I started doing some experimentation with this new Deep Think agent, and after five prompts I reached my daily usage limit. For $250 USD/mo that’s what you’ll be getting folks.
It’s just bizarrely uncompetitive with o3-pro and Grok 4 Heavy. Anecdotally (from my experience) this was the one feature that enthusiasts in the AI community were interested in to justify the exorbitant price of Google’s Ultra subscription. I find it astonishing that the same company providing free usage of their top models to everybody via AI Studio is nickel-and-diming their actual customers like that.
Performance-wise. So far, I couldn’t even tell. I provided it with a challenging organizational problem that my business was facing, with the relevant context, and it proposed a lucid and well-thought-out solution that was consistent with our internal discussions on the matter. But o3 came to an equally effective conclusion for a fraction of the cost, even if it was less “cohesive” of a report. I guess I’ll have to wait until tomorrow to learn more.
They might not have been ready/optimized for production, but still wanted to release it before Aug 2 EU AI Act, this way they have 2 years for compliance. So the strategy with aggressively rate-limit for few users make sense.
wheee, great way to lock in incumbents even more or lock out the EU from startups
Welcome to the lovely world of regulation, enjoy your stay.
If that’s the price to pay avoid poisoning myself with food to name only one thing that’s maybe not that bad.
I highly doubt that Europe needs dozen AI slop start ups.
Several years ago I thought a good litmus test for mastery of coding is not finding a solution using internet search nor getting well written questions about esoteric coding problems answered on StackOverflow. For a while, I would post a question and answer my own question after I solved the problem for posterity (or AI bots). I always loved getting the "I've been working on this for 3 days and you saved my life" comments.
I've been working on a challenging problem all this week and all the AI copilot models are worthless helping me. Mastery in coding is being alone when nobody else nor AI copilots can help you and you have dig deep into generalization, synthesis, and creativity.
(I thought to myself, at least it will be a little while longer before I'm replaced with AI coding agents.)
Your post misses the fact that 99% of programming is repetitive plumbing and that the overwhelming majority of developers, even ivy league graduates, suck at coding and problem solving.
Thus, AI is a great productivity tool if you know how to use it for the overwhelming majority of problems out there. And it's a boost even for those that are not even good at the craft as well.
This whole narrative of "okay but it can't replace me in this or that situation" is honestly between an obvious touche (why would you think AI would replace rather than empower those who know their craft) and stale luddism.
> 99% of programming is repetitive plumbing
Even IF that were true (and I'd argue that it is NOT, and it's people who believe that and act that way who produce the tangled messes of spiderweb code that are utterly opaque to public searches and AI analysis -- the supposed "1%"), if even as low as 1% of the code I interacted with was the kind of code that required really deep thought and analysis, it could easily balloon to take up as much time as the other "99%".
Oh, and Ned Ludd was right, by the way. Weavers WERE replaced by the powered loom. It is in the interest of capital to replace you if they are able to, not to complement you, and furthermore, the teeth of capital have gotten sharper over time, and its appetite more voracious.
> Even IF that were true (and I'd argue that it is NOT)
Can you share what these "hard problems" are that > 1% of developers are working on?
Try using any AI tool to write a working realtime GI (global illumination) implementation. I've been working on a novel implementation for 60fps/1080p GI and everytime I use Copilot or Claude to even try fixing a minor bug/troubleshoot it nukes entire functions and rewrites them using garbled shader code, old syntax/methods.
Puts things into stark perspective for me.
PS. no amount of prompt engineering will save you in this endeavour.
> I've been working on a novel implementation for 60fps/1080p GI
I don't think this is the type of work a "normal" developer is working on though, which is what my question was targeting.
I would think 99%+ are going to use an existing implementation like unreal engine, which AI can certainly help with.
Capital is also willing to have vastly lower quality and burden the remaining labor with more toil in exchange for even lower costs. Velocity will rise, quality will fall, toil will increase leading to more burnout but there will be more expendable bodies to cycle through the slop cleanup farm.
I've started to come to the conclusion that only greenfield projects consist of repetitive plumbing. Legacy software is like plumbing if all the pipes were tied into a knot. The edge cases, ambiguous naming, hacky solutions, etc. all make for a miserable experience, both for humans and AIs.
They're remarkably useless on stuff they've seen but not had up-weighted in the training set. Even the best ones (Opus 4 running hot, Qwen and K2 will surprise you fairly often) are a net liability in some obscure thing.
