Posted by dimonomid 12 hours ago
One interesting comparison is to the history of manufacturing. West/America decided one day that manufacturing would be cheaper to outsource and better (short term) profit was to be made by outsourcing it all to China. The institutional expertise started to deteriorate, to the point that America simply didn't even have the capacity, or expertise anymore to produce stuff (such as grill brush [1])
I feel like you could take all the handwavy comment that are made today to dismiss this caution, and find equal dismissal back then when companies were actively outsourcing the manufacturing.
"I'm coding 10x faster" "look at the output velocity per employee"
"we are producing much more (in China)" "look at profit / number of (manufacturing) employers"
Seems ok if you're American / Chinese but I'm struggling to understand how the rest can be OK with allowing institutional knowledge to deteriorate while having an active dependency to the former two. We already see this with the tech dependency towards USA and manufacturing competition from China.
Absolutely people were extremely dismissive to anyone saying that we're losing the ability to make things in this country!
There were all these theories like Comparative Advantage that people would trot out to point out that, if you don't like outsourcing, not only are you ignorant and backwards you're also probably racist.
I guess we were all lacking wisdom.
When I was in college or going to parties with New Yorker Reader types I'd try to argue against globalization and they would start smugly dropping their theories on me. I was too young and naive to refute them in any kind of convincing way, but my life experience told me that it was wrong.
But it was pervasive in the zeitgeist. Basically anyone trying to argue against it was backwards and stupid, or shrill if they were lefty.
It was so very weird. Even in Ireland (which was the target of the first wave of US outsourcing), people seemed to believe that everyone could be "knowledge workers" and that industry was unimportant.
I still don't really get why everyone believed this, but they did. Something something Upton Sinclair I guess.
However I'm not sure it could really be stopped or whether it was inevitable from containerization. Can you really prevent people from getting cheaper products forever?
What technology was invented in 2000 that allowed everything to go to China? No technology. That was the year the US adopted Permanent Normalized Trade Relations with China. It was a government policy change!
By the way the US already had trade relations with China. All PNTR did was promise not to change the policy in the future. That was the starting gun that signaled to all American companies that they should start investing in production in China.
So would globalization happen on some scale just because of the march of technology? Sure. But the way it happened, the speed at which it happened and the extent to which it happened, were the result of policy choices.
This sort of thing takes time, and by the 00s, enough time (and a recent Hong Kong transfer that did not result in mass liquidation) passed, and foreign investors were gaining confidence.
The world, and especially China has agency of its own, it isn't sorely driven by whatever American policy is it's flavour of the week.
The point is that it wasn't technology it was policy, on the Chinese and American side, that drove the China Shock.
This was also what a lot of anti-globalization people were say at the time. The pro-globalization people were saying not only is it good but it's inevitable. There Is No Alternative.
The anti globalization people were saying if it's inevitable why does it need all these policy changes, trade agreements, and bigwig conferences?
This whole subthread is from like an alternate timeline where the world actually found out or agreed that globalization was bad and stopped or slowed it somehow? Did the US meaningfully take back manufacturing or something?
If globalization is not inevitable, what's the alternative?
No, but there definitely was a much slower gradual repression over a longer period, with a final round of protest repression: https://en.wikipedia.org/wiki/2019%E2%80%932020_Hong_Kong_pr...
> The world, and especially China has agency of its own, it isn't sorely driven by whatever American policy is it's flavour of the week
Amen. A two-pole model is simple and attractive but the rest of the world is also, you know, doing stuff and buying things. America can, for example, preserve its car industry by refusing to allow Chinese EVs, but will gradually discover it has become uncompetitive overseas.
Do you even want cheap products? We're all trapped in low-quality-product hell together, and some of us have realized we'd be less frustrated and less poor with something at least not-quite-so-low-quality.
If people could have realized that sooner, and if there had been some vision or political will at the top, stopping this would've been a matter of very boring policy decisions. It wasn't inevitable. Containerization might have made it easier, but it didn't make it irresistible.
Even back then people on the left had a problem with the term globalization. On the left it was wildly recognized that the term came from the right to decorate global capitalism and (what was then called) Neo-colonialism in a more palatable light for liberals. In Iceland we used to make fun of it and said it should instead be called Americanization.
Right-wing anti-globalization is basically just hating foreigners (except white Americans), and supporting border fascism. Interestingly the right wing types in Europe love American culture, and have no problem with America spreading their capitalism and Neo-liberalism world wide (they only hate it when the EU does it).
Yes, economists famously do, and they also lack intelligence and knowledge given that their theories keep being proven wrong by reality but they never update them.
A trained economist either sells out to the Overlords or he gets a job as a barkeep. Or he ends up as some crank in a half-forgotten nonprofit somewhere. There's no middle ground.
Human intelligence is a remarkable adaptation, but "tendency towards delusion" might be the decidedly maladaptive trait that comes with it.
It's like saying that the Titanic was at risk of sinking after it hit the iceberg.
> A trained economist either sells out to the Overlords or he gets a job as a barkeep.
That's what training is for.
> Human intelligence is a remarkable adaptation, but "tendency towards delusion" might be the decidedly maladaptive trait that comes with it.
I'd say the reason is more external than internal - personal interest is a mind bender.
The idea that the US lost its manufacturing is not based in reality. One reason people think that is because people think cheap plastic crap and consumer electronics when they think “manufacturing.” Another reason is that US manufacturing has become highly efficient and automated, so a fairly small portion of the population works in it.
Yeah, your iPhone wasn’t built in the US. But the plane that got it here probably was.
Values are 2021 because that's the most recent available for US at the same source, to keep consistent.
World Manufacturing output: 16.16 trillion https://data.worldbank.org/indicator/NV.IND.MANF.CD
US Manufacturing output: 2.5 trillion (15.47% of world) https://data.worldbank.org/indicator/NV.IND.MANF.CD?location...
China Manufacturing output: 4.85 trillion (30.01% of world) https://data.worldbank.org/indicator/NV.IND.MANF.CD?location...
Whatever analysis you make, let's use the correct numbers, from which you were off by about 1/5th. Should you think that is a negligible margin, you'd have to consider the US participation in global manufacturing output as similarly significant.
I'm sure fresh numbers are available, they're just unpublished.
We could do some educated guessing here - in the last 5 years China's economy grew at the rates of 8.6%, 3%, 5.4%, 5%, 5%, the US grew at 6.5%, 2.5%, 2.9%, 2.8%, 2.2%. We can extrapolate the manufacturing numbers based on that, which would give some advantage to the US because the US manufacturing lags the average, while the Chinese leads it.
The math gives us the current values for
World: $19.4 T, (%6.5,%3.4,3x%2.9)
China: $6.3 T - 32.5%,
US: $2.9 T - 15%.
Four. 2021 vs 2025. Unless you have numbers from 2026 from the future.
> and you’re going to hassle me about not having accurate figures?
Merely pointing out. The subjective interpretation of the experience is entirely on you.
> What the fuck.
Get more recent numbers that show I'm wrong and your 3 trillion & 1/5th of world total is right then.
World total: 17.6 trillion (https://data.worldbank.org/indicator/NV.IND.MANF.CD?location...) US: 2.961 trillion, 16.82% of world China: 4.82 trillion, 27.38% of world total
So you were very much closer to the 3 trillion figure (again, 2026 annualized data is not current, it's forecast). Still quite off from the 1/5th of total. About the same as the EU - 3.03 trillions for 2025 (https://www.macrotrends.net/global-metrics/countries/euu/eur...)
In terms of participation in world total the US has come down from 22.44% in 1997 to 16.82% in 2025 losing 5.38 percentage points or a loss of over 1/5th in less than 30 years.
I'd say there is some justification to claim a decline in US manufacturing.
Again thank you for engaging productively and providing up to date data. I feel like I should offer praise as I chastised you for cursing at me for using data 4 years out of date.
There’s a decline in the relative share, but that’s down to the rest of the world growing. Adjusted for inflation, 1997 was slightly below the current figure. That’s nothing to brag about, but it’s a far cry from the “we don’t make anything anymore” narrative I see so much.
