positive one. It's all just tokens and embeddings to it.

Positive hallucinations are more likely to happen nowadays, thanks to all the effort going into these systems.

Scubabear683• 11 months ago

This basically ruins the term “hallucination” and makes it meaningless, when the term actually describes a real phenomenon.

anthem20256• 11 months ago

No, it’s just an attempt to pretend wrong outputs are some special case when really they aren’t. They aren’t imagine something that doesn’t exist they are just running the same process they do for everything else and it just didn’t work.

If you disagree then I would ask what exactly is the “specific behaviour” you’re talking about?

epolanski6• 11 months ago

I'll take the bait.

What didn't he understand properly about inversion of control then.

Scubabear683• 11 months ago

That it is ultimately about object lifetime management. Dependency injection is a big part of it, but not the only thing.

tricky_theclown• 11 months ago

insane_dreamer2• 11 months ago

I’ve often heard, with decent reason, an LLM compared to a junior colleague. But I find LLMs are quite happy to say “all tests green”, yet when I run them, there are failures. If that was a junior engineer’s behavior, how long would it be before H.R. was involved?

Reminds me of a recent experience when I asked CC to implement a feature. It wrote some code that struck me as potentially problematic. When I said, "why did you do X? couldn't that be problematic?" it responded with "correct; that approach is not recommended because of Y; I'll fix it". So then why did it do it in the first place? A human dev might have made the same mistake, but it wouldn't have made the mistake knowing that it was making a mistake.

epolanski6• 11 months ago

It did so because his training, weights and context and a billion of matrix calculations led him there.

weakfish• 11 months ago

I think this gets lost on far too many discussions. There is no “why” - it’s statistics, and the closest we have to a “why” is that a lot of code out there sucks

nicwolff• 11 months ago

I’ve often heard, with decent reason, an LLM compared to a junior colleague.

No, they're like an extremely experienced and knowledgeable senior colleague – who drinks heavily on the job. Overconfident, forgetful, sloppy, easily distracted. But you can hire so many of them, so cheaply, and they don't get mad when you fire them!

vkou2• 11 months ago

Experienced and knowledgeable and they also believe in the technical equivalent of flat-Earthism in many, many non-trivial corners.

And if you push back on that insanity, they'll smile and nod and agree with you and in the next sentence, go right back to pushing that nonsense.

sho_hn• 11 months ago

One good yardstick, if one has to anthropomorphize, is that LLMs know and believe what's popular. If you ask it to do something that popular developer opinion gets right, it will do fine.

Ask it for things that many people get wrong or just do badly, or can be mistakenly likened to a popular thing in a way that produces a wrong result, and it'll often err.

The trick is having an awareness of what correct solutions are prevalent in training data and what the bulk of accessible code used for training probably doesn't have many examples of. And this experience is hard to substitute for.

Juniors therefore use LLMs in a bumbling fashion and are productive either by sheer luck, or because they're more likely to ask for common things and so stay in a lane with the model.

A senior developer who develops a good intuition for when the tool is worth using and when not can be really efficient. Some senior developers however either overestimate or underestimate the tool based on wrong expectations and become really inefficient with them.

DrewADesign2• 11 months ago

“Here’s the code:” looks totally plausible but hallucinated libFakeCall.

“libFakeCall doesn’t exist. Use libRealCall instead of libFakeCall.”

“You’re absolutely correct. I apologize for blah blah blah blah. Here’s the updated code with libRealCall instead. :[…]”

“You just replaced the libFakeCall reference with libRealCall but didn’t update the calls themselves. Re-write it and cite the docs. “

“Sorry about the confusion! I’ve found these calls in the libRealCall docs. Here’s the new code and links to the docs.”

“That’s the same code but with links to the libRealCall docs landing page.”

“You’re absolutely correct.” It appears that these calls belong to another library with that functionality:” looks totally plausible but hallucinated libFakeCall.

worik• 11 months ago

Yes. Funny

But it is nothing like a wetware colleague. It is a machine.

furyofantares• 11 months ago

These metaphors all suck. Well, ok, yours is funny. But anyway, LLMs are just very different from any human.

They are extremely shallow, even compared to a junior developer. But extremely broad, even compared to the most experienced developer. They type real fuckin fast compared to anyone on earth, but they need to be told what to do much more carefully than anyone on earth.

Guthur2• 11 months ago

I just spent a good 2 hours trying to debug a SM6 Vulkan issue with unreal engine using an LLM, it had got me to good state but UE kept falling to load a project, it transpired that the specific error message would provide a fix as the top Google result, which I found when I eventually decided to look for myself.

LLM did help a lot to get some busy work out of the way, but it's difficult to know when you need to jump out of the LLM loop and go old skool.

