The bigger issue is that LLMs haven’t had much training on Q as there’s little publically available code. I recently had to try and hack some together and LLMs couldn’t string simple pieces of code together.
Perhaps for array languages LLMs would do a better job running on a q/APL parse tree (produced using tree-sitter?) with the output compressed into the traditional array-language line noise just before display, outside the agentic workflow.
This is the dream, but it keeps crashing and sinking against reality. It seems intuitive that running language models on the AST should work better than running them on the source code, but as far as I'm aware every attempt to do this has resulted in much worse performance. There's so much more training data available as source code, and working in source code form gives you access to so much more outside context (comments, documentation, Stack Overflow posts), that it more than cancels out the disadvantages.
There is some truth in this. I fit into a few of these buckets and I don’t think I could ever recommend their enterprise stuff after having my favourite consumer products pulled.
Just to be clear, Tether and Circle also have complete control over their respective stablecoins if they so choose. They have the exact same power to reverse, freeze and block any transaction or balance just as PayPal and Venmo do.
An implicit fee by not paying you any interest for money held in Venmo.
Also notice there's no option to automatically transfer received money into your real checking account. They are banking on you forgetting your money is there and they are earning the interest but not passing it to you.
For this reason I prefer receiving money via Zelle but pay with Venmo.
We really need to add some kind of risk to people making these claims to make it more interesting. I listened to the type of advice you're giving here on more occasions than I can remember, at least once for every major revision of every major LLM and always walked away frustrated because it hindered me more than it helped.
> This is actually amazing now, just use [insert ChatGPT, GPT-4, 4.5, 5, o1, o3, Deepseek, Claude 3.5, 3.9, Gemini 1, 1.5, 2, ...] it's completely different from Model(n-1) you've tried.
I'm not some mythical 140 IQ 10x developer and my work isn't exceptional so this shouldn't happen.
The dark secret no one from the big providers wants to admit is that Claude is the only viable coding model. Everything else descends into a mess of verbose spaghetti full of hallucinations pretty quickly. Claude is head and shoulders above the rest and it isn't even remotely close, regardless of what any benchmark says.
Tried about four others, and to some extent I always marveled about capabilities of latest and greatest I had to concede they didn’t make faster. I think Claude does.
That poster isn't comparing models, he's comparing Claude Code to Cline (two agentic coding tools), both using Claude Sonnet 4. I was pretty much in the same boat all year as well; using Cline heavily at work ($1k+/month token spend) and I was sold on it over Claude Code, although I've just recently made the switch, as Claude Code has a VSCode extension now. Whichever agentic tooling you use (Cline, CC, Cursor, Aider, etc.) is still a matter of debate, but the underlying model (Sonnet/Opus) seems to be unanimously agreed on as being in a league of its own, and has been since 3.5 released last year.
I've been working on macOS and Windows drivers. Can't help but disagree.
Because of the absolute dearth of high-quality open-source driver code and the huge proliferation of absolutely bottom-barrel general-purpose C and C++, the result is... Not good.
On the other hand, I asked Claude to convert an existing, short-ish Bash script to idiomatic PowerShell with proper cmdlet-style argument parsing, and it returned a decent result that I barely had to modify or iterate on. I was quite impressed.
Garbage in, garbage out. I'm not altogether dismissive of AI and LLMs but it is really necessary to know where and what their limits are.
Actually defining those situations and collecting the data (which should help identify those situations) are the hard parts. Having an autonomous system that do it has been solved for ages.
Yeah, I spent some time researching this crowd and most of the ones I found have the playbook of selling to indie hackers and talking about how successful they are with fake MRR screenshots.
I’ve always dwelled over $5 a month subscriptions for iPhone apps due to subscription fatigue. I find myself signing up for $200 AI subscriptions without a moments hesitation.
What do you do with $200/mo subscription to Anthropic? I’d consider myself a power user and I’ve never come close to a rate limit on the $20 subscription.
If you discusses a plan with CC well upfront, covering all integration points where things might go off rail, perhaps checkpoint the plan in a file then start a fresh CC session for coding, then CC is usually going to one shot a 2k-LoC feature uninterrupted, which is very token efficient.
If the plan is not crystal clear, people end up arguing with CC over this and that. Token usage will be bad.
I personally find gemini 2.5 pro and o4.1 mini to handle complexity better than claude code. i was a power user of claude code for a couple months but its bias to action repeatedly led me down the wrong path. what am i missing?
