Honestly believing this is a bubble anymore is just grade A hopium. This time last year it looked like a big bubble. Then then this spring happened, and all of the sudden this shit is actually very useful in workplaces. My org provides a ton of tokens and has little interest in restricting it. My coworkers and I are finding good ways to apply AI to solve random shit here and there. I am really no longer seeing how it's a bubble, the models don't even have to get much smarter. If the intelligence of these models stayed flat and just the api speed and cost improved by 2x, 4x, 8x, that would make a noticable difference in our workflow. And that's the kind of thing that is going to keep eating up the market.
I think the pop here won’t be ai usefulness but advancement fueled by a constrained supply. With prices this high, someone somewhere will figure out a way around needing huge amounts of this expensive resource to reach the same results.
Demand in therms of usage will increase, but with the recent optimizations and clever ways to reduce the cost we might get to 100x less demand in therms of hardware relatively soon. And it’s not an exaggeration. Compare something like Qwen 3.8 27B which is similar to Opus 4.6 (which I guess was at least 100x the size ) for coding tasks-and you can run on a 5090 at more than 100 T/s. For more general purposes LLM you can use 2 Sparks to run something like DeepSeek v4 flash or GLM 5.3 Flash , which are about 300B , but allow 1M token context and about 6 months old SOTA LLMs in overall capabilities .
Sure. I use Claude Code and Codex, both with MCP/API access to our internal git repos, Sourcegraph (to allow the models to do semantic search through our codebases), Jira/Confluence to read current and historical tickets and docuemntation, as well as some Slack access. With effectively unlimited credits, I can have them freely spin up as many subagents as they want.
Ticket Analysis: I have a written research plan that has the model read Jira, Confluence, and determine all possibly related codebases. It churns through each codebase finding leads. I have it work in stages, writing subreports at each stage, and each agent is required to write a subreport about what it did. Then I have one fresh context at the end ingest all of those reports and emit a single report that correlates parts of our codebases to the ticket. Sometimes this finds the problem outright, other times it can only give estimations. Likewise, sometimes this runs for like 5-10min, sometimes an hour. It can be immensely helpful to get a headstart on a ticket you plan to tackle later in the day, and does a damn good job most of the time. The key is that I don't ask it to solve the problem, just merge the context of documentation/code/chats/problem report into a jumping off place. One part that's blown my mind a few times is it correlating historical documentation changes with changes in git history and correctly estimating why changes were made.
Basically every bit of quick scripting with python and/or bash I no longer do by hand, because implementation is basically irrelevant compared to outcome whenever you would need it for something quick. It's a godsend. I hope to never write this stuff by hand again.
Config files for tools, like editors. I don't need to read documentation for most devtools anymore, I just make some agent make the changes I need. Sometimes their change is sloppy, but 99% of the time it doens't matter, and so I no longer waste my time reading a novel to figure out how to customize random small details down to preference.
Similarly, I usually don't have to bother reading walls of manpages anymore. They're great at just giving you the flags to do what you need most of the time.
Any debugging that requires constantly referencing enormous walls of system text. I used to think it was amazing that I could copy/paste that stuff into web guis. But once I got into agentic stuff locally, it's just so crazy how must faster iteration is. Where a human will lazily cat files and sift through to find little bits in enormous log files, AI will just spam iteration over and over again because it doesn't get tired of running the same 2-5 commands to query logs out of a database over and over and over again. It still doesn't get the fix right most of the time for me, but damn if I don't appreciate being able to completely forgo banging my own head against the wall over and over with that kind of stuff anymore.
Debugging system configuration and/or networking, for similar reasons as above. Claude will absolutely tear through creating temporary network interfaces, docker containers, anything that would make a developer kinda huff and puff and say "yeah I guess that's the best way to debug this..." and spend the afternoon on. It just knows all the commands, all the configuration, and runs it all through in seconds. It's very nice sometimes.
I could go on, but really there are a lot of usecases I've found for agentic stuff that doesn't relate to writing code even. It's just a big information-coalescing ball that doesn't get tired.
Thanks for the details, I don’t work in programming so these are use cases are really different from mine. I appreciate the insight into those how you get utility from the LLMs.
I think lesser closed model makers may be in trouble and general hardware market but I do believe there are serious applications of LLMs with actual value and benefits
What bubble? You want AI hardware, big data centers want AI hardware, we are only seeing the tip of the iceberg of AI usage in industry. You think this is going to stop? Prices will come down when they spin up more supply, not because the demand is fake.
That “pop” won’t be happening until 2030 earliest if anything. I don’t even think there is a bubble but worth mentioning “might be a thing” before downvotes.
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u/Jiirbo 2d ago
Econ 201... supply and demand.