CuriouslyC4• 11 months ago

I've gotten Claude Code to make CUDA kernels and all kinds of advanced stuff that there's zero percent chance a junior would pull off.

AI is like a super advanced senior wearing a blindfold. It knows almost everything, it's super fast, and it gets confused pretty quickly about things after you tell it.

tjwebbnorfolk2• 11 months ago

but they need to be told what to do much more carefully than anyone on earth.

have you ever managed an offshore team. holy cow

root_axis• 11 months ago

I think we had the analogy right with "fancy autocomplete". Sometimes, the completions are excellent and do exactly what we want, other times the completions fail to match our expectations and need human attention. It's a tool.

cortesoft• 11 months ago

And they are incessantly cheerful.

I asked Claude to design me a UI, and it made a lovely one... but I wanted a web ui. It very happily through away all its work and made a brand new web UI.

I can't imagine any employee being that quick to just move on after something like that.

Jcampuzano2• 11 months ago

Overconfident, but also easy to sway their opinion.

They follow directions for maybe an hour and then go off and fix random shit because they forgot what their main task was.

They'll tell you to your face how great your ideas were, and that you're absolutely right about something, then go implement it completely incorrectly.

They add comments to literally everything even when you ask them to stop. But they also ignore said comments sometimes.

Maybe they are kinda like us lol.

amelius2• 11 months ago

and they don't get mad when you fire them!

No, typically __you__ are mad when you fire them ...

Arubis• 11 months ago

If you’re making hire/fire decisions while emotional, you’re doing it wrong.

amelius2• 11 months ago

who drinks heavily on the job

Yes, that's why you should add "... and answer in the style of a drunkard" to every prompt. Makes it easier to not forget what you are dealing with.

fencepost• 11 months ago

No, they're like a completely nontechnical marketing person who has a big library of papers on related subjects and who's been asked to generate a whitepaper by pulling phrases. What comes out will probably have proper grammar and seem perfectly reasonable to another person with no knowledge of the field, but if actually read by someone knowledgeable may be complete gibberish taken as a whole.

Individual sentences and paragraphs may mostly work, but it's an edifice built on sand out of poorly constructed bricks plus mortar with the wrong proportions (or entirely wrong ingredients).

LLM output is "truthy" - it looks like it might be true, and sometimes it will even be accurate (see also, "stopped clock") but depending on it is foolish because what's generating the output doesn't actually understand what it's putting out - it's just generating output that looks like the kind of thing you've requested.

AnotherGoodName• 11 months ago

My analogy is a cursed monkey paw with unlimited wishes. It's actually really really powerful but you have to be careful of leaving any possible ambiguity in your wishes.

gck13• 11 months ago

Overconfident, forgetful, sloppy, easily distracted

And constantly microdosing, sometimes a bit too much.

DrewADesign2• 11 months ago

And sometimes it seems like a macro-dose huffing xylene-based glue.

seadan83• 11 months ago

Like a person that has memorized every single answer in the world to every single question ever asked - but is completely dead behind the eyes.

FlyingSnake• 11 months ago

“You’re absolutely right!”

anthem20256• 11 months ago

It’s funny how people acknowledge the railroads and the similarity to AI, but then jump back to comparing it to the internet when it comes to drawing conclusions.

The internet build out left massive amounts of useful infrastructure.

The railroads left us with lots of railroads that fell into disuse and eventually left us with a complete joke of a railway system. Made a few people so rich we started calling them robber barons and talking the gilded age.

Are we going to continue to use the 60 billion dollar data centers in Louisiana when the bubble bursts? Is it valuable infrastructure or just a waste of money that gets written off?

senko• 11 months ago

Are we going to continue to use the 60 billion dollar data centers in Louisiana when the bubble burst

Why not?

Tracks in the middle of nowhere are useless.

Data centers in the middle of nowhere are useful, as long as energy, cooling and uplink are cost-effective.

(Not saying Louisiana is in the middle of nowhere!)

anthem20256• 11 months ago

I don’t disagree, but was Louisiana chosen because it offers cheap energy/cooling/infra or because the government doesn’t bother to ask questions about the impact to thinks like energy and water usage (which is why we are seeing more and more reports of residents upset about pollution, water usage, and energy costs).

sudhirb• 11 months ago

One of the consequences of this is that we should always consider asking the LLM the same question more than once, perhaps with some variation in the wording. Then we can compare answers, indeed perhaps ask the LLM to compare answers for us. The difference in the answers can be as useful as the answers themselves.