Are there available numbers to support this? Software engineering in the U.S. is well-compensated. $200/mo is a small amount to pay if it makes a big difference in productivity.
My day job in talks to do that. I'm partly responsible for that decision, and i'm using my personal $200/m plan to test the idea.
My assessment so far is that it is well worth it, but only if you're invested in using the tool correctly. It can cause as much harm as it can increase productivity and i'm quite fearful of how we'll handle this at day-job.
I also think it's worth saying that imo, this is a very different fear than what drives "butts in seats" arguments. Ie i'm not worried that $Company will not get their value out of the Engineer and instead the bot will do the work for them. I'm concerned that Engineer will use the tool poorly and cause more work for reviewers having to deal with high LOC.
Reviews are difficult and "AI" provides a quick path to slop. I've found my $200 well worth it, but the #1 difficulty i've had is not getting features to work, but in getting the output to be scalable and maintainable code.
Sidenote, one of the things i've found most productive is deterministic tooling wrapping the LLM. Eg robust linters like Rust Clippy set to automatically run after Claude Code (via hooks) helps bend the LLM away from many bad patterns. It's far from perfect of course, but it's the thing i think we need most atm. Determinism around the spaghetti-chaos-monkeys.
Yes, but that doesn't mean they aren't finding real value
The challenge with the bubble/not bubble framing is the question of long term value.
If the labs stopped spending money today, they would recoup their costs. Quickly.
There are possible risks (could prices go to zero because of a loss leader?), but I think anthropic and OpenAI are both sufficiently differentiated that they would be profitable/extremely successful companies by all accounts if they stopped spending today.
So the question is: at what point does any of this stop being true?
> I think anthropic and OpenAI are both sufficiently differentiated that they would be profitable/extremely successful companies by all accounts if they stopped spending today.
Maybe. But that would probably be temporary. The market is sufficiently dynamic that any advantages they have right now, probably isn't stable defensible longer term. Hence the need to keep spending. But what do I know? I'm not a VC.
Have we seen any examples of any of these companies turning a profit yet even at $200+/mo? My understanding is that most, if not all, are still deeply in the red. Please feel free to correct me (not sarcastic - being genuine).
If that is the case at some point the music is going to stop and they will either perish or they will have to crank up their subscription costs.
I am absolutely benefitting from them subsidizing my usage to give me Claude Code at $200/month. However, even if they 10x the price its still going to be worth it for me personally.
I totally get that but that’s not really what I asked/am driving at. Though I certainly question how many people are willing to spend $2k/mo on this. I think it’s pretty hard for most folks to justify basically a mortgage for an AI tool.
My napkin math is that I can now accomplish 10x more in a day than I could even one year ago, which means I don't need to hire nearly as many engineers, and I still come out ahead.
I use claude code exclusively for the initial version of all new features, then I review and iterate. With the Max plan I can have many of these loops going concurrently in git worktrees. I even built a little script to make the workflow better: http://github.com/jarredkenny/cf
Again I understand and I don’t doubt you’re getting insane value out of it but if they believed people would spend $2000 a month for it they would be charging $2000 a month, not 1/10th of that, which is undoubtedly not generating a profit.
As I said above, I don’t think a single AI company is remotely in the black yet. They are driven by speculation and investment and they need to figure out real quick how they’re going to survive when that money dries up. People are not going to fork out 24k a year for these tools. I don’t think they’ll spend even $10k. People scoff at paying $70+ for internet, a thing we all use basically all the time.
I have found it rather odd that they have targeted individual consumers for the most part. These all seem like enterprise solutions that need to charge large sums and target large companies tbh. My guess is a lot of them think it will get cheaper and easier to provide the same level of service and that they won’t have to make such dramatic increases in their pricing. Time will tell, but I’m skeptical
It looks like their revenue has indeed increased dramatically this year but I can’t find anything saying they’re profitable, which I assume they’d be loudly proclaiming if it had happened. That being said looking at the charts in some of these articles it looks like they might pull it off! I need to look more closely at their pricing model, I wonder what they’re doing differently
I guess my genuine question in response is can you tell investors "Please give us billions of dollars - we never plan on being profitable, just endlessly growing and raising money from outside sources"? Unless the goal is to be sold off eventually that seems a bit like a hard sell.
> "Please give us billions of dollars - we never plan on being profitable, just endlessly growing and raising money from outside sources"?
