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I haven't posted too much about AI on here, largely because my own personal experiences with using it have been boring and underwhelming. Generating offensive memes (9/11 gender reveal, racial stereotypes, etc.) is my most positive interaction with AI. And partly because I find the pro-AI "AGI is just around the corner bro!" crowd obnoxious as hell, and I find that most discussions about it depend on accepting certain massive assumptions about what we actually do (and don't) know about the nature of intelligence, consciousness, the human brain, etc. For the purposes of making my biases clear up front: Personally, I'm religious and believe in the existence of a human spirit/soul, so I'm already strongly biased against claims that consciousness is an emergent property of sufficiently advanced systems or any arguments along those lines.
Regardless, a few developments have happened recently that have motivated me enough to actually make a top-level post about this. The first being my (employer-mandated) use of Claude to generate code. "You're not using the latest model, just one more model and we'll reach AGI"-bros officially in shambles after this one. I have an HTTP API client library I wrote a few years ago for interacting with a 3rd party API. There's a good amount of duplicate logic throughout for things like setting up and making the requests, caching, etc. I asked Claude to look over the code and extract out the duplicate logic into a single implementation Here's how it messed up just the authentication part of it:
This was with the latest version of Claude Sonnet. We don't have access to the latest version of Opus, but I'm sure an AI-bro on here would insist that Opus would totally get this right. Regardless, it failed spectacularly at what would be an easy (but tedious) task for a mid-level developer and above (or a sufficiently talented junior).
The second happening is the ARC prize people releasing version 3 of their AGI test suite, a series of puzzle games. They released it within a few hours of Jensen Huang saying he thinks the latest and greatest models are capable of AGI. Humans were capable of solving 100% of the puzzles. The highest scoring AI couldn't complete more that 0.5%.
I'm willing to bet future models will do st least somewhat better on this, but only because I'm maximally cynical and I fully expect these puzzles to be included in the training set for future SOTA models.
I tried several of the puzzles myself, and none of them are terribly difficult. I'd estimate that anyone in the 100-110 IQ range or higher would be able to solve most or all of them. This development has further reinforced my belief that LLMs are basically just really advanced statistical regression models on crack, but nothing approaching what we would consider actual intelligence or conscious thought (and this is before we get into Chinese Room style criticisms of them).
In any case, I'm curious to see what you all think of these. Even the AI-bros I've been speaking about condescendingly throughout this post. If anything, I'm actually most curious about and interested in the AI-bros responses, I'd love to hear yoyr thoughts.
Here are the AGI puzzles for anyone interested in trying them out: https://arcprize.org/arc-agi/3
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Come on dude, you can't be serious.
I honestly have tired of retreading this conversation, the skeptics are immune to evidence. Even the people on my team who are enthusiastic about AI are configuring and using it poorly. The difference between prepping the environments, having agents go through and document the code base before running an orchestrated agent with dedicated subagents and just yolo throwing an agent at it - and I half expect you didn't even run the damn thing in agent mode - with a half cocked vague prompt practically hoping it fails so that you can own the ai bros is massive. You don't want it to work so it won't. I know this isn't convincing to you, I've tried being convincing to you guys, even after you're out of a job you won't be convinced. It's pointless.
edit: slightly less run on sentence(slightly)
While I do align with you in that I consider the current models very powerful and use them plenty myself, and that using some Sonnet + Cline workflow while claiming that AI is incapable is misleading, I do find this sort of crypto-style FOMO inducing rhetoric counter-productive and annoying.
If you believe that the models will usher in the end of history, that they really do end up as AGI, ASI, ushering in the singularity then no amount of using 2026 agents at work will do anything to save you or change the outcome.
On the other hand, in worlds where the models do plateau at some point and end up being commoditized enterprise tooling, nobody is doomed because they didn't use agents correctly in 2026; even boosters have very little consensus on what actually works right now. There will be time to adopt the tooling as capabilities are better understood, the UX will get better, and people will develop best practices and discard what doesn't work or what is no longer necessary; who's still using LangChain or fine-tuning LORA's on hands in 2026?
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Today was our biweekly demo day. One of our engineers showed their work in putting together a harness for aiding in translating a winform app to a rest site + web front end. They built an orchestrator, an array of custom agents designed to handle the specifics of our environment, custom skills for understanding and interacting with our db ect ect, dozens of files. And as they walked through their prompting it became clear to me that they never actually invoke the orchestrator so they were in fact just using the vanilla agent.
It's obvious to me that we're like a couple more releases from all this work not being necessary, the future tools will simply as a matter of course customize themselves to the environments they're exposed to. But there is as of now an art form to getting really good results from current models. I'll say the most important concept is something like optimizing for "context density", you have ~1million tokens to work with but every marginal token long before that degrades performance while relevant context improves it. So you need to balance it out, using sub agents to offload discreet tasks and provide maximally dense reports. I have oracle agents that simply returns true or false, a single line, or a full report depending on what is asked of it. Of course this works even better if you can have the thing go through and summarize your code ahead of time with the intention of minimizing the guess and check nature of looking through your repo, every wrong search pollutes context. Or you can just poorly write out an ambiguous two lines, throw the vanilla nerfed agent in the deep end, have it go through 5 iterations of "compacting context"(i.e. throwing away important bits of information because you've hit the hard limit) and get back a sub par response then laugh about how dumb these hype monkeys are, maybe they're just so terrible at coding that this half assed demonstration is impressive to them?