There was once a coding agent which achieved SOTA performance on SWE Bench Verified by "just" running the agent 5 times on each instance, scoring each attempt and picking the attempt with the highest score: https://aide.dev/blog/sota-bitter-lesson

jeppester• 11 months ago

In my company I feel that we getting totally overrun with code that's 90% good, 10% broken and almost exactly what was needed.

We are producing more code, but quality is definitely taking a hit now that no-one is able to keep up.

So instead of slowly inching towards the result we are getting 90% there in no time, and then spending lots and lots of time on getting to know the code and fixing and fine-tuning everything.

Maybe we ARE faster than before, but it wouldn't surprise me if the two approaches are closer than what one might think.

What bothers me the most is that I much prefer to build stuff rather than fixing code I'm not intimately familiar with.

epolanski6• 11 months ago

As Fowler himself states, there's a need to learn to use these tools properly.

In any case poor work quality is a failure of tech leadership and culture, it's not AI's fault.

FromTheFirstIn2• 11 months ago

It’s funny how nothing seems to be AI’s fault.

utyop227• 11 months ago

"but quality is definitely taking a hit now that no-one is able to keep up."

And its going to get worse! So please explain to me how in the net, you are going to be better off? You're not.

I think most people haven't taken a decent economics class and don't deeply understand the notion of trade offs and the fact there is no free lunch.

computerex6• 11 months ago

Technology has always helped people. Are you one of the people that say optimizing compilers are bad? Do you not use the intellisense? Or IDEs? Do you not use higher level languages? Why not write in assembly all the time? No free lunch right.

Yes there are trade offs, but at this point if you haven’t found a way to significantly amplify and scale yourself using llms, and your plan is to instead pretend that they are somehow not useful, that uphill battle can only last so long. The genie is out of the bag. Adapt to the times or you will be left behind. That’s just what I think.

globular-toast2• 11 months ago

Yep, my strong feeling is that the net benefit of all of this will be zero. The time you have to spend holding the LLM hand is almost equal to how much time you would have spent writing it yourself. But then you've got yourself a codebase that you didn't write yourself, and we all know hunting bugs in someone else's code is way harder than code you had a part in designing/writing.

People are honestly just drunk on this thing at this point. The sunken cost fallacy has people pushing on (ie. spending more time) when LLMs aren't getting it right. People are happy to trade convenience for everything else, just look at junk food where people trade in flavour and their health. And ultimately we are in a time when nobody is building for the future, it's all get rich quick schemes: squeeze then get out before anyone asks why the river ran dry. LLMs are like the perfect drug for our current society.

Just look at how technology has helped us in the past decades. Instead of launching us towards some kind of Star Trek utopia, most people now just work more for less!

threecheese• 11 months ago

Fast feedback is one benefit, given the 90% is releasable - even if only to a segment of users. This might be anathema to good engineering, but a benefit to user experience research and to organizations that want to test their market for demand.

Fast feedback is also great for improving release processes; when you have a feedback loop with Product, UX, Engineering, Security etc, being able to front load some % of a deliverable can help you make better decisions that may end up being a time saver net/net.

naasking4• 11 months ago

And its going to get worse!

That isn't clear given the fact that LLMs and, more importantly, LLM programming environments that manage context better are still improving.

Cthulhu_2• 11 months ago

I'd argue that this awareness is a good thing; it means you're measuring, analyzing, etc all the code.

Best practices in software development for forever have been to verify everything; CI, code reviews, unit tests, linters, etc. I'd argue that with LLM generated code, a software developer's job and/or that of an organization as a whole has shifted even more towards reviewing and verification.

If quality is taking a hit you need to stop; how important is quality to you? How do you define quality in your organization? And what steps do you take to ensure and improve quality before merging LLM generated code? Remember that you're still the boss and there is no excuse for merging substandard code.

whstl• 11 months ago

LLMs are amazing at producing boilerplate, which removes the incentive to get rid of it.

Boilerplate sucks to review. You just see a big mass of code and can't fully make sense of it when reviewing. Also, Github sucks for reviewing PRs with too many lines.

So junior/mid devs are just churning boilerplate-rich code and don't really learn.

The only outcome here is code quality is gonna go down very very fast.

jstummbillig2• 11 months ago

I envy the people working at mystical places where humans were on average writing code of high quality prior LLMs. I'll never know you now.

dapperdrake2• 11 months ago

Perlis, epigram 7:

7. It is easier to write an incorrect program than understand a correct one.

Link: http://cs.yale.edu/homes/perlis-alan/quotes.html

ekidd• 11 months ago

In my company I feel that we getting totally overrun with code that's 90% good, 10% broken and almost exactly what was needed.

This is painfully similar to what happens when a team grows from 3 developers to 10 developers. All of sudden, there's a vast pile of coding being written, you've never seen 75% of it, your architectural coherence is down, and you're relying a lot more on policy and CI.

