Since when does this have anything to do with AI? Commercial/enterprise software has always been this way. If it's not going to cost the company in some measurable way issues can get ignored for years. This kind of stuff was occurring before the internet exists. It boomed with the massive growth of personal computers. It continues to today.
GenAI has almost nothing to do with it.
All software is this way. The only way something gets fixed is if someone decides it's a priority to fix it over all the other things they could be doing. Plenty of open source project have tons of issues. In both commercial and open source software they don't get fixed because the stack of things to do is larger than the amount of time there is to do them.
Things that are easy, fun, or "cool" are done before other things no matter what kind of software it is.
FaceID has proven pretty popular tools.
This is a bit like the question "what if we spent our time developing technology to help people rather than developing weapons for war?"
The answer is that, the only reason you were able to get so many people working on the same thing at once, was because of the pressing need at hand (that "need" could be real or merely perceived). Without that, everyone would have their own various ideas about what projects are the best use of their time, and would be progressing in much smaller steps in a bunch of different directions.
To put it another way - instead of building the Great Pyramids, those thousands of workers (likely slaves) could have all individually spent that time building homes for their families. But, those homes wouldn't still be around and remembered millenia later.
They would have been better off. Those pyramids are epitomes of white elephants.
This was not the original intent of the construction though.
Yes, but they'd have homes. Who's to say if a massive monument is better than ten thousand happy families?
It's not. The pyramids have never been of any use to anyone (except as a tourist attraction).
I'm referring merely to the magnitude of the project, not to whether it was good for mankind.
Like, surfacing APIs, fostering interoperability... I don't want an AI agent, but I might be interested in an agent operating with fixed rules, and with a limited set of capabilities.
Instead we're trying to train systems to move a mouse in a browser and praying it doesn't accidentally send 60 pairs of shoes to a random address in Topeka.
We don't need to figure out the one true perfect design for standardized APIs for a given domain any more.
Instead, we need to build APIs with just enough documentation (and/or one or two illustrative examples) that an LLM can help spit out the glue code needed to hook them together.
I think about code generation in this space a lot because I’ve been writing Gleam. The LSP code actions are incredible. There’s no “oh sorry I meant to do it the other way” you get with LLMs because everything is strongly typed. What if we spent 100billion on a programming language?
We’ve now spent many hundreds of billions on tools which are powerful but we’ve also chosen to ignore many other ways to spend that money.
Time of executing bytecode << REST APIs << launching a full JVM for each file you want to compile << launching an LLM to call an API (each << is above x10).
> ... we need to build APIs with just enough documentation (and/or one or two illustrative examples) that an LLM can help spit out the glue code needed to hook them together.
If a developer relies on client code generated by an LLM to use an API, how would they know if what was generated is a proper use of said API? Also, what about when lesser used API functionality should be used instead of more often used ones for a given use-case?
If the answer is "unit/integration tests certify the production code", then how would those be made if the developer is reliant upon LLM for code generation? By having an LLM generate the test suite?
And if the answer is "developers need to write tests themselves to verify the LLM generated code", then that implies the developer understands what correct and incorrect API usage is beforehand.
Which begs the question; why bother using an LLM to "spit out the glue code" other than as a way to save some keystrokes which have to be understood anyway?
I get that in the webdev space, that is true to a much larger degree than has been true in the past. But it's still not really the central problem there, and is almost peripheral when it comes to desktop/native/embedded.
Mix of open platforms facing immense abuse from bad actors, and companies realising their platform has more value closed. Reddit for example doesn't want you scraping their site to train AIs when they could sell you that data. And they certainly don't want bots spamming up the platform when they could sell you ad space.
LLMs are 10x better than the existing state of the art (scraping with hardcodes selectors). LLMs making voice calls are at least that compared to the existing state of the art (humans sitting on hold.)
The beauty of LLMs is that they can (can! not perfectly!) turn something without an API into one.
I’m 100% with you that an API would be better. But they’re not going to make one.
Like, we already had a perfectly reasonable decentralized protocol with the internet itself. But ultimately businesses with a profit motive made it such that the internet became a handful of giant silos, none of which play nice with each other.
Every VC pitch is about some ground-breaking tech or unassailable moat that will be built around a massive SAM; in reality early traction is all about solving that annoying and stupid problem your customers hate doing but that you can do for them. The disconnect between the extraordinary pitch and the mundane shipped solution is the core of so much business.
That same disconnect also means that a lot of real and good problems will be solved with money that was meant for AGI but ends up developing other, good technology.