The goal for investors is to be able to exit their investment for more than they put in.
That doesn't mean the company needs to be profitable at all.
Broadly speaking, investors look for sustainable growth. Think Amazon, when they were spending as much money as possible in the early 2000s to build their distribution network and software and doing anything they possibly could to avoid becoming profitable.
Most of the time companies (and investors) don't look for profits. Profits are just a way of paying more tax. Instead the ideal outcome is growing revenue that is cost negative (ie, could be possible) but the excess money is invested in growing more.
Note that this doesn't mean the company is raising money from external sources. Not being profitable doesn't imply that.
> My napkin math is that I can now accomplish 10x more in a day than I could even one year ago, which means I don't need to hire nearly as many engineers, and I still come out ahead.
The only answer that matters is the one to the question "how much more are you making per month from your $200/m spend?"
I'm curious, how are you accounting this? Does the productivity improvement from Claude's product let you get your work done faster, which buys you more free time? Does it earn you additional income, presumably to the tune of somewhere north of $2k/month?
Anecdotally, I can take on and complete the side projects I've always wanted to do but didn't due to the large amounts of yak shaving or unfamiliarity with parts of the stack. It's the difference between "hey wouldn't it be cool to have a Monte Carlo simulator for retirement planning with multidimensional search for the safe withdrawal rate depending on savings rate, age of retirement, and other assumptions" and doing it in an afternoon with some prompts.
For curiosity, how complex are these side projects? My experience is that Claude Code can absolutely nail simple apps. But as the complexity increases it seems to lose its ability to work through things without having to burn tokens on constantly reminding it of the patterns it needs to follow. At the very least it diminishes the enjoyment of it.
It varies, but they're not necessarily very complex projects. The most complex project that I'm still working on is a Java swing UI to run multiple instances of Claude code in parallel with different chat histories and the ability to have them make progress in the background.
If you need to repeatedly remind it to do something though, you can store it in claude.md so that it is part of every chat. For example, in mine I have asked it to not invoke git commit but to review the git commit message with me before committing, since I usually need to change it.
There may be a maximum amount of complexity it can handle. I haven't reached that limit yet, but I can see how it could exist.
Simple apps are the majority of use-cases though - to me this feels like what programming/using a computer should have been all along: if I want to do something I’m curious about I just try with Claude whereas in the past I’d mostly be too lazy/tired to program after hours in my free time (even though my programming ability exceed Claude’s).
Well that's why I'm curious. I've been reading a lot of people talking about how the Max plan has 100x their productivity and they're getting a ton of value out of Claude Code. I too have had moments where Claude Code did amazing things for me. But I find myself in a bit of a valley of despair at the moment as I'm trying to force it to do things I'm finding out that it's not good at.
There are definitely things it can't do, and things it hilariously gets wrong.
I've found though that if you can steer it in the right direction it usually works out okay. It's not particularly good at design, but it's good at writing code, so one thing you can do is say write classes and some empty methods with // Todo Claude: implement, then ask it to implement the methods with Todo Claude in file foo. So this way you get the structure that you want, but without having to implement all the details.
I work at an Amazon subsidiary so I kinda have unlimited gpu budgets. I agree with siblings, I'm working on 5 side projects I have wanted to do as a framework lead for 7 years. I do them in my meetings. None of them are taking production traffic from customers, they're all nice to haves for developers. These tools have dropped the costs of building these tools massively. It's yet to be seen if they'll also make maintaining them the same, or spinning back up on them. But given AI built several of them in a few hours I'm less worried about that cost than I was a year ago (and not building them).
The point is that if a minority is prepared to pay $200 per month, then what is the majority prepared to pay? I also don’t think this is such an extreme priority, I also know multiple people in real life with these kinds of selections.
Yeah, cause we want to be in control of software, understandably. It's hard to charge for software users have full control of - except for donations. That's #1 reason for me to not use any gen AI at the moment - I'm keeping an eye on when (if) open-weight models become useful on consumer hardware though.
So $415m revenue per month, annualized $5 billion / yr. Let's say we use a revenue multiple of 4x, that means OpenAI should be valued at $20 billion USD just based on this. Then one obviously has several other factors, given the nature of OpenAI and future potential. Maybe 10x more.
Which puts the current valuations I've heard pretty much in the right ballpark. Crazy, but it could make sense.
It’s a bizarre language.