Where it has declined substantially is employment. That peaked at almost 20 million at the end of the 70s and is now under 13 million.
> The idea that the US lost its manufacturing is not based in reality.
But I disagree with your claim that people are right in being extremely dismissive of losses in US manufacturing.
>> Absolutely people were extremely dismissive to anyone saying that we're losing the ability to make things in this country!
Interpreting that as a complete loss of manufacture capacity I agree with you, with the qualification that concerns of relative decline were justified since there was a definite decline in aggregate.
I have to say that overall I don't think economies and citizens of these economies should prefer certain economic activities over others. In principle I think specialization is a good thing. On the other hand, I do think that the US has committed some strategy mistakes in terms of how much manufacturing activity was moved out of the country and how dependent it has come to be on foreign supplies of, for instance, rare earths and advanced IC.
But that analysis is predicated on US strategic goals and geopolitical intent. The coming impasse over Taiwan with China is almost completely self imposed due to the prioritization of short term corporate profit over strategic goals.
But that is a different conversation altogether. I think data from the past 30 years show that alarmism over de-industrialization was overblown as you said AND that there has been nonetheless a significant decline in the previously absolutely dominant position of the US economy in global manufacturing. If that is good or bad, to whom or to what ends, is way outside the scope of internet forum messages.
Hint: not here. Even basic machining is cooked here. Go on Xometry and compare the pricing of any simple design made in USA vs. China and you’ll see, we can’t competitively do the basics anymore (you conflate basic with crap).
Europe owned high end quality, especially Germany.
Most Radar technology was invented in the UK and then sent to the USA where American engineers would redesign it for mass production. You'd have a magnetron that the British machined at great expense and the Americans would redesign it so it could be stamped out by the thousands.
Germans would look at the wrecks of bombers they shot down. Early on they'd look at a B17 and say "this thing is crude" but by 1944 they'd look at a late model bomber and say "we don't even know how they made some of this stuff".
People have it entirely backwards. They think if you can do high end manufacturing the low end is easy. In fact if you can do mass production of low end parts with consistent quality then high end stuff is easy.
So who's going to buy the low-end stuff at high-end prices?
You can try tariffs and the like (as we're foolishly doing right now) - that usually ends up having the opposite intended effect. You make everything more expensive domestically, including manufacturing (b/c your inputs got more expensive), and hurt our ability to export, even the things we did well.
Meanwhile, the rest of the world unencumbered with protectionism reaps the benefits of free trade and out competes us.
All this eventually leads to a loss of manufacturing jobs and output and manufacturers that can only sell to the domestic market (instead of the whole world). The whole sector starts to shrink. So that's no good.
China has to import much of their raw materials. Wood from SEA and Russia, oil from the middle east and Russia, even coal, ore and beef cattle from Australia! They don't have the water supply, forests, mines or oil that exist in North America.
There is no good reason for them to be the manufacturing center of the globe and there were rational reasons why the US was the center of manufacturing in the globe in the 20th century.
The reason they became the global center of manufacturing was policy choices in China and the US at the turn of this century, and the reason why they remain the center of manufacturing is they developed a massive amount of capital and know-how in an era where the USA was actively destroying its own.
Australia was never very interested in being a manufacturing power house, they simply don’t have the people for it anyways, and it’s easier to ship raw material than finished goods vast distances. Russia has the same problem in population so China is convenient for them. China would have happened even if the USA didn’t help it along in 80s-90s.
We're not the largest in absolute output, but we are literally the best manufacturer in the world, and we are the 2nd largest.
We manufacture lots of stuff, often the most state of the art and difficult things to manufacture. We're very good at it.
And we've sat around close to full employment for so long, the only way to convert more of the economy to manufacturing is to either import workers, cannibalize other sectors of the economy, or automate even more.
Yes, a larger share of the economy is services, but is there some objective optimal ratio of services to manufacturing we should be shooting for? If so, what is it? No one ever says.
Sending emails from an ergonomic chair for eight* hours a day and having to be civil to female coworkers and polite to your boss inherently degrades the male spirit in a way that permanently damaging and poisoning your body with 12-hour factory shifts with a foreman yelling at you doesn't. This is so self-evident and obvious that nobody even bothers to state it, it's just an assumed undercurrent in discussions of "bullshit jobs" etc.
The fact that leadership is focused on extracting profits out of the enterprise is also not a show of strength. It's a demonstration that they're trying to drain whatever value is left in it.
This is pretty much true of any of the American "greats:" Boeing, Intel, GE, GM, Corning. They're producing (or have recently been producing) great profits because they're eating their own seed corn. Nobody expects them to still be the businesses they currently are in another few decades.
We can make planes and engines because we kept the expertise, and we can make rockets because of herculean efforts of a few people. This simply isn't good enough, we need to make creating new physical things as easy as possible.
Massive numbers of people on production lines is not coming back. The rest of the economy is too well paid for that and automation too advanced. VW will retain car manufacturing in Europe by automating away its workforce, at the cost of high redundancies.
When people think of manufacturing they generally think of going from basic materials to something useful, rather than 'just' assembling quite advanced parts. Like if I go buy some parts and 'build' a PC that'd technically be manufacturing, and quite high value manufacturing if I was able to sell it at a good markup.
[1] - https://www.visualcapitalist.com/cp/boeing-737-global-supply...
Americans cars are falling behind, they rely heavily on American cultural exports which relies on the world not thinking Americas are twats because the voted for Trump. Germany alone is over twice the size of America in terms of car exports. Including domestic destinations then china is three times the size of America.
America planes fall out of the sky far too often as Boeing pivoted from engineering to politics
Musk does good rockets. Not America. China is catching up and lost to overtake unless starship proves itself; and if it does China will copy that too.
Chinese planes are increasing very year, comac now about 15% of traffic, and heading towards about 20% in a decades time. and Boeing is about the same size as airbus at 40% each which will shrink as china catches up, won’t surprise me in 2050 is the largest provider of passenger planes is Chinese.
This conflating US financializing manufacturing vs actual industrial capacity, i.e. the entire manufacturing by value / MVA "cope". US objectively doesn't make much relative to past and a lot of shit US makes, US doesn't actually make, others does the actual making while US financializes and capture via spreadsheet maxxing, hedonic adjustments etc, and do insert "I made this meme" on things US fundamentally did not "make"
Like US does make some legitimate frontier stuff in house - the reality is is in aggregate industrial base terms, US MAKES / FABRICATES very little vs pre industrial chain hollow out, US does import a lot of subcomponents that others make, integrates, or outsource actual MAKING abroad while capturing intangibles like IP/patent fees that get bundled up into value add stats, but when people talk about making they're talking about extraction to factory gate pipeline. Not US can sell a bunch of foreign made goods with some CONUS final assembly or IP accounting because muh smile curve that attributes disproportionate "value" to US "makers". Consider a US widget co imports $10 of components assembles and sells for $40, the statistically manufacturing value add is $30 even though US did very little making. Now add tariffs, regulatory capture etc and widget sells for $50 and that's $40 of MVA for basically doing minimal M. Said US widget factory makes 10 widgets and adds $400 to MVA. Meanwhile a PRC widget company makes 100 identical widgets at $2 each... but their MVA is only $200, but they did literally all the making. These two industrial bases are not the same. Then a few years later, US BLS/BEA applies a statistical adjustment because widget X is slightly more performant... (prevalent in tech), so that widget is now listed as $100 equivalent utility, so hey manufacturing expanded (it didn't).
SpaceX... doesn't make that much... they make a few thing that are reusable and due to techstack can lift a lot, i.e. operational improvement (not discounting how game changing resuable is). But compare to PRC, who made like 2x-3x more first stages, i.e. their output is significantly higher, which is to say SpaceX is "making" at about American scale. For reference Boeing like ~30% domestic content, US auto ~50%. Reality is the ONLY thing US is REALLY good at making is refined fossil, agri, wood products etc. Almost everything else US very good at making money, but not the stuff.