Where LLM's differ is that you can't meaningfully mentor them, and you can't let them go after the 50th time they try turn off the type checker, or delete the unit tests to hide bugs.

Probably, the most effective way to use LLMs is to make the person driving the LLM 100% responsible for the consequences. Which would mean actually knowing the code that gets generated. But that's going to be complicated to ensure.

jimbokun7• 11 months ago

Have thorough code reviews and hold the developer using the LLM responsible for everything in the PR before it can be merged.

thrawa83873363• 11 months ago

Imagine someones add 10 UTs carefully devised and someone notices they need 1 more during the PR.

Scenario B, you add 40 with an LLM, that look good on paper but only cover 6 of the original ones. Besides, who's going to pay careful attention to a PR with 40.

"Must be so thorough!".

stevage2• 11 months ago

What bothers me the most is that I much prefer to build stuff rather than fixing code I'm not intimately familiar with.

Me too. But I think there's a split here. Some people love the new fast and loose way and rave about how they're experiencing more joy coding than ever before.

But I tried it briefly on a side project, and hated the feeling of disconnect. I started over, doing everything manually but boosted by AI and it's deeply satisfying. There is just one section of AI written code that I don't entirely understand, a complex SQL query I was having trouble writing myself. But at least with an SQL query it's very easy to verify the code does exactly what you want with no possibility of side effects.

woah• 11 months ago

One of the consequences of this is that we should always consider asking the LLM the same question more than once, perhaps with some variation in the wording. Then we can compare answers, indeed perhaps ask the LLM to compare answers for us. The difference in the answers can be as useful as the answers themselves.

This is what LLM "reasoning" does. More than "reasoning" in the human sense, it just reduces variance from variations in the prompt and random next token prediction.

stillsut• 11 months ago

we should always consider asking the LLM the same question more than once, perhaps with some variation in the wording. Then we can compare answers,

Yup, this matches my recommended workflow exactly. Why waste time trying to turn an initially bad answer into a passable one, when you could simply re-generate (possibly with different context)

I wrote up an example of this workflow here: https://github.com/sutt/agro/blob/master/docs/case-studies/a...

oo0shiny• 11 months ago

My former colleague Rebecca Parsons, has been saying for a long time that hallucinations aren’t a bug of LLMs, they are a feature. Indeed they are the feature. All an LLM does is produce hallucinations, it’s just that we find some of them useful.

What a great way of framing it. I've been trying to explain this to people, but this is a succinct version of what I was stumbling to convey.

jstrieb2• 11 months ago

I have been explaining this to friends and family by comparing LLMs to actors. They deliver a performance in-character, and are only factual if it happens to make the performance better.

https://jstrieb.github.io/posts/llm-thespians/

lagrange772• 11 months ago

I'll steal that.

red75prime2• 11 months ago

The analogy goes down the drain when a criterion for good performance is being objectively right. Like with Reinforcement Learning from Verifiable Rewards.

ngc2482• 11 months ago

A better analogy is of an overconfident 5 year old kid, who never says that they don't know the answer and always has an "answer" for everything.

bo1024• 11 months ago

This is also related to the philosophical definition of bullshit[1]: speech intended to persuade or influence without any active intention to be either true or false.

[1] https://en.wikipedia.org/wiki/On_Bullshit

mohsen1• 11 months ago

Intelligence in a way is the ability to filter out useless information. Be it, thoughts or sensory information

tugberkk• 11 months ago

Yes, can't remember who said it but LLM's always hallucinate, it is just that they are 90 something percent right.

OtomotO• 11 months ago

Which totally depends on your domain and subdomain.

E.g. Programming in JS or Python: good enough

Programming in Rust: I can scrap over 50% of the code because it will

a) not compile at all (I see this while the "AI" types)

b) not meet the requirements at all

ljm2• 11 months ago

If I was to drop acid and hallucinate an alien invasion, and then suddenly a xenomorph runs loose around the city while I’m tripping balls, does being right in that one instance mean the rest of my reality is also a hallucination?

Because it seems the point being made multiple times that a perceptual error isn’t a key component of hallucinating, the whole thing is instead just a convincing illusion that could theoretically apply to all perception, not just the psychoactively augmented kind.

aitchnyu2• 11 months ago

All models are wrong, some are merely useful - 1976/1933/earlier adage.

pjmorris• 11 months ago

Generally attributed to George Box

lagrange772• 11 months ago

Right, all models are inherently wrong. It's up to the user know about its limits / uncertainty.

But i think this 'being wrong' is kind of confusing when talking about LLMs (in contrast to systems/scientific modelling). In what they model (language), the current LLMs are really good and acurate, except for example the occasional chinese character in the middle of a sentence.