Probably the starkest example of this is build system stuff: it's really obvious which ones have seen a bunch of `nixpkgs`, and even the best ones seem to really struggle with Bazel and sometimes CMake!
The absolute prestige high-end ones running flat out burning 100+ dollars a day and it's a lift on pre-SEO Google/SO I think... but it's not like a blowout vs. a working search index. Back when all the source, all the docs, and all the troubleshooting for any topic on the whole Internet were all above the fold on Google? It was kinda like this: type a question in the magic box and working-ish code pops out. Same at a glory-days FAANG with the internal mega-grep.
I think there's a whole cohort or two who think that "type in the magic box and code comes out" is new. It's not new, we just didn't have it for 5-10 years.
Yeah, I mean the interpolation part is new, but boy do I miss pre-enshitification Google!
I have similar issues with support form companies that heavily push AI and self-serve models and make human support hard. I'm very accomplished and highly capable. If I feel the need to turn to support, the chances the solution is in a KB is very slim, same with AI. It'll be a very specific situation with a very specific need.
There are a lot of internal KB's companies keep to themselves in their ticketing systems - would be interesting to estimate how much good data there is in there that could in the future be used to train more advanced (or maybe more niche or specific) AI models.
They COULD be great AI chatbots with good data, but in general what is being deployed is just crap. The cheapest app they can get set up as fast as possible so they can check a box, there's no real concern to make it good. I had a talk with my last CEO in January about a similar project, I gave a plan on how to do it right. They instead tried to half ass it and it completely failed. But they don't care because the board member that wanted it is happy because it exists.
Curious to know what are those challenging programming problems are. Can you share some examples?
Currently I'm porting the Playwright / Puppeteer client API to run in a Chrome extension without using the Chrome DevTools Protocol (CDP). Since there is 100s of Chrome extension AI copilots using Playwright with CDP with all the problems that come with that, I believe my library can be very useful. The vscode copilot chat with any model is always trying to evaluate strings in the content script using chrome.scripting.executeScript with eval('') and new Function('') which violates the CSP policy in MV3. The use case is novel but calling executeScript is common and this policy has been enforced for the last 2.5 years and available for a couple before that. Worse is it will convert my valid function definitions to eval() and new Function('') even though it has nothing to do with prompt.
This has been my thought for a long time - unless there is some breakthrough in AI algo I feel like we are going to hit a "creativity wall" for coding (and some other tasks).
Any reason to think that the wall will be under the human level?
Off the thousands of responses I have read from the top LLMs in the last couple of years: never seen one that was creative. Throwing writing, coding, problem solving, mathematical questions and what not.
It's somewhat easier to perceive the creativeless aspect with stable diffusion. I'm not talking about the missing limb or extra finger glitches. With a bit of experience looking through generated images our brain eventually perceives the absolute lack of creativity, an artist probably spot it without prior experience with generative AI pieces. With LLMs it takes a bit longer.
Anecdotal, baseless I guess. Papers were published, some researchers in the fields of science couldn't get the best LLMs to solve any unsolved problem. I recently came across a paper stating bluntly that all LLMs tested were unable to conceptualize, nor derive laws that generalize whatsoever. E.g formulas.
We are being duped, it doesn't help selling $200 monthly subscriptions - soon for even more - if marketers admitted there is absolutely zero reasoning going on with these stochastic machines on steroids.
I deeply wish the circus ends soon, so that we can start focusing on what LLMs are excellent, well fitted to do better, faster than humans.
Creative it is not.
It doesn't need to be creative, it needs to brute force boilerplate.
An opinion on the current state of the field. The usual stochastic parrot mention. That, I see. Reasons for the existence of the wall? Not so much.
That it is usual doesn't mean it's false.
Everyone I talked to, knowledgeable in machine learning and/or deep learning, who had no reason to pretend of course, agreed an LLM is a stochastic machines. That it is couples with very good other NLP techniques doesn't change that.
It is why even the best models today miss the shot by a large margin, then hit a decent match. Again back to the creative issue. If it was done before, a good input and a well trained model on good data will output good data likely to match with the best (matching) answer ever produced. Some NLP to make it sound unique is not equal to creativity.
> It’s just bizarrely uncompetitive with o3-pro and Grok 4 Heavy.
In my experience Grok 4 and 4 Heavy have been crap. Who cares how many requests you get with it when the response is terrible. Worst LLM money I’ve spent this year and I’ve spent a lot.