TLDR US got much better at MAKING money from other people MAKING things. The other caveat is regulatory capture = US grossly overpay for mediocre shit, a lot of stuff that makes in US are just... not good, i.e. vehicles, but people trapped paying domestic premium which inflates value add but not actual relevant when people discuss US making things. US broadly makes a lot of mediocre overpriced things while population stuck shopping in company store for large items, that looks good on value add / productivity stats... but it's not reflection of industrial capacity which is what people think about in terms of real manufacturing.
If there was no China, Globalization would spread very strongly with Bangladesh manufacturing clothes and Milan doing the fashion show. All those calculations are coming undone. So please keep this main reason front and center when we discuss this matters.
The Vespa popularized by Hollywood's Roman Holiday propped up a post-War Italy.
Ping-Pong Diplomacy of the 70s gifted China its electronics manufacturing (think: PONG) turning it from a 3rd world backwater to what it is today. The exchange was in removing the 'Gang of Four' and the drugs associated with it.
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The US was put into a privileged position as reserve currency of the WORLD in Bretton Woods. This is with the understanding/responsibility that the US would act as World Police and prop up foreign economies.
That is the system that is ending.
Even with how long Russia has been an antagonist, they are still seen as a “wayward brother.”
This is already showing not to be true with AI. Institutional knowledge is not the same as knowing how to implement low-level software details. Even today, every company does not need engineers to remember git cli syntax by memory, how to write parsers for JSON, or write the large amount of boilerplate from scratch that is at every software company. Most company's already hire engineers who have zero experience in the existing code base, yet they are productive despite this lack of institutional knowledge. For a company to maintain institutional knowledge they may only need N/K engineers.
Do not share videos with si parameters. It links together the accounts of the sender and receiver.
@mitxela: Thank to for taking a moment of your time to educate those who didn't know.
To build a $100M software company, you need 6 engineers and 6 laptops.
To build a $100M hardware company, you need 60 engineers and $100M.
Everyone decided on the most logical choice. It gets even worse if you jump into profit margins, as software is the unambiguous winner there too.
The resilience is more in ensuring archival process to ensure it's never lost forever, and a continuity plan to ensure enough (but few) people still know.
I believe this is what Socrates said (verbatim) about the invention of writing.
Besides, the dialogue is not about writing itself, but about writing down political speeches and the consequences and impact of political speeches being read rather than heard.
Also, the dialogue dates from about three thousand years after writing was invented.
Some direct quotes from the dialogue:
SOCRATES: So, it is obvious to everyone that speech writing is not shameful, not in itself anyway (258d)
SOCRATES: We are left with the question of the appropriateness and inappropriateness of writing, and how it may be executed in a worthy or an inappropriate manner. Do you agree? (274b)
And this, a very relevant insight for an age when some are convinced that text analysis should suffice for intelligent work:
SOCRATES: Then a person who thinks he has left a skill in writing behind him, and anyone who, for his part, inherits this on the assumption that something clear and certain will emerge from writing, would be full of enormous silliness, and indeed ignorant of the prophetic words of Ammon in believing that written words are anything more than a reminder to a person who already knows whatever it is that the written words may refer to.
Ironically, the sharpest criticism Socrates made of writing in the often misread Phaedrus was of misreading texts.
We have significantly degraded in our ability to memorize e.g. epic poems or preserve oral traditions since the advent of writing.
I know that sounds trite in retrospect, given the benefits, and that's half the point. But there are real tradeoffs too! For example, culture has a LOT more generation-to-generation turnover as a result of literacy being common, making it unstable.
The passage criticizing the "invention of writing" is a quote from a legend where a Pharaoh passes negative judgement to a god, by the way, who had invented writing in the story.
Ironically, the dialogue in question, Phaedrus, is about the dangers of misreading text. It discusses writing political speeches not writing per se.
In the world where we didn’t outsource Western manufacturing to China, our homes would not be overflowing with disposable junk; in the world where the Luddites won, our wardrobes would not be stuffed full of disposable clothes; perhaps in the world where Socrates won our minds would not be jammed full of nonsense.
Having something else do all of that for you is completely different and essentially tossing any gain outside of the creation of some gray slurry of an output in the trash.
Right. And the spin I would put on your point: while the U.S. could never compete with outsourced labor, institutional manufacturing expertise could (you would think) still be valuable to startups finding some kind of niche in the manufacturing space.
With all this new 3D printing infrastructure, and constantly improving robotics, maybe some manufacturing efficiencies in some spaces could emerge that compete on cost and outcompete the cost of shipping stuff across an ocean. The availability of institutional expertise could be one of the necessary ingredients for mixing and matching the way to a new efficiency.
Yeah it's bleak in terms of automating away labor, but I'd like to think robotics is the next automated frontier after LLMs and we want to get there first.
At the very latest, when "AGI robots" will be commonplace and taking on the most crucial labour on our behalf.
That's the whole point of Global managerial class even without AI. The idea is just by creating metrics one can measure how teams, groups, organization and whole industries are performing.
Before the current AI complex the previous one Agile Industry Complex One can see we starting producing 10x more code, 50x more JIRAs resolved, 100x more network bandwidth used. All these metrics tremendous gain in productivity.
Also it may not have been seen in US but in many places a change in political regime does want to burn down previously collected institutional knowledge because new dispensation have their own idea about what is knowledge, who will preserve it and how it will be preserved.
I wish more people would at least consider this idea.
It just seems to me that fundamentally this is a knowledge organization problem.
“Any idiot can run a business, but it takes an MBA to run a business that barely functions.”
Tooling is a high-skill trade. America used to be amazing at tooling. What's more, tooling isn't super cost sensitive because one tool can make thousands of parts.
When outsourcing to China began the tools would be made in the USA and shipped to China for the low-skill work. But over time those Chinese manufacturers figured out the tooling. What's more they realized that controlling the tooling would let them control the whole process.
They started doing things like, for instance, including the tooling in the price of the product so you don't even see it, if you use their tools. Just send them the cad file and they'll do the rest. So American tool making went away. But this was a conscious decision on the part of the Chinese manufacturer and an unconscious decision on the part of American importers who didn't really value their in-house expertise.
So the client who writes the ticket understand the domain, the LLM that implement it understand it too.
The dev is the only one that is clueless.
Maybe it will be no big deal, and nobody will read code anymore, but it is understandable why somebody might be concerned about it.
Without domain knowledge, the dev will miss critical simplifications. That is one way the code base accrue complexity.
The political move to put all of that death and destruction onto China, where environmental regulation is willfully ignored in the interest of economics, was a smart move on their end.
We lost so much intellectual knowledge and other "tribal" technology in these processes to this outsourcing, which is unfortunate. We're almost having to rebuild our manufacturing from first principles, which may not be a bad thing.
Avoiding environmental regulation was one reason to move factories to China, avoiding unions and high wages was another.
But the local communities weren't demanding that their factories be moved overseas so they could have a clean if impoverished towns. Your entire causality is completely backwards.
Free trade / globalism was the larger "true" move, and yes getting away from unions and high wages to maximize profits was a big part.
Corporations made a change from valuing stakeholder value (employees, communities, customers, suppliers, the nation) to pure shareholder value (profits over everything else, despite the long term result that profits by any and all means hurts all stakeholders and eventually can cannibalize the company unless you have monopolistic moats).
Was the New Yorker crowd clinking glasses together talking about how good it was to get the dirty steel mills out of the country? Sure maybe but this argument never resonated with anyone on the ground.
Were companies actively moving factories offshore so they could be dirtier because they didn't have to comply with American environmental laws? Absolutely.
Americans produce the highest technology equipment in the world. Our machining base is structurally sound, but its all making weapons, so you don't hear about it.
It's true that China benefitted immensely from outsourcing, but they took the jobs Americans didn't want. It's the same with immigration today - folks cross the Rio Grande to do chores that Americans won't, or others fly in and work for nothing in academia while they wait for their PhD.
> The problem imo is the slow deterioration of institutional knowledge that offloading the mental task of wisdom gathering to AI is causing.
