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class3shock 3 hours ago [-]
"So why isn't our software 10x better?"
The answer has nothing to do with rationing. When all software is designed to grab the most attention, serve the most adds, collect the most data, deliver the maximum amount of the cheapest content and generally be as exploitive of its users as possible, no amount of rationing or not rationing will effect it.
McMaster Carr, a company worth a little over a billion, sells metal, bolts, casters, and many other industrial bits and bobs. Their website is infinitely better than Amazons. Why? It has nothing to do with rationing and everything to do with trying to actual deliver a good customer experience.
marcus_holmes 31 minutes ago [-]
I'm even more cynical; most software from large organisations is bad because it was never designed for the user, it was designed to make the product manager's career advance.
Two factors:
- middle management do not have contact with either customers or shareholders. They report to managers, and only have managers reporting to them. They're insulated from the actual consequences of their decisions.
- the only way to get recognised for promotion is to create change. Any change. Doesn't matter if it's bad (blame can be shifted), it just needs to be noticed.
If a feature of the product hasn't changed for a long time (because it works for the user), it becomes more of a target for changing. If it changed recently, it creates less impact if it changes again, so isn't a good target. So the product becomes worse over time, not better.
xboxnolifes 2 hours ago [-]
Ironically, so many modern tech companies are arguably less incentivized to provide a good technology experience than non-tech companies, since the tech company's value/revenue primarily comes from directly exploiting their users for their data and/or attention. Improving the user experience frequently directly reduces their revenue. No amount of free LLM usage can allow a video serving company to both serve ad-free videos and make money from ads in their videos.
Though, maybe the irony only exists because we use the broad term tech company instead of the more generally accurate terms data harvesting company and attention consuming company.
NegativeK 28 minutes ago [-]
Mcmaster also isn't (somewhat famously) competing on price. They won't rip you off, but you can't sort by price. There are no sales. With limited exception, they won't list brands on the site. (But if you contact them, they'll gladly tell you who so you can buy direct.)
Their target market is people who need to fix something quickly or get some industrial supply item tomorrow so work isn't stopped.
Massive corporations save money via the end user by taking a chainsaw to customer service. Large tech companies (and many other industries) will just quietly fuck the regular customer over because they can't profitably do customer service at scale. Even Amazon's effectively no questions asked return policy is a band-aid over the forgeries and trash that fill their hyperscale market.
If you have a problem with an item that Mcmaster sends you, they're going to go have a stern talk with the manufacturer. They sell reliability, not race to the bottom capitalism.
(I've also heard that they really take care of their employees. People in jobs that are automated away are always retrained; they'll pay for college with no qualifications, because education is good even if they don't directly benefit, etc.)
kibwen 1 hours ago [-]
Crucially, McMaster-Carr is a private company, so their customers are the people giving them money for goods and services. As a public company, Amazon's customers are the gamblers on the stock market.
bemtevi 4 hours ago [-]
I don't think any of this makes much sense. The problem that this article ignores is that people build different things with AI, not the same things but faster. They build more superficial things, more throwaway things, things that are tailor-made for chasing the buck, and it just doesn't feel genuine or interesting.
It was much the same with search. Google search allowed us to find things faster at first...but over time websites responded to that and started doing SEO crap. Now almost every search results in a billion websites that say the same thing, but shallowly. Yes you can still find some gems but was it worth it?
AI makes things faster, but now it means we can target superificial needs very quickly...and so a new breed of crap software will be built with it. As long as there's a dollar sign at the end of the road.
tptacek 2 hours ago [-]
I don't understand this argument. Why do you think everyone is "chasing the buck"? That describes 0% of the work I've done over the last 6 months. There's no "bucks" in the music players, macro trackers, and source code review tools I've built.
doginasuit 4 hours ago [-]
AI makes things faster, and it pushes the envelope on what can be built. Like a telescope lets the human eye see further into space, an LLM helps track more ideas and structures within working memory. Yes, people will use this new potential to build slop. And other people will use it to build what has never been built before. The only question is which one we will prefer.
aselimov3 4 hours ago [-]
By definition an LLM is unable to build what has never been built before because you are going outside the realm of pretraining/RL. You might be able to combine components that already exist in a unique way, but even then you will be fighting against the mediocre making effect of LLMs
outworlder 4 hours ago [-]
That understates what LLMs are capable of doing. They can and do build things outside of their training dataset. As long as you can get there by following existing concepts, they can do it.
fwlr 2 hours ago [-]
Perhaps outside their training set in a strict sense of “this thing itself was not specifically in the training set” (although only perhaps - users often iteratively coax frontier models into achieving things just beyond their envelope, and those achievements could well be folded into the next model’s training set without you or I being aware of that). But even then, it is not directionally outside the training set: it steps outside not in the direction of the unknown, but in the direction of another part of its training set. An evocative metaphor would be dashing from cover to cover in the rain, because it lacks the umbrella or raincoat or simply the fortitude to tolerate getting wet that it would need to reach an uncovered destination.
kasey_junk 2 hours ago [-]
That’s a hard position to defend in the face of navier stokes
jimbokun 11 minutes ago [-]
My theory is that most people have more software in their lives already than they want or need.
stillpointlab 4 hours ago [-]
I am not sure this is default a good thing.