My biggest fear is that we are not investing in the basic, atoms-based tech that we need in the US to not be left behind in the cheap energy future: batteries, solar, and wind is being gutted right now due to chaotic government behavior, the actions of madmen that are incapable of understanding the economy today, much less where tech will take it in 5-10 years. We are also underinvesting in basics like housing, or construction tech. Hopefully some of the AI money goes to fixing those gaping holes in the country's capital allocation.
The elephant in the room is that capital would likely be better directed if it was less concentrated.
A surface-to-air missile?
As funny as that would be, maybe you should define your terms before you try to use them.
> TAM or Total Available Market is the total market demand for a product or service. SAM or Serviceable Available Market is the segment of the TAM targeted by your products and services which is within your geographical reach. SOM or Serviceable Obtainable Market is the portion of SAM that you can capture.
I think we'd still be talking about Web 3.0 DeFi.
I think IoT was more than just hype.
Investing 100 years of open-source Blender does not give them any fraction of monopoly.
Even if scientists present 100's of proposals for computation (optical, semiconductor,...) they will specifically invest in technologies that are hard to decentralize: growing monocrystalline ingots, reliant on dangerous chemicals, ... if there is no money in easily decentralizable processor manufacture, then it could easily be duplicated then proposals to pursue it would basically be equivalent to begging investors to become philantropists. Quite a naive position.
It's in the interest of the group to have quality software, manufacturing technologies, ... so the onus is on representatives of the group of taxpayers to invest in areas investors would prefer to see no investment in (even if someone else invests it). Perhaps those "representatives" are inept or malicious or both.
There is real value being created by creating interactive summaries of the human corpus. While it is taking time to unlock the value, it will definitely come.
The amount of money that's been spent on AI related investments over the past 2-5 years really has been astonishing - like single digit percentage points of GDP astonishing.
I think it's clear to at there are productivity boosts to be had from applying this technology to fields like programming. If you completely disagree with that statement I have a hunch that nothing could convince you otherwise at this point.
But at what point could those productivity boosts offset the overall spend? (If we assume we don't get to some weird AGI that upturns all forms of economics.)
Two points of comparison. Open source has been credibly estimated to have provided over 8 trillion dollars of value to the global economy over the past few decades: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4693148 - could AI-assisted programming provide a similar boost over the next decade or so?
The buildout of railways in the 1800s took resources comparable to the AI buildout of the past few years and lost a lot of investors a lot of money, but are regarded as a huge economic boost despite those losses.
The countries adopting these the most are declining economies. It's places that are looking for something to do after there's no more oil left to drill up and export.
You know where fossil fuel use is booming? Emerging (e.g., growing) economies. Future scarcity of such resources will only make them more valuable and more profitable.
Yes, this is a dim view on the world, but until those alternatives are fundamentally more attractive than petrochemicals, these efforts will always be charity/subsidy.
If you're expecting that to be the area of strong and safe returns on investment, I've got some dodgy property to sell you.
I look around and the only people that are shilling for AI seem to be selling it. There are those that are also in a bubble and that's all they hear day in and out. We keep hearing how far the 'intelligence' of these models has come (models aren't intelligent). There are some low hanging fruit edge cases, but just again today I spent an extra hour thinking I could shortcut a PoC by having LLMs bang out the framework. I leveraged all the latest versions of Opus, Kimi, GLM and Grok. For a very specific ask (happened to be building a quick testing setup for PaddleOCR) none of them got it right. Even when asking for very specific aspects of the solution I had in mind Opus was off the rails and "optimizing" within a turn or two.
I probably ended up using about 20% of the structure it gave me - but I could have easily gone back to another project that I've done where that framework actually had more thought put into it.
I really wish the state of the art was better. I don't use LLMs for searching much as I believe it's a waste of resources. But the polarization from the spin pieces by C-levels on top of the poor performance by general models for very specific asks looks nothing like the age of rail.
Do I believe that there are good use cases for small targeted models built on rich training data? I do. But that's not the look and feel from most of what we're seeing out there today. The bulk of it is prompt engineering on top of general models. And the AI slop from the frontier players is so recognizable and overused now that I can't believe anyone still isn't looking at any of this and immediately second guessing the validity. And these are not hallucinations we're seeing because these LLMs are not intelligent. They lack cognition - they are not truly thinking or reasoning.
Again - if LLMs were capable of mass replacement of workers today OpenAI wouldn't be selling anyone a $20/month subscription, or even a $200 one. They'd be selling directly to those C-levels the utopia of white collar replacements that doesn't exist today.