But what we mean by LLMs 'being wrong' most of the time is being factually wrong in answering a question, that is expressed as language. That's a layer on top of what the model is designed to model.

EDITS:

So saying 'the model is wrong' when it's factually wrong above the language level isn't fair.

I guess this is essentially the same thought as 'all they do is hallucinate'.

jedimastert• 11 months ago

Talking to LLMs reminds me of my time as a system administrator for my university's physics department. A bunch of incredibly smart, PhD level people...just not in the subject they were talking to me about, most of the time.

archeantus• 11 months ago

If all you’ve done with AI is just use it for autocomplete, you’re missing out big time. I built a slick react app using Lovable yesterday and then created a Node BE using Claude Code today. I told Claude Code to look through the FE code to understand the requirements and purpose of the site, and to build a detailed plan (including proposed DB schemas) for a system that could support that functionality.

It generated a thousand line file with a robust breakdown of everything that needed to be done and at my command it did it. We went module by module and I made sure that each module Had comprehensive unit test coverage and that the repo built well as we went. After a few hours of back and forth we made 9 modules, 60+ APIs across 10 different tables, and hundreds of unit tests that all are passing.

Does that mean that I’m all done and ready to deploy to prod? Unlikely. But it does mean that I got a ton of boilerplate stuff put into place really quickly and that I’m eight hours into a project that would have taken at least a month before.

Once the BE was done I had it generate extensive documentation for the agent that would handle the FE integration as a sort of instruction guide - in case we need it. As issues and bugs arise during integration (they will!) the model has everything it needs to keep on track and finish the job it set out to do.

What a time to be alive!

computerex6• 11 months ago

I feel exactly the same way.

Even this post by Martin Fowler shows he's an aging dinosaur stuck in denial.

I’ve often heard, with decent reason, an LLM compared to a junior colleague. But I find LLMs are quite happy to say “all tests green”, yet when I run them, there are failures. If that was a junior engineer’s behavior, how long would it be before H.R. was involved?

I don't know what "LLM's" he's using but I just simply don't get hallucinations like that with cursor or claude code.

He ends with this: > LLMs create a huge increase in the attack surface of software systems. Simon Willison described the The Lethal Trifecta for AI agents: an agent that combines access to your private data, exposure to untrusted content, and a way to externally communicate (“exfiltration”). That “untrusted content” can come in all sorts of ways, ask it to read a web page, and an attacker can easily put instructions on the website in 1pt white-on-white font to trick the gullible LLM to obtain that private data.

Not sure why he is re-iterating well known prompt injection vulnerability, passing it off as a general weakness of LLM's that applies to all LLM use when that's not the reality.

daveguy3• 11 months ago

Okay, but LLMs will produce a thousand lines of code for Hello World.

Plus, there is no telling how many bugs you have in that code.

rester324• 11 months ago

Can you share the code with us? I'd love to see that

Bridged7756• 11 months ago

60+ APIs? Odd way to word. Did you mean endpoints?

What's the impressive thing here? No one said AI can't do boilerplate. Especially if you're doing run of the mill crud and starting from a clean slate, in fact, that's probably one of its only useful usages in my opinion. You still need to understand your system when maintaining or adding features to it, do a code review, understand the architecture, ensure its architecture allows for pivots in design/functionality, etc.

rootusrootus• 11 months ago

For a hot second I thought LLMs were coming for our jobs. Then I realized they were just as likely to end up creating mountains of things for us to fix later. And as things settle down, I find good use cases for Claude Code that augment me but are in no danger of replacing me. It certainly has its moments.

jama21114• 11 months ago

Finally, an opinion on here that’s reasonable and isn’t “AI is perfect” or “AI is useless”.

mexicocitinluez2• 11 months ago

One of the things that has struck me as odd is just how little self-awareness devs have when talking about "skin in the game" with regard to CEO's hawking AI products.

Like, we have just as much to lose as they have to gain. Of course a part of us doesn't want these tools to be as good as some people say they are because it directly affects our future and livelihood.

No, they can't do everything. Yes, they can do some things. It's that simple.

siffland• 11 months ago

The issue is a lot of times it takes a senior level programmer to reason with a LLM to get the results needed. What happens when there are no Juniors to replace the Seniors. I guess by then AI will be able to program efficiently enough.

So far AI seems to be a great augmentation, but not a replacement.

dwa3592• 11 months ago

Stop comparing LLMs to Humans for fucks sake. Anthropomorphization is fine as long as it serves a purpose, it doesn't in this case.

thrown-0825• 11 months ago

Dont forget the gas lighting when they make a mistake and try to act like it was you.

npn• 11 months ago

I will believe his rant when Fowler creates any notable software.

rhubarbtree• 11 months ago

Software Engineering is unusual in that it works with deterministic machines. Maybe LLMs mark the point where we join our engineering peers in a world on non-determinism.