I recall something I once heard about product marketing. The experiment was a product like jam, and they had 3 flavour options available in one case and 12 options available in another. It was a test on how hard it was to make a decision as well as how confident the person was they made the right decision later on.
One of Sartre's points on existentialism was that radical freedom leads to anxiety.
In some sense, AI is removing scarcity. It isn't clear to me that this is the same kind of thing as going from starvation to surplus. It is more like the jam case where we aren't increasing abundance in a total sense, we increasing the options within the existing surplus.
cpgxiii 14 minutes ago [-]
I don't think the last decade of tech (particularly SV) hiring can in any way be defined as "rationing". If anything, we have seen large software companies grossly over-hire for all of the expected dumb reasons (mostly internal politics) and find themselves in the very predictable situation that more people != more productivity.
jimbokun 15 minutes ago [-]
> Companies like Rippling and PostHog were the trailblazers, bundling several related products long before agents.
The trailblazer was Microsoft Office.
Gigachad 2 hours ago [-]
At least what I’m seeing at work is that AI tools have generated thousands of security reports after scanning all of the old code. So our whole team has been bogged down for months now removing eval(), updating ancient packages, updating docker images that haven’t been rebuilt in years, etc. It shows no improvement to the end user but since all of these vulnerabilities have suddenly become very visible we have to action them all right now.
DenisM 58 minutes ago [-]
I imagine this new awareness could create a lot of new jobs, given how much software out there should be in the same state.
I’m not sure if the job title tho. Software restoration?
v64 3 hours ago [-]
Those who haven't read Mythical Man Month are doomed to repeat it. I don't recall anywhere in the book where it states that the problems described in its essays don't apply to you if you're working with dozens of the most intelligent engineers in the world who can produce code in hours instead of days.
I was discussing this with someone else and used the analogy of nuclear energy. Although nuclear plants are highly complex and use forces that have only been understood for the last hundred years, at the end of the day it's still boiling water to create steam to spin a turbine, and nothing about nuclear innovation can engineer away the laws of thermodynamics.
Agents have and will continue to drastically change how we develop code, but it's my belief that drastically changing our software practices to speed up development just means we're going to hit engineering limits faster, rather than being able to overcome those limits entirely.
elcritch 2 hours ago [-]
I was telling a friend something similar last week.
Unless, or perhaps until, we figure out how to use LLMs effectively at the organization level we're still going to be limited to the "slime mold" level of organizational intelligence.
Will that happen? Most likely, but will it take a generation or ten? I suspect it'll be far harder for society to adapt at that level. We've had Mythical Man Month for decades yet software managers continue repeating the same mistakes.
Because of that the idea of OAI and Anthropic just moving to in-house product development makes sense.
marcus_holmes 30 minutes ago [-]
I'm more interested in what the world looks like when everyone has their own personalised software.
jimbokun 10 minutes ago [-]
Vast majority of people have absolutely no interest in this.
xivzgrev 4 hours ago [-]
People respond to incentives: Why do a hard thing when I can do an easy thing?
During the hype cycle, making "x for y" is easiest to get funded and exit.
Once the hype crashes and we're in the trough of disillusionment, then people are bearish. So founders either move to the next hype cycle or figure out a more durable business from the ashes
beloch 42 minutes ago [-]
I think we're about to see a shift in terms of what is a viable software product and what isn't.
In the past, if you wanted to do a task, you had to choose from one of a few (or maybe just one) incumbents and make their tool fit your task. Failing that, build your own tool, but that was expensive. If you did build your own, the tool might have value and could potentially be sold to others.
Going forward, building your own will be much easier and cheaper. Even if the incumbent tools suit the task well, you might choose to build your own for a variety of other reasons. e.g. If the incumbent raises prices or chooses to enshitify. What you will probably not do is adopt some third party's vibe-coded clone of an incumbent's tool, because that won't be customized to your needs and it won't have the track record of the incumbent. You can't trust it or control it so it will be better to build your own.