Have you considered the institutional knowledge could actually be actively preserved and distributed with AI? The kind of tacit knowledge that is situated and not readily preserved in a book might be absorbed by thinking machines and proliferated to the next person who needs it. The caveats would be trade secrets, skill differentiators, that people might not be willing to discuss, and manufacturing secrets of national importance. Maybe you can think of others.
Well that's what this whole debate comes down to. And, to my mind at least, it's a rare case of an actually interesting question about AI, because like the article says, it can plausibly deteriorate exactly that kind of knowledge. But as you note, it can also maintain it.
I think there's a sense in which it might do both at the same time. AI's version of on call tacit knowledge might be something like lazy-loading just-in-time tacit knowledge, but at the cost of who knows what cognitive paths we might have by keeping that knowledge resting in-house. We would gain real efficiency but we wouldn't know we wouldn't know.
>The kind of tacit knowledge that is situated and not readily preserved in a book might be absorbed by thinking machines and proliferated to the next person who needs it. The caveats would be trade secrets, skill differentiators, that people might not be willing to discuss, and manufacturing secrets of national importance. Maybe you can think of others.
It's funny that you credit AI with this (and I don't disagree), because tacit knowledge was exactly the thing Hubert Dreyfus spent a career insisting computers would never have, and his wisdom was taught to generations of undergraduates across the country and world who treated it as received wisdom and still is regarded as such in certain academic corners.
Could you elaborate on this? It sounds like something any anthropologist would laugh at; there's always an oral culture.
When he described those limits, he always frustrated computer scientists and analytic philosophers because he spoke in a kind of informal philosophical vocabulary and didn't really formalize his ideas. So he would say computers didn't have things like "tacit knowledge" or "insight" and he railed against "symbol manipulation". Famously he declared chess would never surpass human expert play. He wrote a book called "What Computers Can't Do" and another called "What Computer's Still Can't Do".
I personally think his argument was laughably wrong if well intentioned. But some people think it was respectable. I think in the present day, he's often rehabilitated with a kind of apologetic reinterpretation, such that things like transformers, weights, vectors, etc were what he really meant all along.
I think he was not wrong that some higher layer of sophistication would prove to be necessary, but he was wrong, I think definitively, to think that "symbol manipulation" of computers was a kind of category error. Even today's best models are still running on logic gates over 1's and 0's, and it was his failure of imagination to doubt that those could be the conceptual bedrock for AI, tacit knowledge and all.
He passed away in 2017, which is too bad because I would have loved to have seen his interpretation of things like GPT-6 Astra.
> think that "symbol manipulation" of computers was a kind of category error
This is where we do drift off into the semantic bog. If there is tacit knowledge in an LLM, then where is it? It must be in the weights, and it must have somehow come from the training data. Therefore the weights represent "compressed" knowledge. Is that then not "tacit" since it's explicitly encoded?
I think what it comes down to, is trying to turn the specialness of human intelligence into undefinable magic, essentially playing god of the gaps with the concept of human insight. So by design, it has to be something that can't be amenable to any formal representation like weights.
He never made such a claim. In the introduction to the 1972 print of this book he discusses the forecasts from Turing to his time of computers' abilities to play chess and the then state of the art. He criticizes the early optimism in 1950s mentioning that in 1957 H. Simon though in 10 years computers would excel in chess.
Dreyfus goes on to discuss the history of forecasts and progress in computer chess in a nuanced and highly informative analysis.
Whatever your opinion of his work I don't think it's fair to say his arguments are "laughably wrong" at any turn. I haven't read his books in detail but from what I know he made great contributions to the dialogue about technology and I don't know any instance of his making crass predictions or anything that he wrote that could be labelled "laughable".
He thought that the complexity of chess rendered it solvable in principle but "uncomputable" in practice. You're right that he was speaking to the times he was familiar with, and what he meant by "impossible in practice" was something like letting a 1Mhz computer explore all the possible chess moves from now until the heat death of the universe. Relying on that to insist that computers defeating humans in his lifetime was consistent with what he envisioned stretches past charitability and into sophistry.
And I don't think you can extend him that charity without doing the same in the other direction, which would also collapse his basic thesis. Dreyfus was disproportionately preoccupied with retelling the failures of the 1950s over and over again using them to represent the whole of computing while the world moved on, and extending the same charitable repairs in favor of AI research make it something less easily caricatured, and still based on the same logic gates and 0s and 1s he was criticizing.
If that's not enough, Dreyfus explicitly said that what was lacking in chess programs was (1) any practical ability to do the brute forcing needed, (2) any kind of nim-style logical shortcuts around brute forcing or (3) any kind of expert level heuristics because he categorically believed those simply weren't programmable. And he believed that those exhausted the options. [1]
There's no version of this that can be correct because even if you think he's right that chess engines got better by progressing to some different conceptual paradigm, that paradigm is still embodied in same logic gates and 1's and 0's that he thought only pertained to prior paradigms he was criticizing. He was wrong to assume such things as "heuristics" were outside of that scope.
1. https://repository.essex.ac.uk/42372/1/Martin%20and%20Willia...
I do object to calling his writing "laughable".
Thank you for the article, it seems quite interesting on skimming and I will save it for later.
This is probably true.
Many artisan fabric techniques were lost when industrial looms displaced those jobs. But heavy industry enabled cloth to be manufactured at a rate that people were free to spend their time and money on other priorities - and now (centuries later) the society is in a sufficiently advanced stage of development that the old techniques are being rediscovered.
Perhaps the example underscores the importance of thoughtful preservation of insitutional knowledge.
It's also very automated so the jobs went away, but the US continues to manufactures a lot of things.
They took jobs that Americans would have wanted higher pay to do, higher benefits, higher safety standards...
You don't develop "institutional knowledge" from a few prompts. It takes years to develop them.
Bogus nonsense.
1. Manufacturing output in West/America is higher than it ever was in history.
2. What has reduced over time is the number of people employed in manufacturing, not manufacturing output. And that's an effect of automation, same as it happened over centuries in agriculture.
3. West/America did not decide anything, the realities of capitalism did.
You're either a capitalist and embrace efficiency of producing where it makes sense (for many different reasons) or you're fine with inefficient third-tier industries.
The tax payer may help a handful of sectors that would otherwise die survive if they are truly critical for national security, but just a handful, not all of them.
"Code maintainability and good architecture don’t have good measurements that we can apply"
Who has no wisdom? There are dozens of ways to measure code maintainability. Cyclomatic complexity is just one.
Nothing stops you from wiring up something like SonarQube metrics to your agentic coding workflow.
Cyclomatic complexity has been pretty solidly discredited within the maintainability research community for decades.
Sonar's cognitive complexity metric is a bit better, but here's a study that found that it still only has about a 0.5 correlation with how much difficulty programmers actually had reading code as measured by multiple methods.
They found that the most accurate way to measure code complexity that didn't involve something like an eye tracker or EEG is still basically just vibes - asking programmers if they thought it was hard to understand.
https://www.frontiersin.org/journals/neuroscience/articles/1...
Halstead Effort came in second, and scored pretty well, but here's another one where it doesn't do so well, either. And it scores the SonarQube metrics even worse, with only a 0.35 correlation: https://www.sciencedirect.com/science/article/abs/pii/S01641...
I definitely wouldn't want AI to be autonomously using that as a guide without doing some fairly serious internal A/B testing first. Kind of like for cyclomatic complexity, it's just too easy to find ways to maliciously comply. And if that's what you ask AI to do then that's likely what you're going to get.
> There are dozens of ways to measure code maintainability.
There are no good ways. I'm averse to making absolute statements, but here I'll take that chance. I worked in dev producitivy for years with people who spent decades in that domain across multiple companies with very high volumes of code production. Everybody agreed: All metrics are flawed and even a combination of metrics is insufficient.
Just to give one fundamental reason (in addition to a lot of the sibling comments): for any given metric there are an infinite set of counter-examples that don't trigger any thresholds but are clearly bad code. So these metrics typically only help in trivial cases, don't catch a majority of the cases, and so often become more of an annoyance due to low SNR. A lot of dev productivity work ends up being wiring these metrics in and then providing escape hatches when they inevitably get too noisy!