Recently some people have compared LLMs to compilers and the resulting source code to object code. This is a false analogy, because compilation is (almost always) a semantics preserving transformation. LLMs are given a natural language spec (prompt) that by definition is underspecified. And so they cannot be semantics preserving, as the semantics of their input is ambiguous.

The programmer is left with two options: (1) understanding the resulting code, repairing and rewriting it, or (2) ignoring the code and performing validation by testing.

Both of these approaches are assistive. At least in its current form, AI can only accelerate a programmer, not replace them. Lovable and similar tools rely on very informal testing, which is why they can be used by non-programmers, but they have very little chance of producing robust software of any complexity. I’ve seen people creating working web apps, but I am confident I could find plenty of strange bugs just by testing edge cases or stressing non functional qualities. The bigger issue is the bugs I can’t find because they’re not bugs a human programmer would create.

Option (1) is problematic because LLMs tend not to produce clean code designed to be human readable. A lot of the efforts coders are making is to break down tasks and try to guide the LLMs to produce good code. I have yet to see this work for anything novel and complex. For non trivial systems, reasoning and architecture are required. The hope is that a programmer can write specs well enough that LLMs can “fill in the gaps”. But whether this is a net positive once considering the work involved is still an open question. I’ve yet to see any first hand evidence that there’s a productivity gain here. It’s early days.

Option(2) is also difficult because there is a crucial factor missing in AI coding, the “generality of intent” as a human user. This is a problem, because the non-trivial bugs an AI produces are unlikely to be similar to those from a human. Those bugs are usually a failure of reasoning, but LLMs don’t reason in the same sense that humans do, so testing in the same way may not be possible. Your intuitions for where bugs lie are no longer applicable. The likely result is worse code produced more quickly, and that trade-off needs exploring.

At the moment I think AI is useful for (a) discussions around design, libraries, debugging, (b) autocomplete, (c) agent analysis of existing code where partial answers are ok and false positives acceptable (eg finding some but not all bugs). Agent coding doesn’t seem ready for production to me, not until we have much better tooling to prevent some of these problems, or AI becomes capable of proper reasoning.

ngc2482• 11 months ago

Exactly, there is a reason why we don't program in natural languages and use "programming languages" which use a subset of keywords to program. Natural language is too ambiguous and not of use in serious programming.

dragonwriter3• 11 months ago

My former colleague Rebecca Parsons, has been saying for a long time that hallucinations aren’t a bug of LLMs, they are a feature. Indeed they are the feature. All an LLM does is produce hallucinations, it’s just that we find some of them useful.

This is an example of my least favorite style of feigned insight: redefining a term into meaninglessness just so you can say something that sounds different while not actually saying anything new.

Yes, if you redefine "hallucination" from "produce output containing detailed information despite that information not being grounded in external reality, in a manner distantly analogous to a human reporting sense data produced by a literal hallucination rather than the external inputs that are presumed normally to ground sense data" to "produce output", its true that all LLMs do is "hallucinate", and that "hallucinating" is not a undesirable behavior.

But you haven't said anything new about the thing that was called "hallucination" by everyone else, or about the thing--LLM output in general--that you have called "hallucination". Everyone already knew that producing output wasn't undesirable. You've just taken the label conventionally attached to a bad behavior, attached it to a broader category that includes all behavior, and used the power of equivocation to make something that sounds novel without saying anything new.

scott_w6• 11 months ago

Fowler is not really redefining "hallucination." He's using a form of irony that emphasises how fundamental "hallucinations" are to the operation of the system. One might also say "you can't get rid of collateral damage from bombs. Indeed, collateral damage is the feature, it's just some of that is what we want to blow up."

You're not meant to take it literally.

xyzzy123• 11 months ago

You might as well say it's interpolating or extrapolating. That's what people are usually doing too, even when recalling situations that they were personally involved in.

I think we call it "hallucinating" when the machine does this in an un-human-like way.

bartread• 11 months ago

I’ve never liked that this behaviour is described using the term “hallucination”.

If a human being talked confidently about something that they were just making up out of thin air by synthesizing based (consciously or unconsciously) on other information they know you wouldn’t call it “hallucination”: you’d call it “bullshit”.

And, honestly, “bullshit” is a much more helpful way of thinking about this behaviour because it somewhat nullifies the arguments people make against the use of LLMs due to this behaviour. Fundamentally, if you don’t want to work with LLMs because they sometimes “bullshit”, are you planning on no longer working with human beings as well?

