Gemini 4 Argon
blog.google
See also: Gemini 4 Argon (High): Intelligence, Performance and Price Analysis - https://news.ycombinator.com/item?id=49914236
blog.google
Edit to add the fix: https://gist.github.com/birep/6f2c8d490c7a29820997d57bd654c3...
I use a mix of Fable 5.1, Opus 5.5, and Gemini 3.8 Flash and Gemini holds it's own. Especially in writing, frontend, and sysadmin work. agy for configuring a NixOS system has been truly incredible.
I cancelled Ultra because they forced me into their harness like I should adapt to them, rather than the other way around.
I actually just cancelled Ultra also because I couldn't subscribe to a YouTube Family plan while I had it active (Google... :[) but trying to use Codex as a replacement while I testdrive Astra makes me yearn for agy again.
That said, compaction feels like an idea that should work reasonably well, but across all of the major providers and agent tools I've used has never actually produced compelling results, to where if I see I'm getting close to the token limit I prefer to start putting a bow on the project and readying it for a fresh start. Even when I provide a detailed compaction prompt it usually focuses on the wrong stuff.
Still works better than compaction.
On another environment I've been doing something roughly similar, but have integrated Hindsight as a kind of all-in-one of the above and am still trying to suss out the best compaction strategy.
Mentioned by the author in a recent HN thread, I'm also experimenting with automating this through a tiny issue tracker called epiq [1] that basically lets the agent sessions themselves file tickets with the follow-on tasks and relevant handoff right in them, and then a dispatcher automatically launches those tickets into new agent sessions.
I assume Anthropic & friends have noticed this as well and will change how they handle long running sessions, so the gap will likely close over time, but this is definitely where things stand today.
Same teamates also post 'Sol deleted my git repo!' or 'Sorry, ignore those 300 PR comments i was just looking!' ~once a month.
What are people using 1M context window for?
I have a skill that spins up worktrees and isolated services on unique ports so I can work in parallel. Antigravity queues all my prompts and makes me confirm to submit them anytime a long running process like a hot reloading UI is active.
The models are fine, the limits are generous, but the dev experience shit tier. Before they were a Codex clone, AntiGravity was an IDE and during the transition to a clone they outright deleted my IDE. It took them a week to roll out a fix.
For almost a year they didn't allow you to see usage limits. Then when they did show them, they update every ~30 minutes and require 4 clicks to navigate to. It's a little better now, but it's still painfully behind the curve.
Do you know a single product from Infosys / Cognizant / Tata done right?
agy --dangerously-skip-permissions
gemini-cli supported 'pre-write diff tabs' (y/n) in external editors like vscode. In Claude Code I heavily use 'pre-write diff tabs' for documentation and miss it sincerely in agy cli.
IMHO Gemini 3.8 flash is fast and good enough, but the agy-suite is below par to say it nice. Someone else in this thread calls agy a terrible harness which is probably more accurate.
accept-edits
With 'some features added' I'm referencing this but don't see change in daily work (still many prompts making getting-work-done impossible): v2.14.0 (September 15, 2026) "New Permissions System" [1]
Details: Introduced the new unified permissions system, presets (Default, Request Review, Turbo), syntax-highlighted permission requests, and restructured the settings under Global Permissions and project-level Inherit Global.
[0] https://antigravity.google/docs/cli/modes/#available-modes [1] https://antigravity.google/docs/changelog/
They've got Zed, VSCode, Jetbrains... But no Emacs or NeoVIM
I would rather not risk my Google account.
It does mean however that it cannot operate as flexibly as it it could with raw API, IMO, but agent-shell is essentially designed towards wrapping the official clients
While it may be technically allowed, I'm not about to risk my account. Google has proven themselves to be capricious and arbitrary when it comes to TOS enforcement, and their appeals system doesn't meaningfully exist in practice.
https://antigravity.google/docs/ide/extensions/
What I see is integrations for specific editors.
[1] https://github.com/agentclientprotocol/registry/blob/main/an...
But thank you, it appears there is an official and generic ACP client.
I also had that weird Youtube problem. I had to go without it for several days because signing up for Ultra hijacks your YouTube account for no reason.
1) Try to integrate agy into a workflow. It can't do standard I/O like: tail -200 app.log | claude -p "Find the problem"
2) Hard iteration limits. Preventing runaways is good. Preventing me from looping on purpose is anti-user. See also number 7.
3) Not open source so I can't fix any of these problems.
4) No skills. In 2026. Yikes.
5) No persistent memory (see Claudes auto memory)
6) No sub-agents or orchestration of any type really.
7) Weird hard coded limits and constant API errors on everything (scaling problems?)
8) No /loop command
9) /btw is weird and ephemeral. No way to merge it back to the conversation.
10) Unstable in general.
11) No way to control it via API.
I could keep going on. I would suggest taking a class on Claude Code or Codex then using it for a few months. Swapping is always painful, but it's so worth it. Then if you want try to go back to agy. Don't worry, agy won't have changed much. It improves at a snails pace.