And most relevant to this discussion: these tools do not say anything about higher-level concerns like architecture, over-engineering and design, which IME is where agents tend to mess up most. I've almost never had a complaint about the code itself; the logic, naming, functions, data structures, even a lot of the testing, are all on point. It's always been the higher-level structure and design: over-engineering, duplicate classes, suboptimal abstractions, redundant operations across layers that could be solved by adding a single variable in a class, etc. etc.
I think the problem, like with code written by humans, is lack of sufficient context while doing a task leading to tunnel-vision. This is why we need to oversee and ensure things are good holistically. I suspect models are now good enough to play the role of an architect as well, though, and I've read some indications of that online... I just haven't tried giving them that much control yet.
Yes. But I think the idea is without "hand written domain driven design development" the result trends to "vibe coded by someone with no technical knowledge or inclination," as developers de-skill.
Somewhat relevant parallel..
Calculators exist, but not all is lost:
- lot of (most?) people can do basic multiplication (I’m too lazy to fetch any stats but I hope you’ll have some observations in your bubble dear reader)
- some people actually compete in mental calculations https://worldmentalcalculation.com/mental-calculations-world...
Same will be with software devs, enthusiasts will continue to exist.
So are you agreeing with me or not? Because it's not black and white. Having a few non-deskilled developers around who do the equivalent of "compete in mental calculations" is the same as having none at all.
It's sort of like the retort "AI won't take all the jobs from humans, some will be left [at the very top and very bottom]." Even if true, fat lot of good it does most people who would be unemployed in that scenario.
Also, if you get widespread deskilling, but massive increases in code production due to AI, you're probably still going to get "organizations falling into the trap" like the OP describes and the remaining skilled people getting burned out trying to hold it all together. Modern American business culture (in aggregate) is incapable of learning to not burn people out until everyone is already burned out, only then will it pay attention to the problem (and then probably forget what they learned and start repeat it in 10 years).
Somewhere in the middle of that continuum sits - "domain driven specifications, described using high level english concepts(that are well defined) from the domain , combined with a selection of a few standard architectures"
Induction fallacy at its best
There's the rub. It requires knowing about and caring about maintainability. And a lot of the people who "haven't written a line of code since 2025" don't care
I’m beginning to believe that if this was a “solvable” problem then the billions of dollars poured into coding agents would have solved it by now.
I like this framing. Humans invented software (and engineering in general) as a means to solve problems with methods that work best for us. There may be entirely different, and parallel, problem-solving methodologies outside of human best-practices.
You say that like adding "Make it maintainable." to your prompts solves the problem. But the reality is we only have weak metrics for measuring maintainability. For example, you can trivially optimize for Cyclomatic complexity by blowing away abstractions and duplicating code everywhere. That doesn't make the code better. Cyclomatic complexity is a tool that has to be applied judiciously.
That doesn't mean you can't or shouldn't use AI to generate code. But it does mean if you want your project to scale, you're still going to need a lot of developer involvement at the code level to ensure the code remains maintainable so that future developers can build on top of it. AI is not like compilers, which allow developers to build complex solutions without being proficient at the next level down (assembly).
> code maintainability and good architecture don’t have good measurements that we can apply, because it takes months, years even, to notice the effects of bad architecture or of unmaintainable code.
One way of understanding "good" here is "actionable", imho.
But that's not actually a change. Humans wouldn't magically make everything maintainable if you don't tell them to (and maybe not even if you do). You have to monitor them and train them, carefully, basically forever.
The trillion dollar question is how you do this, if your employees do not care (they are optimising for salary & time spent not code quality) and you have no way of telling apart AI slop vs. good maintainable code. (If you could you would just train the AI.)
Before AI there was at least some way to tell apart good programmers from bad, because there was some human effort involved in coding. Now with AI and slop generation there is almost now way to do this.
From what I see is it's mainly managers and higher brass who doesn't care about code quality and sustainability, and aims to drive time to market metrics down aggressively with AI.
Any employee who cares about code quality will become a poor performer with a red luddite label because they dare to change what the AI has emitted for them.
I'd love to be wrong, very wrong about this, actually.
Developers, however, are still responsible for the code! We must review the AI....all 80k lines of code it generated yesterday. If we don't then we are at fault. And we must go full throttle of course. So ....not be picky and retrograde about accepting what is generated.....
IOW we know who is going to get screwed and it isn't them.
I am equating the nerve that develops in people that get lost bikeshedding (wasting time on inconsequential parts of the problem) with fighting an llm on inconsequential implementation details.
We most certainly agree: what matters should always be the actual requirements (functional, security, performance, etc.) You can't bikeshed an important topic. Everything else is implementers decision. In my experience an experienced engineer understands the difference and trusts implementers to make the decisions that they do own.
You use the intentionally vague word "implementers" to abstract whether you're delegating to a human or to an AI. But the key point is that these are not the same thing. If I'm delegating to a person, that person is the "implementer". If I'm using an AI to generate an implementation, I am still the "implementer", it is merely a computer program working on my behalf.
Trends over time will drive more observable changes. If a whole generation of programmers picks up bad habits that their managers don't care about (think very junior), that will take some time to play out. It's like children's literacy. You don't notice overnight, but a decade of neglect and you have a reading problem in kids.
I think that's true but it's a special case. AI is here to stay and with AI coding IS faster and quality is better than ever before. Ideally you want your luddite fired along with the slop generators and keep the ones who are using AI and taking their time to deliver a maintainable code.
Is this claim based on something?
I'm not against or "for" AI (whatever that means), I try to use it as effectively I can, but for me it's not at all obvious that quality is better than ever before.
Speed I can buy, especially in new projects and utilities, but quality? At least I haven't seen this in practice, if anything I'm just seeing more code, issues, PR's and pressure ==> more slop, more bugs, less quality.
You can always say "skill issue" and "process issue", but that's partly my point here, AI doesn't magically solve this.
Speaking for myself, based on my own personal experiences, quality is by far the bigger advantage of these tools. It has never ever been easier to write automated tests and to automate tedious manual validation. I'm running my code through like 10x more paces than I ever did before, because I can just say "hey try running this in these twenty different ways" (including with browser automation, if that's relevant), without needing to either do the tedious steps to run all that or to take the time to write a script to do it, and to compile and attach the findings to the PR. This saves me hours to days of work on validation, but the reality is that I just wouldn't have spent that time in the past, I just stopped at a lower bar for quality, because I couldn't justify the ROI for spending all that time on it. But now the ROI is huge, so it's a no brainer.
If people are not taking advantage of this, then yes, that is literally a skill issue.
For some reason this reminds me A LOT of past discussions about microservices, most wonderful on paper and forever debated, but I've never seen it work out perfectly in practice, for me it's mostly been a cluster F in most companies that adopted them.
Currently I see AI similarly, in theory perfect, in practice I don't see the claimed effects. So yes, skill issue, but skills are relevant and your company probably can't hire a rockstar team (if that matters in the future).
But your comment on personal experiences was very good! Spot on, we are all biased, easy to forget. Thank you for that.
...or domain. You said "including with browser automation", so you do web or web-adjacent development.
Not all of us are doing that. What I work on doesn't have any UI or output besides a log file most of the time, but it connects to many places and does many things like an octopus, but nobody sees that, but feels that it's there because their environment keeps on working.
The octopus you just described sounds to me like an excellent example of what having the ability to more easily do tedious validation is most useful for. If you know that the "environment keeps on working", there must be some way for you to observe that fact. And if it is an octopus, it is likely difficult and/or to change the conditions and observe the correctness with respect to those changes. I find it so much easier to do this exact kind of thing now. Or, "easier" really isn't the right word. It's that the activation energy is low enough now that I'm able to do a lot of things up front that I used to rely on runtime monitoring to validate.
I guess YMMV, and it's not magic, but for me it totally changes the calculation on when it makes sense to automate something (like that chart from the old xkcd about how many times you'll do the thing and how long it takes to automate) in a way that means I'm doing a bunch of things that are useful for quality that just would never have passed the bar in the past.