It's interesting how multi-dimensional LLM capabilities have proven to be.
OpenAI reasoning models (o1-pro, o3, o3-pro) have been the strongest, in my experience, at harder problems, like finding race conditions in intricate concurrency code, yet they still lag behind even the initial sonnet 3.5 release for writing basic usable code.
The OpenAI models are kind of like CS grads who can solve complex math problems but can't write a decent React component without yadda-yadda-ing half of it, while the Anthropic models will crank out many files of decent, reasonably usable code while frequently missing subtleties and forgetting the bigger picture.
Those may have been the exact people creating training material for OpenAI…
It's just wildly inconsistent to me. Some times it'll produce a work of genius. Other times, total garbage.
Unfortunately we are still in the prompt optimization stage, garbage in garbage out
I hear this repeated so many times I feel like its a narrative pushed by the sellers. Year ago you could ask for glass of wine filled to the brim and you just wouldnt get it. It wasnt garbage in, garbage out, it was sensibility in, garbage out.
The line where chatbots stop being sensible and start outputting garbage is in movement, but slower than avg joe would guess. You only notice it when you get an intuition of the answer before you see it, which requires a lot of experience on range of complexity. Persisten newbies are the best spotters, because they ask obvious basic questions while asking for stuff beyond what geniuses could solve, and only by getting garbage answer and enduring a process of realizing its actually garbage they truly make wider picture of AI than even most powerusers, who tend to have more balanced querries.
Maybe. That could be true.
But doesn’t happen the same with other tools. I’ll give the same exact prompt to all of LLMs I have access to and look at the responses for the best one. Grok is consistently the worst. So if it’s garbage in, garbage out, why are the other ones so much better at dealing with my garbage?
I think it meant in the training stage, not inference.
It's not particularly interesting if Deep Mind comes to the same (correct) conclusion on a single problem as o3 but costs more. You could ask gpt 2.5 and gpt4 what 1+1= and would get the same response with gpt 4 costing more, but this doesn't tell us much about model capability or value.
It would be more interesting to know if it can handle problems that o3 can't do, or if it is 'correct' more often than o3 pro on these sort of problems.
i.e. if o3 is correct 90% of the time, but deep mind is correct 91% of the time on challenging organisational problems, it will be worth paying $250 for an extra 1% certainty (assuming the problem is high-value / high-risk enough).
> It would be more interesting to know if it can handle problems that o3 can't do
Suppose it can't. How will you know? All the datapoints will be "not particularly interesting".
> Suppose it can't. How will you know?
By finding and testing problems that o3 can't do on Deep Think, and also testing the reverse? Or by large benchmarks comparing a whole suite of questions with known answers.
Problems that both get correct will be easy to find and don't say much about comparative performance. That's why some of the benchmarks listed in the article (e.g. Humanity's Last Exam / AIME 2025) are potentially more insightful than one person's report on testing one question (which they don't provide) where both models replied with the same answer.
> I find it astonishing that the same company providing free usage of their top models to everybody via AI Studio is nickel-and-diming their actual customers like that.
I agree that’s not a good posture, but it is entirely unsurprising.
Google is probably not profiting from AI Ultra customers either, and grabbing all that sweet usage data from the free tier of AI Studio is what matters most to improve their models.
Giving free access to the best models allows Google to capture market share among the most demanding users, which are precisely the ones that will be charged more in the future. In a certain sense, it’s a great way for Google to use its huge idle server capacity nowadays.
I'm burning well over 10 millions tokens a day on free tier. 99% of the input is freely availzble data, the rest is useless. I never provided any feedback. Sure there is some telemetry, they can have it.
I doubt I'm an isolated case. This Gemini gig will cost Google a lot, they pushed it on all android phones around the globe. I can't wait to see what happens when they have to admit that not many people will pay over 20 bucks for "Ai", and I would pay well over 20 bucks just to see the face of the c suite next year when one dares to explain in simple terms there is absolutely no way to recoup the DC investment and that powering the whole thing will cost the company 10 times that.
It could be that your problem was too simple to justify the use of Deep Think.
But yes, Google should have figured that out and used a less expensive mode of reasoning.
"I'm sorry but that wasn't a very interesting question you just asked. I'll spare you the credit and have a cheaper model answer that for you for free. Come back when you have something actually challenging."