I'm not sure this list is correct. Number 4 is especially wrong, since Skills are available with the launch of Antigravity 2:
https://antigravity.google/blog/introducing-google-antigravi...
edit: removed persistent memory from list since I realized I'm using a plugin for that and it's apparently not native
Luckily for me both expired yesterday and I was able to subscribe back again (first Youtube family and then Google AI plan).
I like CLI more than TUI, and TUI more than GUI where appropriate. For working with text TUI is better, for e.g. images GUI (GIMP).
All the AI-generated UIs I've seen have been very derivative, certainly not eliminating any of the disadvantages of typical GUI interfaces.
Realistically, the whole "overlapping windows" GUI model, and everything that derives from that, was a metaphor geared towards people who'd never seen a computer before. It fit the increasing consumer focus of computing interfaces. It's no wonder that technical people often prefer TUIs.
Maybe AI will bring real advancements in GUIs, but someone's still going to have to make it happen.
That is what we need, and if you're making the argument that a terminal shell is the best place to provide them then I don't know what to say.
It's not that GUIs are inherently worse in principle, but in practice they often are.
The point about overlapping windows is that that "desktop" model permeates the thinking about GUI design, but it's fundamentally limiting and misguided.
> we need complex (but not overlapping windows, sure!) interactivity and rich display capability.
Yep. Pity today's GUIs can't deliver that.
Despite the capitals and trying to come across as someone who SEES "what is COMING" (yay!), you have absolutely no idea what you are talking about.
No wait, I will leave you with a hint. Do whatever you wish to do with that.
Hint: So, you probably want me to use a toolset which is incredibly inferior, as of today, for "me/my usage", just because you feel "IT CAN BE". Right.
Perlis has an aphorism for this, as he does every important problem [0].
In today's world - and idea stated stated is an idea stolen .
It's missing the point. I mean think about the basics, why open the huge bash hole only then to have to close it? If you think about it logically, the only way you can sandbox bash is by writing your own bash implementation specifically for agentic use cases.
What benchmarks are usually good at is showing to what degree new models are better than old models. What they are not good at, by construction, is showing that harnesses are well adapted to how people use them.
Why? Because 4.6 actually talked like a human being. It actually organized its thoughts well, and got the main information across without the wall of text that makes your eyes glaze over. So from the perspective of human-computer interaction and maximizing the productivity of a developer+agent team, 4.7 and 4.8 were regressions. Despite much better benchmark performance.
Even if we consider autonomous agents, that benchmark is not indicative of how well they will interpret *your* requests. Or how well they will interact with other agents in a flock/swarm situation. The benchmark just doesn't cover this. (And the difference can be nontrivial! Sakana AI's published results show two generations of uplifting potential from better harnesses.)
This sort of thing alarms me. Having a $bigcorp account becomes a ""social credit"" system where they can ban you from all your personal stuff if they decide that you (or your agents!) are doing stuff they don't like.
I don't want to mess with antigravity because my google account is too entrenched in my life.
without having an entirely separate google account with its own separated bans, theres just no ability to trust those
Yes it's the same with Claude. However, OpenAI allows you to use any harness you like. Which makes sense and that's the primary reason I have their plan now rather than Googles.
EDIT: I checked online and I couldn't find any report of complete banning for using third party harnesses, only Gemini service suspension, but It's never hurts to be careful. If my secondary email gets banned, I should be able to use my main email on agy.
It also makes me think if Google Family with Google One could be abused for extending inference limits.
Not only could it not complete the small task, the code was obviously wrong from looking at it and did not even compile.
When I pointed that out it got pissy and insisted the code was perfect and I didn't know how to use a compiler, or the compiler was buggy. Pasting the compiler errors did not help.
Surreal.
Is that a reference to https://xkcd.com/353/
Gemini's web-app UI leaves so much to be desired but it does do a good job for siloed questions and research.
Also I hope you don’t have children, eat meat, travel, have a car, run AC, buy things in other countries and such. Those things all take way way way more natural resources.
Patches to open source software are public goods. Your using them doesn’t prevent others from using them. So if you spend resources creating a public good, it’s in everyone’s interest to share it.
Why would the guy who wrote curl share it? We can all build our own now...
Why do the Linux folks need to be so selfless? We can all build our own kernel now...
in amdkfd and hsakmt
Yes, that means AMD is sitting on both sides. They wrote software that doesn't work with their own software.
Asked pi agent it to identify the main hero sprite size of game I was running. It had a ton of shader effects so it was hard to determine.
It used some cli tools to identify that it was a game made with Godot, decompiled the executable but data was encrypted, broke the encryption after writing a brute force tool to test keys extracted from the exe, then proceeded to extract the game gd scripts and assets, only to answer the question of the sprite size.
Still impressive that it did so much just to answer my simple question.
I'm using it to run overnight tasks, and that's it until my quota runs out.
Canceled my subscription.
its a lot less chatty imo
In the local app interface the winning choice is “efficient” and then turn off the sliders for warmth enthusiasm emoji etc.