If an incumbent exists and you can now vibe-code a clone, you probably can't sell that clone "as is" and expect to be successful. You might be able to sell your services to customize and refine tools for small businesses that can't really afford to hire a solitary soul to build their own solution, but don't expect that niche to persist long-term.
TwelveEyes 4 hours ago [-]
To be honest I don’t believe this is the case. Admittedly, I don’t have any experience with the best of the best models, only up to clause Opus. AI accelerates coding for myself a hell of a lot, but frankly the amount of time I spend coding complex programs feels about the same. I think there’s still a bottleneck, and it has been reiterated here before: judgement, testing, and understanding. If AI really accelerated coding, no amount of cultural inertia would be able to hold it back. It’s just not.
Grombobulous 4 hours ago [-]
I was about to say something similar. The article’s premise is solid as long as we accept the assumption that AI is actually fundamentally changing the velocity and methodology of coding.
To me, so far it seems like it is and it isn’t simultaneously. Some things are going at insane paces. I see things like reverse engineering and emulation breaking barriers. But we don’t see things like the quality and complexity of highly architected systems getting much better, maybe even getting worse as management pushes velocity onto employees.
I think what this shows is that AI is great at tedium and brute forcing, but it’s not actually good at making substantially better software with less of that “rationed” expertise. At least, not yet.
ares623 3 hours ago [-]
All this talk about removing bottlenecks at all costs, and we haven't even considered the final boss of bottlenecks: the end users themselves. All the gains will be a waste if the end users have a choice. And as "The Goal" has taught us, we must remove the further-most bottlenecks first. Hence, we must remove all choice and power from end-users first.
Tell me I'm wrong.
4 hours ago [-]
01284a7e 4 hours ago [-]
"Build me a high speed railway. Make no mistakes."
Now that SaaS CRUD apps can be built in 10 minutes, we can maybe do something more worthwhile, no? So much wasted effort has gone into this field already, in my opinion.
latentmirror 2 hours ago [-]
hehe...Before Claude(BC) and After Claude(AC).
In Claude we trust.
PaulHoule 5 hours ago [-]
... by improving model efficiency so AI tokens are "too cheap to meter" or alternately not that much more expensive to operate than conventional sorts of software?
It's a different view of scaling. I mean a lot of people have this absurd idea of "super intelligence" which is like the cover of this book
i mean what does "intelligence quotient 10,000" even mean? On the other hand it's very easy to imagine a world where we have systems like the best ones we have now but we can really afford to use them.
cyberax 4 hours ago [-]
> i mean what does "intelligence quotient 10,000" even mean?
IQ tests measure, essentially, how fast you can do shallow pattern recognition.
And AI can do these kinds of tests easily.
Dylan16807 3 hours ago [-]
There are time limits but to say they're primarily measuring speed sounds wrong.
tonymet 4 hours ago [-]
Before we were spending 10:1 on features:quality . I worry that ratio will remain, but at 100x or 1000x the velocity.
There's no reason to think that companies are suddenly going to be spending 1:1 features : quality because both are faster / cheaper .
So expect the low quality to continue, and naturally get worse, because complexity is going up 1000x
ares623 4 hours ago [-]
The free holiday will last forever. Make your plans accordingly.
The answer has nothing to do with rationing. When all software is designed to grab the most attention, serve the most adds, collect the most data, deliver the maximum amount of the cheapest content and generally be as exploitive of its users as possible, no amount of rationing or not rationing will effect it.
McMaster Carr, a company worth a little over a billion, sells metal, bolts, casters, and many other industrial bits and bobs. Their website is infinitely better than Amazons. Why? It has nothing to do with rationing and everything to do with trying to actual deliver a good customer experience.
Two factors:
- middle management do not have contact with either customers or shareholders. They report to managers, and only have managers reporting to them. They're insulated from the actual consequences of their decisions.
- the only way to get recognised for promotion is to create change. Any change. Doesn't matter if it's bad (blame can be shifted), it just needs to be noticed.
If a feature of the product hasn't changed for a long time (because it works for the user), it becomes more of a target for changing. If it changed recently, it creates less impact if it changes again, so isn't a good target. So the product becomes worse over time, not better.
Though, maybe the irony only exists because we use the broad term tech company instead of the more generally accurate terms data harvesting company and attention consuming company.
Their target market is people who need to fix something quickly or get some industrial supply item tomorrow so work isn't stopped.
Massive corporations save money via the end user by taking a chainsaw to customer service. Large tech companies (and many other industries) will just quietly fuck the regular customer over because they can't profitably do customer service at scale. Even Amazon's effectively no questions asked return policy is a band-aid over the forgeries and trash that fill their hyperscale market.