Actually why not? Recognizing problem complexity as a fist step is really crucial for such expensive "experts". Humans do the same.
And a question to the knowledgeable: does a simple/stupid question cost more in terms of resources then a complex problem? in terms of power consumption.
IIRC that isn't possible under current models at least in general, for multiple reasons, including attention cannot attend to future tokens, the fact that they are existential logic, that they are really NLP and not NLU, etc...
Even proof mining and the Harrop formula have to exclude disjunction and existential quantification to stay away from intuitionist math.
IID in PAC/ML implies PEM which is also intentionally existential quantification.
This is the most gentle introduction I know of, but remember LLMs are fundamentally set shattering, and produce disjoint sets also.
We are just at reactive model based systems now, much work is needed to even approach this if it ever is even possible.
[0] https://www.cmu.edu/dietrich/philosophy/docs/tech-reports/99...
Hmm, I needed Claude 4’s help to parse your response. The critique was not too kind to your abbreviated arguments that current systems are not able to gauge the complexity of a prompt and the resources needed to address a question.
It feels like the rant of someone upset that their decades of formal logic approach to AI become a dead end.
I see this semi-regularly: futile attempts at handwaving away the obvious intelligence by some formal argument that is either irrelevant or inapplicable. Everything from thermodynamics — which applies to human brains too — to information theory.
Grey-bearded academics clinging to anything that might float to rescue their investment into ineffective approaches.
PS: This argument seems to be that LLMs “can’t think ahead” when all evidence is that they clearly can! I don’t know exactly what words I’ll be typing into this comment textbox seconds or minutes from now but I can — hopefully obviously — think intelligent thoughts and plan ahead.
PPS: The em-dashes were inserted automatically by my iPhone, not a chat bot. I assure you that I am a mostly human person.
Google actually does provide that service! https://cloud.google.com/vertex-ai/generative-ai/docs/model-...
Just put in the prompt customization to model responses on Marvin from Hitchhiker's Guide.
"Here I am, brain the size of a planet, and they ask me to ..."
Do they not do this in some general way behind the scenes? Surprising to me if not.
> ... Recognizing problem complexity as a first step...
Well, I don't think it's easy or even generally possible to recognize a problem complexity. Imagine you ask for a solution for a simple expressed statement like find an n > 2 where z^n = x^n + y^n. The answer you will receive will be based on a trained model with this well known problem but if it's not in the model it could be impossible to measure its complexity.
I know this is a joke but I have been able to lower my costs by routing my prompts to a smaller model to determine if I need to send it to a larger model or not.
“This meeting could’ve been an email”
Model routing is deceptively hard though. It has halting problem characteristics: often only the smartest model is smart enough to accurately determine a task's difficulty. And if you need the smartest model to reliably classify the prompt, it's cheaper to just let it handle the prompt directly.
This is why model pickers persist despite no one liking them.
Yes but prompt evaluation is far faster than inference as it can be done (mostly) in parallel, so I don't think that's true.
The problem is that input token cost dominates output token cost for the majority of tasks.
Once you've given the model your prompt and are reading the first output token for classification, you've already paid most of the cost of just prompting it directly.
That said, there could definitely be exceptions for short prompts where output costs dominate input costs. But these aren't usually the interesting use cases.
No, you're talking about costs to user, which are oversimplifications of the costs that providers bear. One output token with a million input tokens is incredibly cheap for providers
> One output token with a million input tokens is incredibly cheap for providers
Source? Afaik this is incorrect.
Chevk out any LLM API providers pricing. Output tokens are always significantly more expensive than input (which can also be cached).
Input tokens usually dominate output tokens by a lot more than 2x though. It’s often 10x or more input. It can even easily be 100x or more. Again in realistic workflows.
Caching does help the situation, but you always at least pay the initial cache write. And prompts need to be structured carefully to be cacheable. It’s not a free lunch.
That's usually not the case for thinking models. And usually hard problems have a very short prompt.
For me personally (using mostly for coding and project planning) it's nearly always the case, including with thinking models. I'm usually pasting in a bunch of files, screenshots, etc., and having long conversations. Input nearly always heavily dominates output.
I don't disagree that there are hard problems which use short prompts, like math homework problems etc., but they mostly aren't what I would categorize as "real work". But of course I can only speak to my own experience /shrug.
Yeah coding is definitely a situation where context is usually very very large. But at the same time in those situations something like Sonnet is fine.
but if the less strong model has low false positives you can just route them in order of strength
That's a very big "if".