It makes openai models so good to talk to i really have trouble switching.
Also canceled my ChatGPT Pro $200/mo subscription. Their Oct 30 price hikes and slow GPT-6.1 model has me looking for alternatives.
- AI is just a tool, like excel; it does what the human operating it tells it to
- next token prediction cannot be true understanding
- models can have no desires and goals, don't anthropomorphize it
However, "hallucination" is very much not one of them
It didn't work. Gemini: "Oh yeah, that obviously cannot work, it's not possible to do it through OBD2" (paraphrasing)
It was quite funny to me, but a bit less so to my colleague.
I'm assuming that Argon has at least a June 2026 date, but man, the 3 series models were a mess with newer information.
> The knowledge cutoff date for Gemini 3.8 Flash is March 2026
https://deepmind.google/models/model-cards/gemini-3-8-flash/
The "some domains" are very narrow. They likely just RL'ed popular queries.
Of course, it's called "flash," and that implies its purpose. I have little use for speed and a LOT of use for accuracy, so I'm hopeful 4.0 is much better. I saw a benchmark earlier today showing that it is much less prone to hallucinations. Let's see.
And I saw it do this twice, once for Android 14 and once for Android 16.
I think this is just within 3.8 flash's capabilities.
Including going first for decompiling AGY binary instead of searching the web for documentation...
completely offtopic but is rolling with rocm worth it? I spend a fair bit monthly on rental gpus for projects and going to upgrade at home instead, AMD has some solid winners here pricewise but get conflicting reports about using it for ML in 2026.
once upon a time it seemed unthinkable to use anything but nvidia but seems to have come a long way since I last looked, probably would be just pytorch and gemma 31B
I get the feeling the situation is only going to improve longer term so might be a good time to just do it
It's so fucking easy.
From AMD: https://lemonade-server.ai/
Then you can easily throw a openweb-ui container in front, and then connect to the openweb-ui via your mobile app of choice (if you want chat, otherwise you just point your harness of choice at the lemonade server api endpoint).
ROCm promises a 30-50% prompt processing speedup. This is REALLY important for my workflow so I've been trying to get this shit to work for months. But no release before v10 worked well enough with any engine for it to matter.
The llama.cpp release binaries for ROCm (10) FINALLY work on gfx1501 and its relatives (with the correct shell variables), but the prompt processing boost doesn't materialize and the token generation speed decreases.
There continues to be a chronic problem across all engines with the ROCm integration for UMA devices. The good news is that some improvements have been made to that end for Vulkan, so more recent llama.cpp Vulkan binaries are now faster.
I say this is awesome, even as I glossed over the README and vomited in my mouth. The halogen repo looks like the same utter AI bullshit littering GitHub. But this one delivers, in spite of it's slop-riddled hallmarks.
In any case, yeah, ~55 tok/s on a high quality model (and massive RAM savings I think?), seems dope.
I've tried Codex as a harness too, and that was nice. I don't find a significant difference between Antigravity and Codex. Codex has more features but I don't use them.
I have a client app on a very old (for the JS world) version of eleventy using NetlifyCMS (also outdated). Claude has quite easily picked that up to add features to it along the way.
I don't think their point was about knowing the stack, but being able to point a harness at something running on your desktop GUI and say "change this".
Being able to edit and recompile pretty much any part of the OS and userland (often not even needing to reboot!) is not something that can be said about Windows for sure, or even lots of things on Macs too. Or even when the browser is effectively the operating system, the JS/TS others write is also hard to change in your end.
Even if the source code's old, the fact that it is publicly available makes it much easier to train & improve on than if it were walled off.
I used to be afraid of Arch, because I don't want a system that takes work because I'm already busy with work. But now I love it, because the LLM can tweak every knob and fix every issue for me, so it ends up being the OS that takes the least work to use. Get an error message? Tell the LLM and they fix it. Something not working exactly like you like it? Tell the LLM and they tweak it for you. This also works to great effect on Win and Mac, but not to the same extreme degree as it does on Linux and especially Arch.
Anything I want different now, I just tell the LLM to do it for me.
(For example I can now close lots of windows of the same type with 1 click, not 3, my whisker search now finds files and folders and I am able to run games that refused before)
I still ocasionally run into the usual linux driver issues, but not for much longer I suppose. I probably could fix some driver bugs now already if I point fable towards it and pay some attention.
In theory I could do all this before myself, but not just like that in some minutes, but in days/weeks/months ..
Drifted off my point a bit, which I guess was meant to be it’s not necessarily Linux-specific training.
Drivers, Coding environment setup etc. are great too and it's nice to have everything logged so the next (more powerful) agent can come and improve the thing once in a while.
Since LLMs have been so successful at finding exploits it's been clearer than ever that the people so obsessed with enshittifying every system with ineffective (other than pissing you off and wasting your time) "secure boot" functionality were really just too ignorant to succeed at actual novel security work, so they focused on this make believe crap. Well, glad that's over.