I didn't. I made a guess. I might be wrong, that's OK. I love to be wrong, because I learn things by being wrong. Also no offense was intended, and I don't consider webdev inferior anything. What I tried to mean is, if AI has more training data for a domain, it does better. If you fire the same model on a niche domain, it falls flat.
> If you know that the "environment keeps on working", there must be some way for you to observe that fact.
Yes.
> And if it is an octopus, it is likely difficult and/or to change the conditions and observe the correctness with respect to those changes.
Nope. On the contrary, because there's so much innate knowledge that is required to know what to do, simulating in mind, deploying and testing on real world is much easier and faster than letting loose an ML model on it. You need real data, real data comes in slow, but you can catch problems early and easily.
Considering it's a niche area, AI also doesn't have much training on that domain, so it's doubly inapplicable for what we do.
> but for me it totally changes the calculation on when it makes sense to automate something ... (snipped for brevity)
It's great that if it works for you, but YMMV part is way more correct than people want to accept and want to learn. AI is a pneumatic hammer, but not everything is a nail which can be driven in with that.
When it works, it works. When it doesn't, well people still pretend it does or insists it shall. We must accept the limitations.
No this is what you're not getting. It is "doing the things I would do to deploy and test in the real world, but faster and in the background while I do other things", it is not "letting loose an ML model on it". This is the new capability. If you have any process like "do {action}, wait until {something}, check {something}, determine if it matches expectation", it is now possible to run that loop way more times in way more variants without either spending the time on it synchronously oneself or writing a script to do it. (If you do that specific action loop often enough, it's probably worth writing the script anyway, but that's also much quicker to do now.)
The AI doesn't need training on the domain, it just needs to be told "these are the things I would do, please do them for me and report back".
I'm sympathetic to not everything being nail-like, but I really think you're leaving a lot of chips on the table if you can't imagine any of this kind of action-check-evaluate loop you have that you could offload.
Everything is already at the background. That thing doesn’t need a CI, not the classical or AI enabled kind.
Maybe I was not clear about that part of what I do. Three minutes of something not working correctly doesn’t burn our world down.
Obviously I have no idea what your work looks like! But what I'm saying is that time savings are not just time savings. There can be a point at which the time savings bring you under an "activation energy" such that it unlocks a new capability, not just a speedup. And some of those unlocked capabilities can be directed toward improving the quality of software. And I think that's awesome and useful, is my prevailing point here. I won't claim that it will usher in an industry wide improvement in quality or anything, but for me personally, I'm making better software more quickly now, and I'm very pleased that I can do that.
Maybe it's true that lots of people aren't taking advantage of this and are shipping trash, but that's their own problem, and there have always been people who do the job poorly.
That's true, but it doesn't have to stay in this form.
> with AI coding IS faster and quality is better than ever before.
Citation needed, because the last study I read about was painting a completely different picture about code quality. Also, just because the AI pulling and remixing code from a known repository with high quality doesn't mean your code will be at the same quality automatically. Passing tests is not enough.
> Ideally you want your luddite fired along with the slop generators.
The thing is it's not possible to see who generates slop and who generates code, and if you fire the only people who knows about the codebase intimately, you'll be on a very exciting, possibly fatal ride. I don't recommend this. AI doesn't know your history and trade-offs. These guys do, and can guide you to clear.
AI can't.
Believing that AI will create bug-free code from start is believing that Rust is the silver bullet.
There are no silver bullets.
But I totally agree with you about the measurement problem. I think it's a very difficult time to be a hiring and firing manager.
This is exactly what I said in my top comment. This is a huge problem.
> Ideally you want your luddite fired...
The luddite is the person holding everything together.
Like the office maid who looks like doing nothing but keeping morale high and everybody's sanity intact.
The answer to that is "Fuck you, no. I have worth, and you will respect it". Gilded Age paternalisms are not something that needs to be brought back into vogue unchallenged, especially when the intent is to keep the rabble quiet, and the checks rolling in and up.
I labeled the person who uses AI to generate code and uses their brain and wisdom about the system to refine that code as the luddite since they will work slower when compared to other "higher performers" who don't care about the code quality.
In my framing I'm aware that the person is not a luddite per-se, but will look like it since they will be slower while trying to create better code, albeit using AI in the process as well.
Citing myself:
> Any employee who cares about code quality will become a poor performer with a red luddite label because they dare to change what the AI has emitted for them.
I don't think companies are living and dying, by how much code they can produce. Even tech companies.
AI slop can actually help with this, because it reduces the cost of replacing their enshittificated software.
Ive seen soooo many people burnt out, or "ive given years to the company and i got hit with layoffs", or "$200 software would have saved $1000000 when I brought it up to them". And companies will throw you away the MOMENT your usefulness is gone, even if just perceived. So, use them just as much as they use you.
And that idea of slacker is ALSO a way to generate more money for you, by slyly withholding or slowing work. I didnt get my paltry 3% last year. Inflation up 15% or whatever stupid number. But I can control how much work I do, so my effective wage/hour stays with inflation.
Save your caring for your personal projects, nonprofits you help at, your and family/friends labor you help with.
1. Caring about the company when you are a worker and not owner?
2. Companies will throw away/layoff people with no notice?
3. Work slowage (work-to-rule) as a counter to low/no pay raises in accordance to general inflation
4. Invest emotional and physical labor in ventures you gain completely out of
1. Caring about the company when you are a worker and not owner?
Yes and no. I don't care about the company. I do care about what I do. It's a self-respect matter. I do good work not because I'm a slave to company, but because of self respect. My deal is simple: "I'll do my best to produce the best artifact and push the company further as long as it doesn't conflict with my personal principles, you'll buy that time for that amount of money".
I have a simple, foundational rule: I'll sleep sound at night, and this rule is rooted in my ethics. So, I don't shortchange anyone, incl. my employer. If terms change between us, we will discuss, but this probability is not a reason to do shitty work (or optimize for money, or which sugarcoated absurdity others name this).
2. Companies will throw away/layoff people with no notice?
Yes, this is bad. This is life. It's not nice, fair or acceptable, but without unionization, you can't act against this. So, you either try to change this or you just accept it. Realities of work life is not a predicament to shortchange your employer again.
This is as absurd as saying "I'll die anyway, why do all these things? I can just die on-demand".
Meaningless...
3. Work slowage (work-to-rule) as a counter to low/no pay raises in accordance to general inflation
We can accept that, but you all shall really unionize. It's not scary. Try organizing. It's a force multiplier.
4. Invest emotional and physical labor in ventures you gain completely out of
Everybody should have hobbies either productive or unproductive. I can't find the question.
There are companies where you can spend years doing as you describe. More and more, though, you’re competing with people who care even though they don’t own, put in the same effort YoY, and invest in their job. Companies love these employees.
So I guess it really depends on your values and what you want out of life. If you enjoy hobbies and time outside of work, sure find a job where you can coast. Don’t get frustrated when you get laid off just find another place to work. Etc.
Plenty of people want to grow within the industry and build a career, though. And many have what we call basic self-respect and care about how they are perceived.
Go fast and break things has been a mantra for how long?
I think a lot of the laments about "good" code are really about "ownership" - and as someone who spent most of my working career in OTHER peoples code bases I have seen some things. There are a lot of you who think that your code bases are "great" when they are NOT. Personal understanding is not a good measure of quality.
The increased cadence from AI is just speed running to the legacy code base.
The answer: express the concern, and reiterate it after every issue that arises because of increased complexity. Start building a plan on how to "unravel" the mess, how to migrate things in place, how to start drawing boundaries in your systems. The system is designed to reward heroes who fix problems - you want to be super man who stops the bridge from falling apart, not the engineer who pushes the costly fixes it before it does.
Practically since eternity, but just as we learnt to manage current rate of "fast" and "breakage", somebody attached a solid booster behind us. So we're trying to understand what happened and what's happening and what will happen.
> I think a lot of the laments about "good" code are really about "ownership"
People owning what they did, have responsibility and initiative about doing better is always a good thing, yes.
> and as someone who spent most of my working career in OTHER peoples code bases I have seen some things.
I can understand that, I'm sorry you had to go through this.