I'm not in the AI sceptic camp (LLMs can be useful for some tasks, and I use them often), but this is the big issue at the moment.
In order for agentic AI to replace (for example) a software engineer, we need a big step up in capability, around an order of magnitude. These chain of thought models do get a bit closer to that, although in my opinion we're still a way away.
However, at the same time we need about an order of magnitude decrease in price. These models are expensive even at the current price tokens are sold at which seems to be below the actual cost. And these massive CoT models are taking us in completely the wrong direction in terms of cost
Interestingly Gemini CLI has a very generous free quota. Is Google's strategy just overpricing some stuff and subsidizing the underpriced stuff?
It doesn't, it's not "1000 Gemini Pro" requests for free, Google misled everyone. It's 1000 Gemini requests, Flash included. You get like 5-7 Gemini Pro requests before you get limited.
I'm getting 100 Gemini Pro requests per day with an AI Studio API key that doesn't have billing enabled.
After that it's bumped down to Flash, which is surpisingly effective in Gemini CLI.
If I need Pro, I just swap in an API from an account with billing enabled, but usually 100 requests is enough for a day of work.
Obviously I'm not talking about API keys, this is what I would recommend though: https://ai.google.dev/gemini-api/docs/rate-limits#free-tier
I'm talking about "logging in with a Google Account".
Your link shows the free tier gets 100 Pro requests per day.
That matches my experience with a free account. With Gemini CLI it doesn't seem to matter if I log in with a Google Account or use an API key from AI Studio with billing disabled.
Yesterday I had two coding sessions in Gemini CLI with a total of 73 requests to Pro with no rate limiting.
https://imgur.com/a/Ki6g1qc
I can't explain why you're seeing something else, but my experience has been pretty consistent.
Maybe your usage pattern is different from mine and you're getting hit by the 5 RPM limit??
I created an AI Studio key (unbilled) probably over a year ago or so. Is it still good for the current models are should I be creating a new key?
There is no difference.
Whoa. I’m definitely getting just handful of requests via free, just like patent commenter…
This is the fundamental pricing strategy of all modern software in fact.
Underpriced for consumers, overpriced for businesses.
I've found the free version swaps away from pro incredibly fast. But our company has gemini but can't even get that - we were being asked to do everything by API key.
I suspect that the main goal here was to grab the top spot in a bunch of benchmarks, and being counted as an "available" model.
They're using it as a major inducement to upgrade to AI Ultra. I mean, the image and video stuff is neat, but adds no value for the vast majority of AI subscribers, so right now this is the most notable benefit of paying 12x more.
FWIW, Google seems to be having some severe issues with oddball, perhaps malfunctioning quota systems. I'm regularly finding extraordinarily little use of gemini-cli is hitting the purported 1000 request limit, when in reality I've done less than 10.
I faced the exact same problem, with the API. It seems that it doesn't throttle early enough, then may cumulate the cool off period, malong it impossible to determine when to fire requests again.
Also, I noticed Gemini (even flash) has Google search support. But only via the web UI or the native mobile app. Via the API that would requires serp via MCP of sort. Even with Gemini pro.
Oh, some models are regularly facing outages. 503s are not uncommon. No SLA page, alerts, whatsoever.
The reasoning feature is buggy, even if disabled, it sometimes triggers anyway.
It occured to me the other day that Google probably have the best engineers given how good Gemini performs and where it's coming from, and the context window that is uniquely large compared to any other model. But that it is likely operated by managers coming from AWS where shipping half baked, barely tested software, was all it took to get a bonus.
The part I cannot understand is why for many AI offerings, I cannot make out what each pricing tier does with a quick glance.
What happened to the simplicity of Steve Jobs' 2x2 (consumer vs.pro, laptop vs. desktop)?
I'd be interested in tests involving tasks with large amounts of context. Parallel thinking could conceivably useful for a variety of specific problem types. Having more context than any specific chain of thought can reasonably attend to might be one of them.
What was the experimentation? Can you share with us so we can see how "bizarrely uncompetitive" it is?
Bizarrely uncompetitive is referencing the 5 uses per day not the performance itself
Similar complaints are happening all over reddit with the Claude Code $200/mo plan and Cursor. The companies with deep VC funding have been subsidizing usage for a year now, but we're starting to see that bleed off.