I am still very happy with my switch to Linux. But if I didn't have the AI help I would say linux is still unacceptable platform for those not willing, able, and excited to get their hands very dirty.
Most llm could do it. Claude went from firmware thread -> rtos scheduler -> mcu reference manual -> hardware controller register interface -> vendor sdk -> problem identification and the solution to it in a matter of 30 minutes. Linux could be even easier since it is so well trained on.
hope it's not run by multiple threads and dlsym is not allocating.
Nobody has a moat.
This is the kind of story that ones tells to investors to justify the huge amount of cash burn. :-)
I think many/most of the players will crash and burn, and the ones that are left will divide the world.
I think that would be a pretty satisfactory outcome compared to one hypercompany consuming trillions of dollars of the world economy.
Internet access is not really unlimited, but for many people with fiber at home, it effectively is and we pay a flat rate.
Perhaps by the end of next year, most programmers will stop thinking about metered access for AI? For many people, the cheaper models (about as good as today’s frontier models) will be good enough.
Which might sound good, but the downside is that it will also be easier to build an AI botnet without the users paying for it noticing. Particularly when people are running AI inference on their own hardware.
My guess is even if the AI market busts there is still a massive demand for hardware as models are solving all kind of problems now.
But ya, lots of hardware everywhere not managed well is how you get sovereign AI.
The net effect is that the most likely scenario is if one big lab fails, they will likely all fail. Their revenues are all correlated.
To go to your dotcom comparison, the winner will be the ones picking through the assets that were written down by orders of magnitude and trying new products with the technology until one sticks to the wall. But I don't know if a dramatic crash is guaranteed either.
Concerning the leverage on energy and real estate: don't forget that the AI companies have quite a lot of choice where to build their data centers. So AI companies have lots of opportunities to play several parties off against each other (in particular also for real estate and energy).
Uhm, what? LOL.
People dont value firms based on balance sheets fella. Have you taken a basic valuation class?
Tesla is a nice stock for traders - they like the volatility. Nobody holds Tesla as stock for investing. If you were to truly value it on an intrinsic value basis you'd have to bring in failure risk.
I would argue those who already rule the world, will continue to do so.
What happens to OAI and Anthropic? No idea, probs go bust. Google just has to offer a half-decent offering in the long run and have a cost-advantage and it'll eventually knock OAI and Anthropic out as firms figure out what combination of models they want to be best for their economics and generating returns. Enterprises trust google over OAI and Anthropic. A clear signal of this was the Apple deal.
Dont forget those sweet returns fellas! CEO's are hired to make the owners wealthier. That is not gone.
The story that some AI company might reach singularity and then "everything will be different" is another science-fiction story that executives of AI companies love to tell to justify the staggering amount of necessary investments and cash burn. :-)
If we theoretically found a way to shield or reverse gravity, things in aviation or space travel would be different. Or if we theoretically found a way to make cold fusion work, things would be very different. :-)
It is in my opinion not a good idea to invest in companies for which the feasibility of the business models depends on the capability of making science-fiction stories work.
Our current capabilities were science fiction a very short time ago, and we are still improving in multiple areas simultaneously (hardware, algorithms, scaling, data efficiency, inference...). We don't really know what the limit is yet.
Reversing gravity seems to counteract the current knowledge of the physical laws, but human-level intelligence doesn't (it has already been achieved once), and there's enough reason to believe that human-level intelligence itself is not a fundamental limit (energy usage constraints in evotution, brain-size limit fitting through the birth canal, etc).
It's also hilarious, because OpenAI had the lead and ceded ground already.
For now, for cloud training. but for consumers, nvidia vs amd reasonably close - the moat there is thin and shrinking. I suspect AMD will surprise us. nvidia has no motes in china, which may be a new source of (gpu) chip design. Huawei's Ascend 910C is about a generation behind... again: for now.
point is: moats dry up. I see nvidia's shrinking as a real possibility.
I don't think anyone has any clue how long it will take for them to have actual functioning EUV machines, but I highly doubt they will do it within 2 years.
Are you sure?
--
China Just Built What TSMC Said Was Impossible
https://www.youtube.com/watch?v=Pk-w279ESHg
--
China Just Built What ASML Feared Most
To be clear, I think China will eventually crack domestic EUV. And I also think their advances with multi-patterning LUV are remarkable. But there's just a hard physics wall of how far they could possible take it.
Right now they are producing 5nm with multi-patterning LUV but yields are at 20%! It's a massive economic loss but they are heavily subsidizing it because they have no other choice until their EUV program is achieved
So far, that's not exactly how it's played out. Humans are still necessary for the leaps in capability or efficiency. A model can grind on a problem to eke out the most performance, and models can synthesize data and iterate on various techniques to find the optimal combination. But, seems like humans still have to provide the real thinking, and the talent and drive for doing that is not concentrated in one company or city or even one country. And, (surprisingly) a lot of the people involved are in it for advancing the field more than making another billion dollars, so they're publishing their research.