It doesn’t hold up.

But, more than that, going back to your point: it’s much harder to redefine the term “bullshit” to mean something different to the common understanding.

All of that said, I don’t mind the piece and, honestly, the “I haven’t the foggiest” comment about the future of software development as a career is well made. I guess it’s just a somewhat useful collection of scattered thoughts on LLMs and, as such, an example of a piece where the “thoughts on” title fits well. I don’t think the author is trying to be particularly authoritative.

dragonwriter3• 11 months ago

I’ve never liked that this behaviour is described using the term “hallucination”.

I have a standard canned rant about "confabulation" is a much better metaphor, but it wasn't the point I was focussed on here.

Fundamentally, if you don’t want to work with LLMs because they sometimes “bullshit”, are you planning on no longer working with human beings as well?

I will very much not voluntarily rely on a human for particular tasks if that human has demonstrated a pattern of bullshitting me when given that kind of task, yes, especially if, on top of the opportunity cost inherent in relying on a person for a particular task, I am also required to compensate them—e.g., financially—for their notional attention to the task.

movpasd• 11 months ago

At the risk of sounding woo, I find some parallels in how LLMs work to my experiences with meditation and writing. My subjective experience of it is that there is some unconscious part of my brain that supplies a scattered stream of words as the sentence forms --- without knowing the neuroscience of it, I could speculate it is a "neurological transformer", some statistical model that has memorised a combination of the grammar and contextual semantic meaning of language.

The difference is that the LLM is _only that part_. In producing language as a human, I filter these words, I go back and think of new phrasings, I iterate --- in writing consciously, in speech unconsciously. So rather than a sequence it is a scattered tree filled with rhetorical dead ends, pruned through interaction with my world-model and other intellectual faculties. You can pull on one thread of words as though it were fully-formed already as a kind of Surrealist exercise (like a one-person cadavre exquis), and the result feels similar to an LLM with the temperature turned up too high.

But if nothing else, this highlights to me how easily the process of word generation may be decoupled from meaning. And it serves to explain another kind of common human experience, which feels terribly similar to the phenomenon of LLM hallucination: the "word vomit" of social anxiety. In this process it suddenly becomes less important that the words you produce are anchored to truth, and instead the language-system becomes tuned to produce any socially plausible output at all. That seems to me to be the most apt analogy.

scott_w6• 11 months ago

If a human being talked confidently about something that they were just making up out of thin air by synthesizing based (consciously or unconsciously) on other information they know you wouldn’t call it “hallucination”: you’d call it “bullshit”.

I'd recommend you watch https://www.youtube.com/watch?v=u9CE6a5t59Y&t=2134s&pp=ygUYc... which covers the topic of bullshit. I don't think we can call LLM output "bullshit" because someone spewing bullshit has to not care about whether what they're saying is true or false. LLMs don't "care" about anything because they're not human. It's better to give it an alternative term to differentiate it from the human behaviour, even if the observed output is recognisable.

jimbokun7• 11 months ago

"Bullshit engine" is the term that best explains to a lay person what it is that LLMs do.

ljm2• 11 months ago

If everything is a hallucination then nothing is a hallucination.

Thanks for listening to my Ted Talk.

belter• 11 months ago

You've just taken the label conventionally attached to a bad behavior, attached it to a broader category that includes all behavior, and used the power of equivocation to make something that sounds novel without saying anything new.

You mean like every evangelist says AI changes everything every 5 min...When in reality what they mean is neural nets statistical code generators are getting pretty good? because that is almost all AI there is at the moment?

Just to make the current AI sound bigger than it is?

cess112• 11 months ago

The point, though awkwardly stated, is that there is no difference between 'hallucination' output and any other output from these compressed databases, just like there is no such difference between two queries on a typical RDBMS.

It's a good point.

dragonwriter3• 11 months ago

The point, though awkwardly stated, is that there is no difference between 'hallucination' output and any other output from these compressed databases,

But there is, except when you redefine "hallucination" so there isn't. And, when you retain the definition where there is a difference, you find there are techniques by which you can reduce hallucinations, which is important and useful. Changing the definition to eliminate the distinction is actively harmful to understanding and productive use of LLMs, for the benefit of making what superficially seems like an insightful comment.

sceptic123• 11 months ago

I actually found that comment interesting. It's pointing towards something I've struggled with around LLMs. They are (currently) incapable of knowing if what they output is correct, so the idea that "it's all hallucinations" acknowledges that point and gives useful context for anyone using LLMs for software development.