Humans have fashions. If something is considered cool, many people start doing that thing, because it automatically gives them a bit of appreciation from most other people. It is often rational to follow a fashion and reap the social benefits it brings.
People are bad at estimating probabilities. They heavily discount the future, and want everything now, hence FOMO. At the same time, they tend to believe in glowing future prospects uncritically, because it helps build social cohesion and power structures.
This is why fads periodically flush all over our industry, and our society, and the whole civilization. And again, it becomes rational to follow the trend and ride the wave. Say the magic word (OOP, XML, Agile, Social, Mobile, Cloud, SaaS, ML, more to come), and it becomes easier to get a job, press coverage, conference invites, investments.
Then the bubble deflates, the useful parts remain (often quite a bit), the fascination, hype, attention, and FOMO find a new worthy object.
So companies add "AI features" partly because it's cool (news coverage, promotions), partly because of the FOMO (uncertainty is high, but what if we'd be missing a billion-dollar opportunity?), partly because of social cohesion (following fashion is natural, being a contrarian may be respectable, but looking ignorant is unacceptable). It's not about carefully calculated material returns on a a carefully measured investment. It may look inane, but it's not always stupidity, much like sacrificing some far-future perspectives in exchange of stock growing this quarter is not about stupidity.
It feels like more and more products are focused on looking impressive, when all I really want is for the everyday features to just work well.
They've never had to generate a real return, create a product of real value, etc. This wave-of/gamble-on AI slop just shows that they don't even know what value looks like. We've operated for ~40 years on a promise of...something.
So you want an open source project to really succeed? It's not money, but real passion for the work.
Write better documentation (with realistic examples!) and fix the critical bugs users have been screaming about since over a decade ago.
Sure fine pay a few people real wages to work on it full time, but that level of funding has to deliver something more than barely documented functionality.
Yeah, nah... passion only sustains a person for 3 days max before they expire.
My theory is that open source boomed in the last few decades because developers had enough income and free time from their day jobs to moonlight as contributors. With the gravy train ending, I suspect open source will suffer greatly. Maybe LLMs can cover what was lost, or maybe corporations will pay their engineers to contribute directly (even more so than what they do now), but there will definitely be some losses here.
This would be very efficient in avoiding duplication, the entire industry would probably only need a few thousand developers. It would also save material resources and energy. But I think that even if the software these companies produced was entirely reliable and bug-free it it would still be massively outcompeted by the flashy trend-chasing free-market companies which produce a ton of duplicated outputs (Monday.com, Trello, Notion, Asana, Basecamp - all these do basically the same thing).
It's the same with AI, or any other trend like tablets, the internet, smartphones - people wanted these and companies put their money into jumping aboard. If ChatGPT really was entirely useless and had <10,000 users then it would be business as usual - but execs can see how massive the demand is. Of course plenty are going to mess it up and probably go broke, but sometimes jumping on trends is the right move if you want a sustainable business in the future. Sears and Blockbuster could've perfected their traditional business models and customer experience without getting on the internet, and they would have still gone broke as customers moved there.
There is a lot of stinky garbage in AI, but at least you can rescue some value from it, in fact it could be most of the activity out there, but you only notice what stinks.
It's damn hard work to dig in, uncover what's wrong, and fix something broken - especially if someone's workflow depends on the breakage.
Flashy AI features get attention and even if they piss you off, they make you believe the thing is fresh. Sorry but you're human.
I understand organizationally how this happens, and the incentives that build such a monstrosity but it’s still objectively a shame.
The amount of interest to explore this opportunity is worth it. The bubble is worth it. I don't think it's lost years, and even if it is, the technology is compelling enough to make the gamble worth it.
The fatigue of reading the same shit over and over again makes people forget that it's only a couple years. It also makes people forget how ground breaking and paradigm shifting this technology was. People are complaining about how stupid LLMs are when possibly just 5 years back no one could even predict that that such levels of intelligence in machinese was even possible.
Asking Gemini _is_ just much better at finding you the answers you need, _and_ providing links for you to verify that information.
It will be a sad day when they start injecting ads, I really hope the foss alternatives catch up.
So cheap gaming hardware in the future (similar to when telecoms over invested in transcontinental undersea fiber-optic cables)? What's the hangover gonna look like after this? What's the next grift?
We are in the infancy of LLM technology.
Well maybe for you and not the millions of people that use this technology daily.
It would be philanthropy (not-for-profit), but i imagine if you get a few big names behind it (some around here, some not), you could probably have a fair group of developers, translators, UI experts, API experts, hardware bug experts, etc. Like a Think Tank that outputs bugfixes and incremental improvements to software. The sort that reduce electricity usage - you could get some government funding out of that, i bet. How much did Britain pay for those "delete old emails to save water" marketeering?