I think the primary concern of this industry right now is how, relative to the current latest generation models, we simultaneously need intelligence to increase, cost to decrease, effective context windows to increase, and token bandwidths to increase. All four of these things are real bottlenecks to unlocking the "next level" of these tools for software engineering usage.
Google isn't going to make billions on solving advanced math exams.
Agreed, and big context windows are key to mass adoption in wider use cases beyond chatbots (random ex: in knowledge management apps, being able to parse the entire note library/section and hook it into global AI search), but those use cases are decidedly not areas where $200 per month subscriptions can work.
I'll hazard to say that cost and context windows are the two key metrics to bridge that chasm with acceptable results.... As for software engineering though, that cohort will be demanding on all front for the foreseeable future, especially because there's a bit of a competitive element. Nobody wants to be the vibecoder using sub-par tools compared to everyone else showing off their GitHub results and making sexy blog posts about it on HN.
Outside of code, the current RAG strategy is throw shit tons of unstructured text at it that has been found using vector search. Some companies are doing better, but the default rag pipelines are... kind of garbage.
For example, a chat bot doing recipe work should have a RAG DB that, by default, returns entire recipes. A vector DB is actually not the solution here, any number of traditional DBs (relational or even a document store) would work fine. Sure do a vector search across the recipe texts, but then fetch the entire recipe from someplace else. Current RAG solutions can do this, but the majority of RAG deployments I have seen don't bother, they just abuse large context windows.
Which looks like it works, except what you actually have in your context window is 15 different recipes all stitched together. Or if you put an entire recipe book into the context (which is perfectly doable now days!), you'll end up with the chatbot mixing up ingredients and proportions between recipes because you just voluntarily polluted its context with irrelevant info.
Large context windows allow for sloppy practices that end up making for worse results. Kind of like when we decided web servers needed 16 cores and gigs of RAM to run IBM Websphere back in the early 2000s, to serve up mostly static pages. The availability of massive servers taught bad habits (huge complicated XML deployment and configuration files, oodles of processes communicating with each other to serve a single page, etc).
Meanwhile in the modern world I've ran mission critical high throughput services for giant companies on a K8 cluster consisting of 3 machines each with .25 CPU and a couple hundred megs of RAM allocated.
Sometimes more is worse.
IMO: Context engineering is a fascinating topic because it starts approaching the metaphysical abstract idea of what LLMs even are.
If you believe that an LLM is a digital brain, then it follows that their limitation in capabilities today are a result of their limited characteristics (namely: coherent context windows). If we increase context windows (and intelligence), we can simply pack more data into the context, ask specific questions, and let the LLM figure it out.
However, if you have a more grounded belief that, at best, LLMs are just one part of a more heterogeneous digital brain: It follows that maybe actually their limitations are a result of how we're feeding it data. That we need to be smarter about context engineering, we need to do roundtrips with the LLM to narrow down what thbe context should be, it needs targeted context to maximize the quality of its output.
The second situation feels so much harder, but more likely. IMO: This fundamental schism is the single reason why ASI won't be achieved on any timeframe worth making a prediction about. LLMs are just one part of the puzzle.
Information in an LLM exists in two places:
1. Embedded in the parameters
2. Within the context window
We all talk a lot about #2, but until we get a really good grip on #1, I think we as a field are going to hit a progress wall.
The problem is we have not been able to separate out knowledge embedded in parameters with model capability, famously even if you don't want a model to write code, throwing a bunch of code at a model makes it a better model. (Also famously, even if someone never grows up to work with math day to day, learning math makes them better at all sorts of related logical thinking tasks.)
Also there is plenty of research showing performance degrades as we stuff more and more into context. This is why even the best models have limits on tool call performance when naively throwing 15+ JSON schemas at it. (The technique to use RAG to determine which tool call schema to feed into the context window is super cool!)
I wonder if the next phase for leveraging LLMs against large sets of contextual, proprietary data (code repositories & knowledge bases come to mind) is going to look more like smaller models highly (and regularly) trained/fine-tuned against that proprietary data (that is maybe delegated tasks by the ultra-sized internet scale omni-brain models)
If I'm asking Sonnet to agentically make this signin button green: does it really matter that it can also write haikus about the japanese landscape? That links back to your point: We don't have a grip, nearly at all, on how much this crosstalk between problem domains matters. Maybe it actually does matter? But certainly most of it doesn't. B
We're so far from the endgame on these technologies. A part of me really feels like we're wasting too much effort and money on training ASI ultra internet scale models. I'm never going to pay $200+/mo for even a much smarter Claude; what I need is a system that knows my company's code like the back of its hand, knows my company's patterns, technologies, and even business (Jira boards, Google docs, etc), and extrapolates from that. That would be worth thousands a month; but what I'm describing isn't going to be solved by a 195 IQ gigabrain, and it also doesn't feel like we're going to get there with context engineering.
