Like yesterday? LLM-assisted coding is $100/mo. It looks very commoditized when most houses in developed world pay more for electricity than that.
My definition of LLM-assisted coding is that you fully understand every change and every single line of the code. Otherwise it's vibe coding. And I believe if one is honest to this principle, it's very hard to deplete the quota of the $100 tier.
im probably just not being charitable enough to what you mean, but thats an absurd bar that almost nobody conforms to even if its fully handwritten. nothing would get done if they did. But again, my emphasis is on that im probably just not being charitable to what you mean.
x = 0
for i in range(1, 10):
x += i
print(x)
They just mean they can more or less follow along with what the code is doing. You don't need to be very charitable in order to understand what he genuinely meant, and understanding code that one writes is how many (but not all) professional software developers who didn't just copy and paste stuff from Stackoverflow used to carry out their work.
How deep do i need to understand range() or print() to utilize either, on the slightly less extreme end of the spectrum.
But ya, im pretty sure its a point that maybe i coulda kept to myself and been charitable instead.
print(X) is a great example. That's going to print X. Every time.
Agent.print(x) is pretty likely to print X every time. But hey, who knows, maybe it's having an off day.
Jeff Atwood, along with numerous others (who Atwood cites on his blog [1]) were not exaggerating when the observed that the majority of candidates who had existing professional experience, and even MSc. degrees, were unable to code very simple solutions to trivial problems.
[1] https://blog.codinghorror.com/why-cant-programmers-program/
That's how I read it, and I would agree with that.
If it's low-stakes, then the required depth to accept the code is also low.
at what level of abstraction can you claim to actually "understand" the code?
You're claiming to understand down to the CMOS, but you are failing to even engage with what level understanding should be accepted. is "down to the CMOS" the bar? because then you're gonna be on an uphill battle as potentially the only human who traces a simple hello world python script down to it, because thats not how people develop software with high level languages.
is understanding the print()'s underlying code the bar? seems fairly gatekeepy, its kinda intuitive what a print does, everyone trusts its gonna do what its designed to do in the same way we trust the water that comes out of our faucets.
Obviously I don't mean "understanding it so you can draw the exact memory layout on the white board from memory."
But I and others in my company have very heavy usage. We only rarely, with parallel agentic processes, run out of the $200 a month plan.
And what do I mean by "hard"? I mean, it requires a lot of active thinking to think about how you can actively max it out. I'm sure there's some use cases where maybe it is not hard to do this, but in general, I find most devs can't even max out the $100 a month plan, because they haven't quite figured out how to leverage it to that degree yet.
(Again, if someone is using the API instead of subscription, I wouldn't be surprised to see $2,000 bills.)
You can use a Max subscription for work, btw.
But, it's not $100/mo. I think the best showcase of where AI is at is on the generative video side. Look at players like Higgsfield. Check out their pricing and then go look at Reddit for actual experiences. With video generation the results are very easy to see. With code generation the results are less clear for many users. Especially when things "just work".
Again, it's not $100/month for Anthropic to serve most uses. These costs are still being subsidized and as more expensive plans roll out with access to "better" models and "more* tokens and context the true cost per user is slowly starting to be exposed. I routinely hit limits with Anthropic that I hadn't been for the same (and even less) utilization. I dumped the Pro Max account recently because the value wasn't there anymore. I am convinced that Opus 3 was Anthropic's pinnacle at this point and while the SotA models of today are good they're tuned to push people towards paying for overages at a significantly faster consumption rate than a right sized plan for usage.
The reality is that nobody can afford to continue to offer these models at the current price points and be profitable at any time in the near future. And it's becoming more and more clear that Google is in a great position to let Anthropic and OAI duke it out with other people's money while they have the cash, infrastructure and reach to play the waiting game of keeping up but not having to worry about all of the constraints their competitors do.
But I'd argue that nothing has been commoditized as we have no clue what LLMs cost at scale and it seems that nobody wants to talk about that publicly.
Video is a different ballgame entirely, its less than realtime on _large_ gpus. moreover because of the inter-frame consistency its really hard to transfer and keep context
Running inference on text is, or can be very profitable. its research and dev thats expensive.
this is a small nit, but you still have to pay your electric bill, the $100/mo is on top of that. if you're doing cost accounting you don't want to neglect any costs. Just because you can afford to lease a car, doesn't mean you can afford to lease a 2nd car.
I anticipate a Napster-style reckoning at some point when there's a successful high-profile copyright suit around obviously derivative output. It will probably happen in video or imagery first.