I guess the downside is meta, google, microsoft, et al will benefit the most, but whatever.
Also someone the other day mentioned something like this, where users could subscribe for $50 to pursue legal avenues - sorry i don't have the link, but:
> What we actually need is a Consumer Protection Alliance that is made by and funded by people who want protection from this and are willing to pay for the lawyers needed to run all of the cases and bring these cases before a judge over and over and over again until they win.
> This would mean people like you and me and a million others of us paying $20-$50/month out of pocket to hire people to sue companies that do this [...]
1. Unwanted but coercive upgrades.
2. Delivering genuinely upgraded complexity and incoherence, under the cloak of new feature theatre.
3. In a context where customers just wish they could keep using what they have already paid for. Without paying again.
4. But they have to, because of artificially introduced cross-user new-version incompatibilities and strategically scheduled bit rot.
5. All so a company can keep extracting money from people it is no longer actually serving.
This is very similar to enshittification. Customers invest (time, money, data), in something they love. Their common investment has been transmogrified into a dairy, er, prison. And now they are all milked by all the dark patterns and milk pumps their supplier/master can insert between the users and water, users and grass, users and the midden, er, I mean into every remaining useful part of the service. While they shamelessly and unconvincingly claim to still be their hope, light and beloved benefactor.
Encrapification? No...
Fucked-up-grades? Choose which ever silla-bic em-fassis you prefer. (Sorry for the saucy stout boldness, to you of youth and gentle heart. But the point of crass terms is to not let targets off the verbial hook, with a weak or cute euphemism.)
Quickbooks, anyone? The list is long.
I don’t feel like this article is trying to start a conversation, it wants to end the conversation so we can have dessert (aka, catastrophizing about the outcome of the thing “we know” is bad).
Yes! That’s correct!
Perhaps not ironically, the careless distribution of incorrect information, combined with a dismissal of human endeavor, is such a perfect encapsulation of why so many people absolutely despise everything surrounding LLM hype.
The implied answer to this question really just misunderstands the tradeoffs of the world. We had plenty of money and effort going into our technology before AI, and we got... B2B SaaS, mostly.
I don't disagree that the world would be better off if all of the money going into so many things (SaaS, crypto, social media, AI, etc.) was better allocated to things that made the world better, but in order for that to happen, we would have to be in a very different system of resource allocation than capitalism. The issue there is that capitalism has been absolutely core to the many, many advances in technology that have been hugely beneficial to society, and you if you want to allocate resources differently than the way capitalism does, you lose all of those benefits and probably end up worse off as a result (see the many failures of communism).
> So I ask: Why is adding AI the priority here? What could have been if the investment went into making these apps better?
> I’m not naive. What motivates people to include AI everywhere is the promise of profit. What motivates most AI startups or initiatives is just that. A promise.
I would honestly call this more arrogant than naive. Doesn't sound like OP has worked at any of the companies that make these apps, but he feels comfortable coming in here and presuming to know why they haven't spent their resources working on the things he thinks are most important.
He's saying that they're not fixing issues with core functionality but instead implementing AI because they want to make profit, but generally the sorts of very severe issues with core functionality that he's describing are pretty damaging to the revenue prospects of a company. I don't know if those issues are much less severe than he's describing or if there's something else going on with prioritization. I don't know if the whole AI implementation was competitive with fixing those - maybe it was just an intern given a project, and that's why it sucks.
I have no idea why they've prioritized the things they have, and neither does the author. But just deciding that they're not fixing the right things because they implemented an AI feature that he doesn't like is not a particularly valid leap of logic.
> Tech executives are robbing every investor blind.
They are not. Again, guy with a blog here is deciding that he knows more than the investors about the things they're investing in. Come on. The investors want AI! Whether that's right or wrong, it's ridiculous to suggest they're being robbed blind.
> Unfortunately, people making decisions (if there are any) only chase ghosts and short term profits. They don’t think that they are crippling their companies and dooming their long term profitability.
If there are any? Again, come on. And chasing short term profits? That is obviously and demonstrably incorrect - in the short term, Meta, Anthropic, OpenAI and everybody else is losing money on AI. In the long term, I'm going to trust that Mark Zuckerberg and Sam Altman, whether you like them or hate them, have a whole lot better idea of whether or not they're going to be profitable in the long term than the author.
This reads like somebody who's mad that the things he wants to be funded aren't being funded and is blaming it on the big technology of the day then trying to back into a justification for that blame.