Whatever dude, enjoy 6 more months of ignorance before it's impossible deny, you'll deny it anyways, not my problem, I tried to warn you.
What software/framework are you using for creating/orchestrating agent systems? I tried smolagents for a while but my results were clearly less good than just formatting my query nicely and giving it to a normal LLM.
Enough people I trust are getting good results with agents that I want to look into it more, but would very much appreciate any advice you can give.
At work we're stuck with VS code/github copilot which is not ideal but allows customizing/spawnimg subagents and tool calls which is the big requirement. Agents themselves aren't a big deal, they're essentially just custom prompts, they become important for being created as sub agents to isolate tasks to narrow contexts. There's some customization to your own env you should do but you can just start by asking Claude/gpt to spin up some basic ones that you can tweak over time. Basically any time I notice it get stuck or need to guess and check a bunch I have it makes some tweaks to the documentation. I'm cooking up a method to automate that process. Where I'd spend my time first is making some guide files for your code base. Putting together some easily greppable documents outlining our database schema greatly improved its ability to interact with it.
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The real LLM psychosis is how insane interacting with genpop over AI is.
It's revolutionizing my ability to code, as in, I could not code (still can't) and yet I have a small and growing fleet of tiny software tools I always wished existed
It has 100% replaced google for me, and I haven't spent an evening trying to figure out what the best toaster/winter coat/part for my car is since late 2024, because I can now get an answer in 5-35 minutes. Maybe some of that is just cutting down analysis paralysis, but it's not like I've lowered my standards
It's materially improved my productivity at work (not a coder) and I know there are significant productivity gains on the table due to extremely slow corporate adoption (no agents allowed) and the fact I just haven't set aside a week to figure out jankier "agents at home" processes bc I'm hoping IT just comes to their senses.
Then you have people still in 2026 who genuinely think AI will "go away" or at least it's a passing fad like VR that some will enjoy and most will ignore.
Even at work, where the initial gains are obvious and easy to capture, I speak to managers who can barely get their teams to open ChatGPT/Copilot (hard to blame them for not wanting to use copilot) at all?!? I don't actually hate this, because all the people who don't adopt AI buy me ~6 extra months of employment before I'm replaced too, but it's genuinely making me feel insane.
I'm watching a technological revolution that's going to change every part of the adult life I thought I'd have , and so many people just.... don't see it? Insane
Note that both of those can be true at the same time. Total cumulative AI capex will probably cross $2 trillion this year, and cumulative opex is on the same order again. And that's just in the US.
If this "technological revolution" doesn't end up replacing a significant percentage of national labor costs, it actually might fade away - and the only thing remaining will be whatever open weight models can be run on cheap hardware at that point in time. And if the Chinese keep releasing last year's SOTA for free, none of the envisioned business models might hold water.
Either way, there's really only one way costs can go from here. If business AI doesn't go away, those of us still with jobs will get to work with agents costing our employers on the same order as the people they replaced.
Inference profit margins are pretty healthy. If all AI development stopped today, we'd end up with some very profitable AI providers once the dust settled
The AI Labs could all be happy fat tech companies if they just became inference providers.
It's the training, and the capex required to support ever larger training runs, that is the massive money sink.
This is 100% the future. I am very much on this path today. That being said, with token prices over time, the token spend might end up being a lot cheaper than the salary spend it replaced. Although if token prices fall I might just end up using 100x more lol
Yes, but we aren't quite there yet. Not even close, in my opinion, at least when we're talking about serious job displacement. Unless there's a phase change, were looking at years of more insane capex and opex.
And those trillions will need to be payed back with interest. We're not talking about a Netflix or Office 365 licence that every office drone just has. For millions of workers, access to those tools will rival transportation and housing in ongoing cost.
No problem, if your employer already has half the staff on SolidEdge/Ansys/ect licenses and generally does not care what toolboxes everybody gets. For the rest? Small business, low productivity labor, labour limited by hardware throughout (classic example: radiology)? They won't really contribute to paying off that debt, so they won't get a lot of tokens, and none from the good models.
We've gotten used to tech, especially software, being cheap. For the current economics to make any sense, this will come to a hard stop. On the cost side, AI is much more like an excavator than like a shovel, and it really needs to replace just as many workers to make sense building them.
And I can well imagine this never happening. Maybe they'll never get reliable enough for that much unsupervised work, especially work you can't write unit tests for.
I should have said "post bankruptcy, the AI Labs could be happy fat inference providers. I actually think the current SOTA models, if we worked on improving harnesses, etc a lot, could still be a huge change on their own. Obviously smarter model = better, but just what we have now with a ton of scaffolding can do a lot.
But yes, the amount of capex they have all spent means they now need something way better than "excel helper" to pay it back. But in a non-hyperscale world, LLMs as a normal technology could a profitable medium/large SAAS industry.
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Claude Sonnet 4.6 is the latest model though
It's not known for sure, the labs do not say, but it's very likely that sonnet 4.6 is a distillation of opus 4.6, essentially training a weaker model on the signal of the stronger model. What is definitely known is that sonnet 4.6 is considerably weaker than opus 4.6, given opus tokens cost 5x as much as sonnet tokens this should be clear. Sonnet is great for rote work, my admin agent that handles git and jira calls uses sonnet, but I'd never use it for core work.
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This is a very uncharitable response, c’mon, you know that. Sonnet 4.6 is a cheaper, much smaller model that was released 12 days after Opus 4.6, of course it’s going to be worse.
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