So, yeah, the moat isn't deep. Even the compute moat, that OpenAI, Musk, and a bunch of other also-rans (like Oracle) bet the farm on, isn't really panning out. The Chinese makers just spent their effort on making models vastly more efficient, since they couldn't do anything about having an order of magnitude less compute available.
I'm also skeptical that LLMs can ever invent new ideas.
I may be wrong about how soon the curve will flatten, and I may be wrong about LLMs fundamental limitations. But, I don't think it's extremely obvious that LLMs can have novel ideas or can grow into having novel ideas.
They're not there yet. Once they get there, that's literally the definition of Singularity.
But they are getting closer. Recursive Self-Improvement used to be a phrase people mocked LessWrong crowd for using and worrying about, now it's something both OpenAI and Anthropic already publicly admitted not only to pursue, but to already be benefiting from.
So far, I don't think the models are capable of running away on their own. Of course, it would be playing with fire to not at least consider the risks of such a runaway scenario and build in safeguards against it. But, there is no model that can build a better model on its own, thus far, to the best of my knowledge (which is far more limited than the models, so maybe I should ask them).
It starts with what they already claim to be doing - increasingly relying on existing models in non-trivial work related to training, evaluating and optimizing the next, more capable generation of models. As long as the proportion of work keeps shifting towards agents doing more and more of it, and humans less and less, that's RSI at play.
It may be that it turns out LLMs lack some fundamental level of judgement and it plateaus, but frankly I find this notion absurd; LLMs already show better judgement than most people. The alternative is, at some point LLMs will show the ability to futz their way into improvement of the next generation of models even without humans in the loop - even if much less efficient at first, if generation N+1 is more capable than generation N, it'll either take off or burn out.
just because more and more agents are doing human work, that in no way means the model somehow becomes magically more intelligent, it just means the work will stall and continue on at the same level forever
hell even if they hypothetically have an internal model that can output the entire training data set in a better format, there's no scientific evidence that the newer format has new information that is sufficient enough to train a better AI
as a matter of fact the scientific evidence is on the contrary
All intelligence, LLM or not, is bound to plateau around the point where the need to operate within physical reality bottlenecks the speed of feedback. AI is progressing swiftly in the digital realm where feedback is nearly instantaneous, but it's unclear whether that would translate into improvements in the physical world where signals are much noisier and intelligence and judgment are less impactful.
My go-to example: wheels suck at mobility in the natural world. There's a reason no animal uses wheels as their means of locomotion. They only really work on flat, hard surfaces that give a good grip.
Our solution? We didn't build mechanical legs for all-terrain mobility. We paved the world instead. Dirt roads, then brick roads, then asphalt and concrete - since ancient history, we were forcing the world to adapt, so the one means of mobility we could easily built was effective.
We repeat this pattern every time when the world doesn't agree with us, and adapting ourselves to it is too much of a hassle.
Now, AI will likely adopt the same approach to these kinds of problems, which may not end up very nicely for us.
(Note: we are already adapting our own digital worlds to make them easier for AI. Much like building roads for our wheels, we now have a resurgence of CLI, with new tools exposing high-level operations optimized for agents - not humans - to use comfortably.)
With AI it's a bit of a chicken or the egg situation. Before it can ablate the complexity of the world, it needs to be able to deal with it, including adversaries such as ourselves. For this it needs a tool as sophisticated as our bodies, but could AI develop such a tool for itself without the help of this tool? I'm not convinced.
At least they’re led by trustworthy and honest people or we’d need to take their claims with some dose of skepticism.
I can already see the border shift even for mundane tasks I have Claude working on. Increasingly, I'm just setting a high-level goal, and then checking progress and occasionally answering questions or doing something like configuring a system Claude can't easily reach itself (e.g. recording a bunch of traces through my normal use of a system that Claude deemed too fragile to risk operating on its own). Of course, I get detailed instructions to help me - "go there, do this and that, then press this to capture recording, run through this script here to process, attach result to next message". In those cases, Claude is effectively using me as a tool to call.
weve seen some improvement from the LLMs unattended, maybe, but will it actually keep improving vs needing a human to bring it back on track?
the recursive part is that it keeps improving on itself, but we really have no example of that. if it does it 30 times with improvements, then maybe, but even then, to actually be relevant it has to do better than paying scientists to do the work for the same cost, consistently.
RSI still means nothing if it costs 1000x the cost to get the same improvements as a human researcher
This is the nuance that poster doesn’t understand. Given how much money thrown at it - we’re not even close. Who has the appetite to keep throwing more given they continually need to keep raising fresh money?
Money is fake and not a constraint, that's literally the point of capital investments you guys are overindexing on so badly.
> Could vehicle factories be more automated if we threw a gazillion dollars at it?
They already are automated as much as it makes sense. Some of the automation is silicon-and-steel based, some of it is protein-based. Car manufacturers aren't in the business of pushing robotics and nanotechnology, so they prefer to hire protein automatons instead of developing and building their own, so yeah, they "pay people", but think what exactly they are paying them for.