Eridrus• 11 months ago

Humans are also incapable of knowing whether their output is correct. We merely convince ourselves that it is and then put our thoughts in contact with the external world and other people to see if we actually are.

thwarted• 11 months ago

It's meant to contrast/correct the claim that

"LLMs produce either truth or they produce hallucinations."

Claims worded like that give the impression that if we can just reduce/eliminate the hallucinations, all that will remain will be the truth.

But that's not the case. What is the case is that all the output is the same thing, hallucination, and that some of those hallucinations just so happen to reflect reality (or expectations) so appears to embody truth.

It's like rolling a die and wanting one to come up, and when it does saying "the die knew what I wanted".

An infinite number of monkeys typing on an infinite number of typewriters will eventually produce the script for Hamlet.

That doesn't mean the monkeys know about Shakespeare, or Hamlet, or even words, or even what they are doing being chained to typewriters.

We've found a way to optimize the infinite typing monkeys to output something that passes for Hamlet much sooner than infinity.

redditor98654• 11 months ago

The way you have expressed this, I am borrowing it for myself. Many times i run into these kind of situations and I fail to explain why doing something like this is frustrating and actually useless. Thank you.

TomBers• 11 months ago

One aspect of using LLM's to code that I have not seen mentioned is the "loss of attachment" to code in my projects.

For example, working on my project (https://mudg.fly.dev) I wanted to experiment with a new FE graph library. I asked an llm the fantastic (https://tidewave.ai) and it completely implemented a solution, which turned out to be worse than the current solution.

If I had spent many hours working on a solution I might have been more "attached" to the solution and kept with it.

Perhaps this is a good thing? (https://en.wikipedia.org/wiki/Nonattachment_(philosophy))

raziel2p2• 11 months ago

Have you considered that LLMs are also biased against new languages and libraries, so the code quality will be worse compared to something more established regardless of what you personally think/feel?

kookamamie• 11 months ago

We are still figuring out how to use LLMs, and it will be some time before we have a decent idea of how to use them well

As per typical, Martin is so late to the party that he projects his own lack of understanding of the subject to be the general state of matters.

People are, and have been for a while, using LLMs as very effective accelerators or augmentations of themselves.

Claude Code, is a great example of something that can easily one-shot most trivial-to-average programming tasks.

While some wait for the "bubble to burst", a lot of people are gaining significant benefits from using LLMs to boost their speed in software development.

It's not a good idea to muddy the expectations for AI by mixing AGI and LLMs into the same bucket. LLMs are already useful for many purposes, even if they didn't lead to AGI, whatever the definition is.

ChrisMarshallNY• 11 months ago

One of the consequences of this is that we should always consider asking the LLM the same question more than once, perhaps with some variation in the wording. Then we can compare answers, indeed perhaps ask the LLM to compare answers for us. The difference in the answers can be as useful as the answers themselves.

That’s a useful tip.

jonnycomputer2• 11 months ago

All an LLM does is produce hallucinations, it’s just that we find some of them useful.

Nice callback to "All models are wrong, but some of them are useful."

mikewarot• 11 months ago

Should senior engineers get out of the profession before it’s too late?

If they are actually using the engineering principles that their job description hints at, they'll probably be fine. Software Engineering is a growing field, the need for actual Engineers (you know, the kind that get licensed in other fields) is unlikely to shrink any time soon.

siliconc0w• 11 months ago

I started a greenfield project the other day and was excited to see how far AI has gotten for a real internal business use-case - not just a toy project. It was great at setting up the scaffolding and helping with some of the tests but overall it might have been a drain on productivity.. I had to repeatedly correct it and explicitly tell it how to do things (or hunt down examples in the codebase) or it would make up APIs or generate not particularly idiomatic code for our codebase (was writing golang fwiw).

There is really a balance between spoon-feeding it the answers vs just abandoning it - the spikiness is really apparent where it can totally nail some things but then utterly fail what should be pretty simple (i.e there was a point where I wanted to refactor two similar functions into a shared library and it just couldn't do it)

tony2016• 11 months ago

The developer is supposed to check and verify the code that an LLM creates. Ask for smaller requests in the prompts so you don't get too much code. Unit tests are verified and run by the developer. I don't know what he means by an LLM runs all the tests and gives green. It can fake running tests. I always the tests myself, whether they were created by an LLM or not.

abcde-bbq• 11 months ago

More details on tolerance would be nice. Examples:

1: when building games for my kids, the tolerance is high so the LLM can implement a feature in one shot and I will manually test the feature (without bothering to review the code or generating unit tests or using a type-safe language).

2: building a demo at work has lower tolerance for errors, but is still good with LLM with a brief code review.

3: for production code, other processes can be in place to meet the even lower tolerance requirements.

Some thoughts on LLMs and software development

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