> There are a lot of you who think that your code bases are "great" when they are NOT. Personal understanding is not a good measure of quality.
My codebases are as great as my knowledge. I love when someone reads my code and points where I f'ed up. I also love to show what I did has achieved something I was aiming for and discuss how to achieve it betterer.
> The system is designed to reward heroes who fix problems...
And this is the problem. Because I work silently and diligently build something looking unimpressive while working like an atomic clock without any problems.
Sounds like there is a compensation problem then.
It's not that hard: treat people with dignity and take their contributions seriously, not as a disposable meat mass. In fact, not only will this improve code quality, it's likely to improve employee retention too.
Try using an LLM to rewrite an LLM output without the slop (vs asking for no slop to begin with) or sandboxed subagents that critique a parent's draft.
There is absolutely a step-function improvement in quality but: 1) not everyone wants to explode their cost by adding extra calls 2) this can't just be "trained in" to a system as obviously they have attempted this but the technique still provides an uplift.
There were only bad ways, and the best way was to just find people who were both good programmers and cared about quality to keep an eye on the rest. Nothing much has changed in that respect.
I don't think it's that hard of a question to answer. I've noticed on my team, our thinking has shifted from how do you directly solve a problem, to how you get an agent to effectively solve the problem and not produce slop in the process.
One thing that we have done that's probably made the biggest impact is alot more upfront architecture with the knowledge that pretty soon agents will be running wild all over the code. Having worked with these agents for a while now, you get a very good sense of how they will go about solving a problem and the various footguns they will encounter along the way. Editing an AGENTS.md file or building a skill is not nearly as fun as coding by hand but it will pay dividends over and over if you do it right.
Another big thing is doing refactoring passes. Early on in our projects our agents generated ALOT of slop and we had to go back and fix alot of it. But every time we did one of these passes, a major aspect was improving agent instructions / skills / etc so it doesn't happen again. It can be a painful process at first but I found that over time, the amount of slop the agent produces goes down by orders of magnitude.
I feel like we're still very much programming, but we're now doing it at a "higher level" where we are not writing the code ourselves but instructing the agent to. And IMHO, properly instructing an agent on a production codebase is not a trivial task.
I know how to distinguish good maintainable code from garbage. I have known for quite a few years. But knowing how to train someone, or an AI? I'm a good coder, not necessarily a good teacher. And there are things about code that I _feel_, not that I can rationally explain.
You might write good, maintainable code, but they will prefer the slop generator who delivers quicker.
SonarQube
I guarantee you will pass the gate, and the code will still be dumb, except now you'll spend 10x the tokens.
That's why companies were interviewing people on tasks that had nothing to do with writing maintainable software. /s
I understand the concern and it should be addressed and researched. But, simply saying "humans were writing code themselves" doesn't provide any evidence for better quality.
I for one, have far more rigorous quality checks in my hobby projects (where AI coded), than I ever could justify when I hand-coded them.
I'm not claiming to be everybody, but surely a good portion of the population are using these technologies similarly.
I do think this gets to the heart of the matter. I think many programmers have missed the forest for the trees on why things like data structure and code complexity matter. They do matter, but they don't matter in and of themselves. They matter because they are the best techniques we have for making software that is of high quality (the software, that is, not the code) and which remains so over time, while continuing to be developed and adapted.
I strongly believe that it is now much easier to create software that is of high quality and adaptability, orthogonally to the data structure and code complexity concerns. Those concerns remain relevant, but it's a mistake to think of them as the primary thing rather than things that support the primary thing
Yet.
I'm sure in few years, as new criteria enter benchmarks, AI will be creating the clearest and smartest code people every seen, by default.
The first version was built in about two weeks of part time work. Then I started exploring. I learned relational algebra, researched almost every kind of database, reworked the internals, built a small relational algebra layer, a query planner, and an executor, covering everything from the backend storage to the query language. I learned more in those two months than in the previous 20 years.
Did I care what code the agents wrote? No. I read zero lines of generated code. What I cared about was correctness, verified through tests, and the high-level product features. For the first time in my career, I acted as a senior product manager, steering the project along the right roadmap. Without AI, I wouldn't have been able to do that.
When you have superpowers in your hands, you don't need to worry about the laundry. For the first time in my career, I can produce code in C, C++, Java, .NET, or any other language. Sometimes it takes me longer than a senior developer in that language, but does that really matter? Absolutely not. Writing documentation and code by hand in 2026 is like driving a horse and buggy. It doesn't matter how skilled you are with the reins; you'll never compete with a car. My hobby db project isnt opened source yet.
Most of proprietary software is just crap, and always has been. The agents are not producing worse code than typical, demotivated, i-dont-care-what-i-am-building-i-wont-try-using-it corporate development teams have over the years. I'd even bet that because now making changes and fixes is so much easier, the user perceived quality will trend upwards for popular stuff.
The code itself may or may not be spaghetti. Not that I care as a user. User experience and code quality had never a particularly strong correlation even before AI.
Code examples are literally bread and butter when it comes to learning.
How can you learn without looking at code? That's like saying that you can learn to be an architect without looking at drawings...
It's fairly standard for undergrad CS programs and most textbooks include at least a section on relational algebra.
They might take six courses at a time.
So you are saying that you learned more than 400 times as much content as a university course?
I'm sorry but this just sounds hilarious And unlikely to most people, and it comes off a bit crazy sounding... For example, do you actually think you could even pass a single university course exam on introductory relational algebra without AI I assistance at all?
I think what is more likely is that you feel confident trying to solve problems with AI's help.
That is not learning.
To learn something, it has to be in your brain, not the AI.
Learning is not the same as becoming capable.
There's an ocean of difference between those two
But you did not LEARN Relational Algebra. You learned OF relational algebra. And there's a pretty stark difference there.
I apologize for being blunt, and I don't mean to sound argumentative here, I'm just trying to give you an outsider's perspective:
You should not tell people you learned more in 2 months than in 20 years.
That's all I meant.
It makes you sound crazy, and its also wrong.
Maybe you built more or you feel more capable than you have felt for 20 years, but you surely did not learn more.
I don't need to hear anything from you about how the capability of the average developer has changed. I'm well aware of that already.
To be as frank as possible--you "understand" these topics in the same way someone who watches pop-science videos about astronomy "understands" astronomy. That is to say, you have a surface level understanding at best.
This is the sort of behavior people have been talking about over the last few years. People confuse what the AI "understands" with what they understand, and it makes them say delusional stuff like "I learned more in the last 2 months than in 20 years."
It is fault tolerant, distributed broker which gurantees durable queues, pub/sub and RPC all into one easy to use programming model. The application is in production and passing millions of messages every day with sub-millisecond performance, you can crash a server and replica set invokes within seconds without losing any messages. The entire project is created in less than a month with part time working, just because of AI. Its in production and already proven.
Yes, you're running it in production, but to put it in perspective: PHP 5 was also proven production software at one point, running way more production instances than you.
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I also don't care the exact cpu instructions my compiler emits. In fact I have no idea what the "stuff" it emits means (beyond the most elementary)... That doesnt stop me from creating very useful software that maybe even billions of people rely on (once you include the users of products that use our software)
This is the opposite of really striving to understand what your AI is producing.
Those with 20 years of experience are crushing it.
My worry is not about us. The worry is about the kids. I'm tech lead. I don't think the juniors at my company are learning anything from me. I'm not certain they're learning anything about _software_. Idk, I could be wrong. We barely talk because everyone has become so siloed.
In general, I believe learning usually needs to be driven by the learner.
- ignore that it is annoying to some. (To certain degree, it is unavoidable and it is ok)
- reserve 1 hour meeting every month(week?) to demo/review/present.
- get buy-in and support words from manager
But why though?
lol
and 9/10 times project open source, project bad slop
There's so much money in it right now. There's such a momentum. There are zero incentives to slow down for those that are in charge.
I've accepted that in 5-10 years, the vast majority of human devs. and engineers will not touch a single line of code. It'll be small increments, with a couple of big ones here and there.
And there will not be any triumph for those that hold steadfast to the principle of human coding. They'll be tiny boutique shops that do custom stuff, in the same way cobblers are to the mega shoe factories.