Then consider that this is very much the work the AI is gradually getting as good as, or better, than us.
> This is the nuance that poster doesn’t understand. Given how much money thrown at it - we’re not even close.
What you seem to be missing is that "investing in AI" isn't investing in a chatbot, it's investing in technology that will (and already partially is) sit upstream of every other industry, of everything humans do. Like electricity or the Internet itself.
(The other thing you seem to not understand, in contrast with some of the investors, is that RSI is not a linear walk, it's an exponential curve. X-risk notwithstanding, by the time it's obvious to everyone, it's too late to make money investing in it.)
> keep throwing more given they continually need to keep raising fresh money
Have you heard about R&D?
I'm starting to think that "investors first" thinking that's so common here, that makes people feel they're smart, is actually quite backwards, especially at this scale. Or maybe it's simply people starting with a conclusion and trying to fit reality to match it, no matter how clear of a nonsense that conclusion is?
Not necessary. Scientists are capacity limited and supply limited.
> RSI still means nothing if it costs 1000x the cost to get the same improvements as a human researcher
It means you can replace a human researcher with 1000x their salary burned on electricity. It also mean you can get two of them for 2000x of salary of one,
That's a bargain, actually, even with the anomalous, absurdly-overinflated salaries in top-tier ML.
I you knew it was consistent, then these companies would immediately fire their scientists, and burn 10 000x as much as mean researcher salary this month to be able to 10x their virtual headcount overnight, and then use that to make the 1000x be 500x, then 250x, then 125x, then ... and at that point they'd had all the money in the world, because even more skeptical investors would notice what's going on there.
--
TL;DR: what you all seem to miss is that electricity scales better than people.
The slightly lower Chinese open models are good enough for almost everything, too, and much cheaper. Like with humans there is plenty of employment for people with below genius level IQ's.
Not if the genius level IQs take the market share.
People always compare the inflated API prices, but subscription prices of American models are competitive for the intelligence. You get >20x the subscription cost in tokens.
If anything it looks like the first place is training their competition, through distillation, while not capturing much value in return
---
maybe it's this Anthropic post on GLM?
https://www.anthropic.com/research/glm-5-3-and-the-spread-of...
> Governments should conduct safety testing on sufficiently capable AI models, including successors to GLM-5.3. Without high-quality evaluations from independent sources, the impact of these capabilities might not become fully clear to model developers until it is too late. As AI developers across the world build increasingly capable open-weight models, we hope they work to appropriately safeguard these capabilities and prevent misuse.
I for one do not think my government is up to the task of designing or implementing such a system
OpenAi is alledged to have been monitoring these internally and not contacting authorities. Lawsuits have been filed, I see gross negligence without the gory details
I have for more concerns around human-chatbot maladies than I do around the cyber security stuff. For example, why hack grandma when you can get her to do something willingly through impersonation. How do we prove authenticity in a post truth world?
Custom hardware, data centers, huge cash reserves, deep/broad talent pool, and non-AI customer base are all huge advantages if not moats.
Google, Microsoft, or Amazon are more likely to be the AI leaders than OpenAI or Anthropic.
This is the interesting part to me. People talk about a “SaaSpocalypse” because AI makes SaaS features cheap to copy, yet deeply embedded SaaS still accumulates integrations, data, and switching costs. Gemini is a good example: Google can put AI directly into Gmail, Docs, Drive, Search, etc., where people already work. Meanwhile, frontier-model performance leads often seem to disappear within months. Could model quality itself actually be a less durable moat than workflow and distribution? Curious where people who’ve worked in ML for a long time see the moat actually compounding.
I'm sure the thinking out there, and hence investment, is all about how to tether the user to the most addictive, network-effected, incredibly deep, server-side, moat-able version of AI possible.
Google is already on gen 8 of its TPUs and is certainly already working on the next version or two.
I am not a data center expert, but TPU chip interconnect reaches 13 terabytes per second: https://introl.com/blog/google-tpu-v6e-vs-gpu-4x-better-ai-p...
If not now, then when will these companies be AI leaders?
Even Google, with its staggering advantages in cash, compute, real estate, training data, and having basically invented the field only manages to briefly claim a 1-2 week lead once or twice a year.
Google literally has billions of user that simply integrating it all with Gemini is massive undertaking
sure gemini is not frontier for coding but you know that is doesn't matters for google consumer
Crossing the Chasm book talked about this, innovators/early adopters care more about stuff than later portions of the market https://en.wikipedia.org/wiki/Crossing_the_Chasm
And Chinese labs openly publishing so much of their methodology destroyed any hope, which was inevitable
I think the secrecy doesn't make sense. People swap jobs between labs so I'd say the big players can' really keep secrets for long, and any secret sauce advantage gets incorporated by competitors in a major product cycle at most.
One possible explanation: because Google is a little bit more frugal and focuses on how to make providing AI models financially feasible - combined with some willingness to burn money so that they don't strongly fall behind on their AI models.
On the other hand, OpenAI and Anthropic at least formerly concentrated on building and providing the best models that they could with concerns about financial feasibility taking a backseat.