If you have a problem with an item that Mcmaster sends you, they're going to go have a stern talk with the manufacturer. They sell reliability, not race to the bottom capitalism.
(I've also heard that they really take care of their employees. People in jobs that are automated away are always retrained; they'll pay for college with no qualifications, because education is good even if they don't directly benefit, etc.)
It was much the same with search. Google search allowed us to find things faster at first...but over time websites responded to that and started doing SEO crap. Now almost every search results in a billion websites that say the same thing, but shallowly. Yes you can still find some gems but was it worth it?
AI makes things faster, but now it means we can target superificial needs very quickly...and so a new breed of crap software will be built with it. As long as there's a dollar sign at the end of the road.
I recall something I once heard about product marketing. The experiment was a product like jam, and they had 3 flavour options available in one case and 12 options available in another. It was a test on how hard it was to make a decision as well as how confident the person was they made the right decision later on.
One of Sartre's points on existentialism was that radical freedom leads to anxiety.
In some sense, AI is removing scarcity. It isn't clear to me that this is the same kind of thing as going from starvation to surplus. It is more like the jam case where we aren't increasing abundance in a total sense, we increasing the options within the existing surplus.
The trailblazer was Microsoft Office.
I’m not sure if the job title tho. Software restoration?
I was discussing this with someone else and used the analogy of nuclear energy. Although nuclear plants are highly complex and use forces that have only been understood for the last hundred years, at the end of the day it's still boiling water to create steam to spin a turbine, and nothing about nuclear innovation can engineer away the laws of thermodynamics.
Agents have and will continue to drastically change how we develop code, but it's my belief that drastically changing our software practices to speed up development just means we're going to hit engineering limits faster, rather than being able to overcome those limits entirely.
Unless, or perhaps until, we figure out how to use LLMs effectively at the organization level we're still going to be limited to the "slime mold" level of organizational intelligence.
Will that happen? Most likely, but will it take a generation or ten? I suspect it'll be far harder for society to adapt at that level. We've had Mythical Man Month for decades yet software managers continue repeating the same mistakes.
Because of that the idea of OAI and Anthropic just moving to in-house product development makes sense.
During the hype cycle, making "x for y" is easiest to get funded and exit.
Once the hype crashes and we're in the trough of disillusionment, then people are bearish. So founders either move to the next hype cycle or figure out a more durable business from the ashes
In the past, if you wanted to do a task, you had to choose from one of a few (or maybe just one) incumbents and make their tool fit your task. Failing that, build your own tool, but that was expensive. If you did build your own, the tool might have value and could potentially be sold to others.
Going forward, building your own will be much easier and cheaper. Even if the incumbent tools suit the task well, you might choose to build your own for a variety of other reasons. e.g. If the incumbent raises prices or chooses to enshitify. What you will probably not do is adopt some third party's vibe-coded clone of an incumbent's tool, because that won't be customized to your needs and it won't have the track record of the incumbent. You can't trust it or control it so it will be better to build your own.
If an incumbent exists and you can now vibe-code a clone, you probably can't sell that clone "as is" and expect to be successful. You might be able to sell your services to customize and refine tools for small businesses that can't really afford to hire a solitary soul to build their own solution, but don't expect that niche to persist long-term.
To me, so far it seems like it is and it isn’t simultaneously. Some things are going at insane paces. I see things like reverse engineering and emulation breaking barriers. But we don’t see things like the quality and complexity of highly architected systems getting much better, maybe even getting worse as management pushes velocity onto employees.
I think what this shows is that AI is great at tedium and brute forcing, but it’s not actually good at making substantially better software with less of that “rationed” expertise. At least, not yet.
Tell me I'm wrong.
Now that SaaS CRUD apps can be built in 10 minutes, we can maybe do something more worthwhile, no? So much wasted effort has gone into this field already, in my opinion.
In Claude we trust.
It's a different view of scaling. I mean a lot of people have this absurd idea of "super intelligence" which is like the cover of this book
https://en.wikipedia.org/wiki/Supermind_(novel)
i mean what does "intelligence quotient 10,000" even mean? On the other hand it's very easy to imagine a world where we have systems like the best ones we have now but we can really afford to use them.
IQ tests measure, essentially, how fast you can do shallow pattern recognition.
And AI can do these kinds of tests easily.
There's no reason to think that companies are suddenly going to be spending 1:1 features : quality because both are faster / cheaper .
So expect the low quality to continue, and naturally get worse, because complexity is going up 1000x