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Sure, if they are using Copilot. Copilot is fucking garbage.
If they made a model that was half as good as Claude or Kimi or Qwen, they wouldn't be in this mess.
How many tokens does laughing use?
This might be the most perfect gif for the situation
Meh, right now, and only if you're trying to replace the work force. At is current state, on a $30 a month Copilot plan you'll already see a huge gain in efficiency with supervised coding and agents doing minor chores and maintenance.
The average coder isn't better then opus 4.6. No, is not ready to run production code bases unsupervised. Yes, it's absolutely ready to do many many simple tasks autonomous and more complex coding with supervision.
If you take even a week to try out this shit with an eye for what's possible currently and have an ounce of common sense, I fail to see how folks don't realize this will absolutely change how software is delivered. Yes humans will be involved but there will be much much less direct coding and a lot more supervision over multiple concurrent tasks that have had the time to delivery cut significantly.
I use these tools extensively, and they absolutely do not replace the need for a coder. The reality is that they're fundamentally incapable of telling whether something is correct or not in the business sense. And Simply churning out a ton of wrong code really fast doesn't actually help anybody.
They certainly can be a help for a developer. For example, I can fluently write code in any language now even if I'm not familiar with the stack or syntax. A skill that would've taken months of effort to build previously. But in terms of actual workflow, it's not all that much faster because I still have to review what the tool is doing, and human comprehension is still the bottleneck in the whole process.
I don't feel like the companies are concerned about it costing more right now as much as they are betting that it will be cheaper in the long run. The cost of labor isn't unlikely to decrease drastically while the technology is likely to become cheaper.
While I would love to believe Microsoft is being burned by spending on AI, I think they don't mind spending more now so long as they can trade the cost labor for the costs of technology and maintain similar productivity.
Feels like they hope this will be to white collar jobs what Uber was for taxi drivers. Current profitability isn't really the goal as much as being able to reproduce similar outputs.
Even if AI someday does become profitable short term, total profits will still go down long-term, because all profit comes from human labor (or exploiting nature). All any technology ever does in capitalism is to replace human labor, thereby putting more pressure on the empirically proven tendency of profits to fall. Profit gains from technology can only ever be short term and relative to competition who hasn't yet adapted the technology. Once everyone has, prices drop, adjusting to lower socially necessary labor time.
The only way for the billionaire class to keep profits flowing a bit longer at this point is to do what we already see them doing now: get rid of the free market by enforcing monopolies with captive markets, bonded labor and merging big capital with an increasingly violent and warring state apparatus: capitalism inevitably leads to fascism-imperialism every time.
AI lends itself to this because of the centralized nature of data centers, the already monopoly based business model of tech companies and the political power and influence those monopolies hold. On the other hand, there is some potential, if not revolutionary at least disruptive potential, in small scale, specialized, open source models that can be trained with fewer resources.
The only alternative road to fascism, of course, leads to communism.
Even if you take worst case costs Anthropic's "Profitability" Swindle https://share.google/UV5HNgJyMzfcknekF it's already approaching profitable.
If you slow down the model update cycle it's looking like at least anthropic can be profitable 🤷♂️. That argument is loosing it's weight quickly.
It’s a bad bet. AI is unprofitable now because they are building the datacenters.‘do you think one day those are going to be done and just never touched again? No, they’re going to constantly upgraded. They’re an ongoing and forever cost for upkeep, a fraction of- but a significant one - of their initial cost.
The AI companies, however, are never going to lower prices. They’re going to raise them until some of the market gets uncomfortable paying them. Then, they’re going to hold them there.
Will it be cheaper in the long run? No. Not really.
But it was never about replacing workers anyways. It was about fooling enough C-levels and middle managers and even “normies” that Super Amazing Smart Bot can do your job while YOU live in comfort! Incredible! Hey kids, tried of thinking? Let the agents do it FOR you!
And once enough were on board, and the “demand” was so great; oopsie they bought up all the compute on planet earth. All information flows through them now. Every company, every home, every document, every email, every PowerPoint presentation, every lewd text. Indexed, summarized, graphed, reported in real-time.
We just lost personal computing, and it’s literally capitalism’s design goal.
I think AI will be profitable for the next generation of AI business models that emerge from the abandonment of the current business model of developing the frontier. But the prerequisite is that the companies give up on developing the frontier and decide that the models they have are good enough, then get hardware optimized for inference on those models, stagnating into long term commodity infrastructure, like providing phone service or electricity for profit.
So yeah, I think many of these technologies are here to stay, but the growth will stagnate this year as data center construction swallows up companies that overextended.
Nah, they don’t care if they spend money so long as it can’t be conceived of as “giving” money to blue collar working class. How dare they take a billionaire’s money!
This is my take too.
People were probably making the same jokes in the early mainframe era of computers.
They did not. Computers were able to do things that were almost impossible to do by hands.
LLMs don't do that. They regurgitate what we've been doing for decades.
They're great to brute force some problem tho. You give them shitloads of data and they're incredible to go through it.
Problem is, we took a REALLY unfinished tech and tried to package it as the saviour .
The “ AI as a savior” was only ever a marketing ploy to encourage enterprise to fall entirely and foolishly into the arms of SaaS, which then forces the consumer market to follow suit.
The fascists have built their panopticon.
Someone had to say it first - it’s no secret. A bit surprising that MS is early here.
Hahahahahhhahahahahaaha
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There are ways to make it cheaper. Starting with maybe not encouraging token-maxing.
Generally, unless you're either a FOSS project or generating images/video, you have to be doing something very wrong to spend more on AI than on salaries.
Not really. LLMs are still completely unable to manage even medium scale architectures. At a corporate scale they’re literally just spending on trying to have the most context they can in the LLM. There’s no getting around it.
Yeah, the smarter way to use LLM-based agents is carefully defined tasks. Mozilla describes their vulnerability assessment processes in this blog post.
Mozilla describes the process they've used: building a harness that instructs a model to find a specific category of vulnerability on a specific interface, and then write up its findings. It's a narrow enough context that the model gets specific instructions, and a simple definition of success, and it sets up many such tasks that can be fed into the existing process for verifying and triaging bugs. Note that the output for this LLM pipeline basically feeds into the same interface for accepting bug reports from the public, or from their human contributors within the project.
There's a couple of takeaways here, too:
- This pipeline is model agnostic. Mozilla set it up before Mythos was released, and its description of other models (Opus 4.7, Codex) confirms that Mythos is better but not a true game changer. The ability to swap out other models provides some assurance that the work done to develop the pipeline will be useful when cheaper or better models come along, or when a model becomes unavailable (like when a provider decides a particular model is too expensive to run, or a provider goes under).
- The increase in automated output (and presumably automation-assisted contributions from the public) has given the humans more work to do. Automation in this context actually increases the demand for human labor.
- Other projects will need to develop their own custom pipelines, specific to their project, to get good results from LLM based agents.
There are ways to use these tools, but none of it really seems like a truly revolutionary/disruptive change to how large projects are managed.
Generating images is not that expensive, it's surprisingly inexpensive.
Yeah, it's counterintuitive because it's a lot more work for a human to draw a picture (much less a photorealistic picture) than to write a few words, but human language grammar actually has a lot of strict rules that makes that stream of letters work as "valid" output, much less "decent" output that kinda matches the prompt/description. Transpose a pair of letters or even substitute a single letter (or token) and you've got an output that just doesn't work, in a way that generated images don't have to worry about.
At which point this is about billionaires trying to escape accountability again. Regarding a work force, again.
this post was submitted on 23 May 2026
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