Just to be clear: I do have the impression that by now (likely because of pressure from investors) OpenAI and Anthropic take these financial concerns more seriously, but nevertheless Google's vs OpenAI's/Anthropic's "DNAs" concerning on what to focus on differ.
There’s no magic there. You get an account executive and a call with a systems architect to find out what you’re doing.
Clouds gonna cloud, this is the reason they rolled deepmind into gcp and arguably the inverse is true, the labs are trying to become clouds
That feels right. It's not as if they've been missing out on great profits.
Google never really has to outpace the competitors (other than to have some relevance) but they have a very large group of business customers using them for business process work in Gmail, Docs, etc.
Clearly they will win when the models are close enough to frontier to be good enough, but are long-term cheap for buy. I.e. they will aim to make it a commodity.
In theory MS has the same opportunity (plus they have GitHub so, you know, dev eco system too) but seem be blowing the strategy.
Anthropic and OpenAI are having to race to the top on ability entirely to keep their name in the media and in front of us all (which costs: hence more recently trying to pivot away from model releases and more into controversy/danger). The main cost is in training and so this strategy is much much more expensive and this will play out either as a huge cost hike or a forced slow down in pace.
I believe essentially Google is betting on that & I think it's probably the right strategy.
Yeah, they have Phi but offer it nowhere on CoPilot as far as I know, I can't even register for copilot, which is bizarre. They came out with "MAI" but... nobodys talked about it since, not sure if its even used by anyone? They're as bad as Mark Zuckerberg is about it.
I do appreciate both Microsoft and Google for releasing small models, unlike Anthropic and (not so) OpenAI.
I'm gonna need you to look at the capex obligations they've undertaken in the last 12 months. They are definitely not being frugal. If they are behind, its not for lack of spending.
See for example the exodus of talent this year, triggered by mismanagement and politics. They still have a lot of talent but they've lost a lot.
Also, competition with Google Cloud for compute resources, less urgency and focus than the competitors, and (strange to say) not as much user LLM behavior data to feed to RL for coding, work, etc.
But I think they will keep catching up and stay relevant for a good class of LLM use cases.
Most Google products even use flash lite underneath, so their frontier model is mostly used for distillation.
A good consideration; just one point from my side: as far as I am aware (but I may be wrong), Gemini is not known to perform well in an agentic framework.
This is no contradiction to your other claims, quite the opposite: perhaps (or even likely) Google wants to avoid that their models become a commodity in some (agentic?) application where the middleman who actually writes this application gets a disproportionate of the money that the customer of the application pays for it.
I used it for a month over the summer, right before they were going through the migration to antigravity. It was a fine workhorse IMO, no complaints from me.
I don't know , if it really finds all kinds of data center optimizations, quantum breakthroughs, helping make their workers smarter and more efficient - stuff an opensource model can't quite do - there's real economic value here no ? perhaps not worth dozens of billions but it could get there really fast.
Because it's not an existential battle for Google. If OAI or Anthropic disappear from the absolute frontier for ~8 months the news cycle and churn will diminish them to the second rate. Google is processing near 4 quadrillion tokens every month, that's - I'm sure - significantly more than OAI or Anthropic, because Google is interested more so in their flash models and getting these competitive, which they are.
Meanwhile, Anthropic/OpenAI will struggle to survive the next 24 months on their current trajectory.
Want to make a wager? I think it's wildly unlikely that Anthropic/OpenAI will not survive the next 24 months
Because they're not desperate. Slow and steady wins the race, at this rate all Google has to do is wait for OpenAI and Anthropic to exhaust themselves on aggressive training, then they can casually amble along right past them.
That's actually a feature. We don't need 2 new Googles.
Slow and steady wins the race. How could they lose to something on the web, when Microsoft owns the web browser itself? Everything runs on Windows and IE. They can just relax and wait for competitors to exhaust themselves, then quickly build their own version. Isn’t that how Netscape lost. Etc.
Now Google is the new Microsoft, just like Microsoft became the new IBM.
When the market keeps expanding, you don’t need to kill your predecessor. The shelf life for legacy enterprise computing is very long.
They would have been right if Google's marquee product was an Office Suite.
Google was the AI company before AI companies were a thing. The comparison with Microsoft and IBM are misplaced because they failed to capture new territory; ML/AI is Google's stomping grounds. The criticism that Google is bad at consumer chatbots is true, but that's not where the real future value lays.
The reverse could also said to be true. Google has models that run with search, producing usable results in well under a second. I suspect the world is consuming far, far more of those Google tokens then the tokens produced by OpenAI or Anthropic.
So why are OpenAI and Anthropic so far behind? They are serving a different market: the one that wants high intelligence / high cost tokens. Google is targeting the low cost end of the market - ie the commodity. That's where they've always played with search, email, docs and the like. That's were they are playing with AI too, and they are killing it.
If anything, their search was the gold standard, and placing sometimes-wrong LLM results above them has hurt people's perception of both Google's search, and AI in general.