It's also a question of general vs specialized tools. If LLMs are being used in a limited capacity, such as retrieving recipes, then a limited environment where it only has the ability to retrieve complete recipes via RAG may be ideal in the literal sense of the word. There really is nothing better than the perfect specialized tool for a specialized job.
I did embedded work for years. A 100mhz CPU with 1 cycle SRAM latency and a bare metal OS can do as much as a 600MHZ CPU hitting DRAM running a preemptive OS.
Specialized tools rock.
Big, coherent context windows are key to almost all use-cases. The whole house of cards RAG implementations most platforms are using right now are pretty bad. You start asking around about how to implement RAG and you realize: No one knows, the architecture and outcomes at every company are pretty bad, the most common words you hear are "yeah it pretty much works ok i guess".
> Similar complaints are happening all over reddit with the Claude Code $200/mo
I would imagine 95% of people never get anywhere near to hitting their CC usage. The people who are getting rate-limited have ten windows open, are auto-accepting edits, and YOLO'ing any kind of coherent code quality in their codebase.
I have ultra. Will not be renewing it. Useless, at least have global limits and let people decide how they want to use it. If I have tokens left, why can't I use it for code assist?
it turns out that AI at this level is very expensive to run (capex, energy). my bet is that AI itself won't figure out how to overcome these constraints and reach escape velocity.
Perhaps this will be the incentive to finally get fusion working. Big tech megacorps are flush with cash and could fund this research many times over at current rates. E.g. NIF is several billion dollars; Google alone has almost $100B in the bank.
Mainframes are the only viable way to build computers. Micro processors will never figure out how to get small and fast enough for personal computers to reach escape velocity.
Why do you think the analogy hold?
Hardware typically gets faster and cheaper over time. Unless we hit hard a wall because of physics then I don't see any reason that won't continue to be true.
The factories to make the better chips are themselves increasingly expensive; this is acceptable when their cost of construction can be amortised over more devices, but we're already at the point where the global poor get smartphones before safe water, so further factory cost increases can't really be assumed to be amortised better.
That said, current LLMs are not compute constrained, they're RAM and bandwidth constrained, so a (relatively) cheap factory that's dedicated just to filling a datacenter with hardware designed specifically for one particular AI architecture, that's something I think is plausible. As @tome accidentally reminded me about recently, the not-Musk Groq (https://groq.com/) is all about this.
Well it's an analogy, that's a water-tight argument.
our minds are incredibly energy efficient, that leads me to believe it is possible to figure out, but it might be a human rather than an AI that gives us something more akin to a biological solution.
This could fix my main gripe with The Matrix. ”Humans are used as batteries” always felt off, but it totally would make sense if the human brains have uniquely energy efficient pattern matching abilities that an emerging AI organism would harvest. That would also strengthen the spiritual humanist subtext.
Thats because the original script did actually have the human farms being used for brainpower for the machines. They changed it to "batteries" because they thought audiences at the time wouldnt understand it!
Wow, TIL. That strengthens my perception of the masterpiece. Thanks
That bugged me too! I decided that they actually meant "source of creativity" which made more sense.
> it turns out that AI at this level is very expensive to run (capex, energy)
If it's CapEx it's -- by definition -- not a cost to run. Energy costs will trend to zero.
Why will energy costs trend to zero?
Renewables will keep getting more efficient and cheaper to install, batteries will continue to get cheaper, at some point they'll crack fusion. Prices go negative or zero in several places already (West Texas wind energy overnight, solar in Chile). The question seems less "when will we get abundant, virtually free clean energy" and more "will we do it in time to avoid climate collapse".
Uncompetitive how, what task and Eval?
Gemini is consistently the only model that can reason over long context in dynamic domains for me. Deep Think just did that reviewing an insane amount of Claude Code logs - for a meta analysis task of the underlying implementation. Laughable to think Grok could do that.
The rate limits are not because of compute performance or the lack of. It's to stop people from training their own models on the very cutting edge.