You can make a choice not to become a button pusher and still do things by hand. You dont have to fry your brain. You're falling for a massive trap to strip you of your value.
The most passionate people with the best reputation will suceed, they will continue to be in the most demand and will be the most valuable, like a swiss watchmaker.
Sorry LLMs arent replacing anyone who's got strong skills and cultivates them. You're so wrong here. These more passionate builders will however replace the lazy people who offload all their skill and brain capability to llms, and they'll use AI to help them.
Being lazy and letting llms do everything for you is not a good strategy.
Handmade watches are a tiny, niche market; they survive only because they've positioned themselves as a status symbol. Quartz watches are both cheaper and more accurate.
There is not room in the world for more handmade watchmakers, and there's not going to be room for much "artisan software" either.
I understand why people are resistant to this from an emotional perspective, but I really don't see a plateau in sight. RLVR is clearly still cooking and narrow RSI seems to be on the horizon.
But I'm also a realist. If the technology exists, it will be used to the maximum economical extent.
It's non-existent. LLMs still suck at writing code just as much as they did at the beginning of 2026, or 2025 for that matter. LLM proponents are always trying to hype everyone up on the supposed improvements, but they have never yet been real. That means they are unlikely to be real in the future either.
Economics depend on that a lot.
feels like pure copium to believe that won't change rapidly.
Why would companies employ human engineers then? What is the value addition to justify high human salaries. If AI is going to get so good (and I am not saying it won’t happen, that’s a separate debate), why can’t AI figure out the prompts itself?
Yes, AI for now has significant code quality issues, but that's mostly because it lacks agency to take care of code quality unless you explicitly tell it to. It is good at refactoring its own messes when you even vaguely ask for it. So while something like Astra still needs supervision to produce decent code, I expect that in a year or two it will be unnecessary.
With that in mind, writing bad code is bad.
Also a confirmation to people who have the same inner thoughts and are ashamed to admit in public that they think the exact same thing.
I think we need such kind of posts to combat the influx of AI news.
What you don't see from most perspectives are the silent masses who simply don't engage, don't care about the discussion, and/or are too busy doing what they enjoy.
It has been a fairly painful experience for me to shift my thinking on this, but it's a much better mindset. I still care about many of the same code quality concerns I always have, but I'm thinking a lot more about why I care than I once did.
That mindset has also deteriorated my working environment due to some coworkers buying into it.
So it's kind of nice to see some sanity checks that align with my beliefs too. I can share articles like this with my teammates. I can see that I'm not alone in thinking most LLM code is slop.
I think too junior devs NEED to see this. My team had a couple of promising juniors who are now completely brain rotted by AI and can't even write "Hello World" without consulting Claude anymore.
Anyone could say the same about any post they don't agree with, doesn't seem very helpful.
If you're just writing code to fuck around or automate a small part of your life, whatever. But if you're making a big system or wanting other people to use your product, these things about how to make good software become more relevant.
I'm going to make my own prediction: this isn't going to happen
Just look at the growing gap in traffic accident rates between human and AI driven cars. Humans are losing.
I don't disagree with you, but now what? Seriously - if we accept humans are just generally losing to AI now, what's the right thing for us to do now?
I dunno. Lobotomy? Heroic amounts of debauchery?
Any suggestions?
"We're not going to use tractors to plow the fields"
"We're not going to use trucks to deliver our produce"
"We're not going to use planes to meet with our global partners"
"We're not going to use computers to run our business"
"We're not going to open a web shop, brick and mortar forever"
"We're not going to use AI to make decisions for us"Pet peeve - luddites had never been against technology. They had been against using low paid inexperienced grunt workers to displace well paid experts.
See: https://www.smithsonianmag.com/history/what-the-luddites-rea...
As the Industrial Revolution began, workers naturally worried about being displaced by increasingly efficient machines. But the Luddites themselves “were totally fine with machines,” says Kevin Binfield, editor of the 2004 collection Writings of the Luddites. They confined their attacks to manufacturers who used machines in what they called “a fraudulent and deceitful manner” to get around standard labor practices. “They just wanted machines that made high-quality goods,” says Binfield, “and they wanted these machines to be run by workers who had gone through an apprenticeship and got paid decent wages. Those were their only concerns.”"We're not going to wear Google Glass"
"We're not going to use Blockchain for every single transaction"
"We're not going to connect every single object and device we have to IoT"
Yes, the also called lots of actual crazy people crazy. In fact the probability of actually being crazy, given people are calling you crazy, is very high.
https://archive.nytimes.com/www.nytimes.com/books/97/05/18/r...
And why are the people calling people Luddite some of the least intelligent people I meet, and seem to be complete sheep fighting some psychological war on behalf of their billionaire lords who own the machinery.
Not wanting to hand off all your labor to a machine does not make you a luddite, nor should it be acceptable to call people that because you dont know how to have a real conversation.
Thats a really bad website too btw, design and functionality. Maybe use your brain before you lose it.
For a 100x engineer with 40 yrs exp that you claim to have, you make some real basic high schooler projects lol.
And AI does it all for me while I sip coffee and play mahjong (built by AI of course)
AI can be a multiplier of both your intelligence and your stupidity, arrogance is a byproduct
AI maxxers know they're doing harm to themselves by overusing llms. The way they react so defensively when you point it out to them lets you know its real. Everyone wants to pretending like they have superpowers and are evolving into "100x" engineers, but in reality they're devolving.
I’m sure there are companies who are writing “perfectly maintainable and highly scalable code”. However, for half of my career I’ve been brought into startups to clean up the mess created by engineering teams.
While AI may create an unmaintainable mess (I’m not totally convinced), from my perspective many (not all) engineering teams have been doing that all along. #v2 #refactor
To me, AIs seem to write code with a normal (low) level of quality, but I now have so many more tools to make sure the software works nonetheless, to refactor quickly, and to encode better practices that (mostly) stick in the future.
So to me, this is all a huge net win. But I guess if I'd ever worked on a pristine perfectly engineered system, I might see this all differently.
To me, it's always been a mess, and I'm just ecstatic that I have more ways to manage the mess now.
Unless you steer and understand what an LLM will produce, you will end up with something that possible ”works” that has no future plans baked in. Suno generated music has a very unpleasant feeling of sounding like competent music with nothing to say.
I’d say that vibecoded software is similar. My speculation is that current breed of LLMs do not have an I, and I really don’t exactly knows what goes on in those vast arrays of numbers. There’s something there perhaps, but no person.
Still even in the short term someone wants to run a company that expects responsibility of its organisation, how are you going to exact that responsibility if no one actually understands how the thing the organisation makes works.
Maybe a simple crud system can be made fast and loose. But a bank settlement? A pacemaker? Deletion of sensitive data?
I know some companies are betting on that the agent can fix what the agent breaks. It may be true, but up until now everytime I try to relax on strict steering of an agent it tends to go badly rather fast.
Again I don’t know, but I think as long as we don’t invent synthetic persons with their own ideas on what they want to do, which btw opens a massive can of worms, the current situation will persist. However clever the current breeds of systems are.
I do want to state that a find the current trajectory fascinating. I use LLMs daily, it expands the number solutions I can explore. But in order to make something I feel is mine. There’s a choice and the buck stops with me.
So, I think if you know just a little bit of film making and writing and LLMs you know you could not prompt any of them into existence with just saying ”write lord of the rings”
But if you have an idea for a creative project, using an LLM to explore ideas can definitely be a fruitful endeavour.
The gods don’t give gifts by ghost goblins cannot be dismissed as slop. I’ve personally used LLM to explore large sets of photographs to use as a backdrop for a DJ set, essentially to have the set tell a story.
So, as most things, it’s complicated. But at the same time I’m curious what will happen next
Oh boy do I have some news for you about legacy codebases.
I’m not even sure that his axiom is true. A project started with a 2024/5 model can subsequently be worked on by more capable models (who don’t, unlike some humans, have a deep aversion to paying down technical debt). I’ve seldom seen a human written legacy application spontaneously acquire a better engineering team every six months.