Google made a mistake in prioritizing speed over accuracy there.
OpenAI and Anthropic are AI business, if the AI market burst tomorrow, they'd be the first to flounder.
Google just has to keep pace in the AI space, they don't have to lead. Especially since whom is leading changes like two or three times per month non-stop for four or so years now, including small (by US standards) Chinese AI labs with a fraction of the money who keep pushing the tech forward every month while being open for now.
Technically, they arent even a business. One is a business when the revenue model works. People in IT tend to forget that.
Chinese models are barely behind the leaders. Google can catch up anytime they hit the gas. I think they’re intentionally spending less, and when this crazy race burns out they can play their cards.
The better interpretation is Google completely commoditized and killed ChatGPT's initial chat product. If you think about it, Google right now has Gemini Flash running on single TPU instances at massive scale serving almost every android device. I use it every day and it's more capable than GPT-40.
This literally forced Anthropic and OpenAI to come up with new use cases and revenue streams such that now we think the product is actually agentic coding.
I use Gemini Flash every single day without paying a dime. It's the one LLM call on the most, but GPT-6 and Opus are the ones I use the most tokens on.
Data centers are important but a few others also has them: Amazon, Microsoft, Meta. SpaceX will likely be in/at the top I AI dedicated precessing power in 2027 as well.
I don't see the moat. I see a company with a lot of other commitments that is not the best at delivering consumer facing products. They have some good cards but so do others.
Google should be dominating, but instead all we currently have is a disparate collection of consumer facing apps and a flash model that’s fast, clever and expensive.
Muse and Dots are doing what Google should have brought out last year, with their resources and know-how.
Meta was genius in coming up with a new name and offering Muse for free to start. They can upsell you once you see the value. Meanwhile I have 3 existing Google subscriptions to different services and from what I can tell, paying for one of the all in one subscriptions that includes Gemini would cost me a lot more.
the risk of randomly getting my email banned is way too high
That's why I've gone to using open models, they are getting there slowly. A bit much of hand-holding but that's fine by me. If a customer of mine decides to use Google's models, I will have them sign a disclaimer that I'm not responsible of them getting insta-banned or similar. I just can't recommend it.
That's too long to be so far behind. This release looks like it puts them back in it but if they don't ship anything again for a year plus it's hard to imagine building on top of them and watching the world go by.
As others note, this is most relevant for us here, Google does not need to chase YC developers and the like. They can move more slowly, they have the size to do that. But it does suggest a lot of dysfunction given they have the world at their fingertips and couldn't seem to ship anything for a year.
Did they ever really abuse the power Google could have wielded? I could be missing something, but for the most part they seem to just get down to building and pushing technology/science forward and avoid drama rather than welcome it.
However, I don't think the alternative would have made people any happier (and frankly they probably would be justifiably even more angry)
Nothing would be "free", so google sub, news source subs, reddit sub, youtube sub, discord sub, instagram sub, FB sub, app stores are full of paid only apps, podcasts are all paywalled, all the ad supported stuff is subscription supported. Everything would feel like constant nickle and dime'ing you.
On the bright side, there would be no ads, and relatively robust privacy.
https://grapheneos.social/@GrapheneOS/117282080803799576
> Google should not be gatekeeping security patches to the standard Android platform code from Android OEMs but that's what they've started doing.
The whole Android developer verification program controversy.
These 2 are the recent things that come to mind that are most adjacent to "power-abuse".
Is this a sarcasm?
Plenty of times. Perhaps the worse is pushing Manifest v3 with no support for real ad blockers in Chrome. Not to mention no support for ad blockers in Chrome mobile.
Also, their stealing of newspaper content.
Their new push for AI summaries instead of actual search in their search product, stealing even more clicks from sites that actually produce information.
YouTube pushing Shorts to everyone, and now the weird text posts that you can't hide.
In general their gigantic push for ads everywhere all of the time.
That's "trustworthy" to you?
The "Do no evil" never being an official slogan and people saying it's no longer true for the last 10 years or so, they're just really massive. They do mess up a lot, but when you're Microsoft, Apple or Google sized, its nearly impossible to get everything right all at once.
See them shifting now.
(Aside: I didn't appreciate the revelation!)
If you continue, you need to log in.
If you continue, the account is blocked in less than 30 minutes.
If you try to use cloud machines you are blocked before the first video. If you try to use known proxies, there are already tar pitted or blocked because everyone is trying.
You are dead wrong if You can stop Industrial scrape
It's a numbers game.
I call it phone farm and this is individual/small group run
imagine they got access to more money
https://finance.yahoo.com/news/click-farms-internet-china-15...
I used two IP addresses in a datacenter. Both were blocked after a few hundred videos, but the block expired after a day or so. It is not as hard as you make it sound.
Gemini wins and I said this since the beginning. Google wins in general. I never understood why they have not been the highest market cap companh for the last 10 years. And I despise google but its obvious.
Google makes AI profits, its in their breakdowns. And Google has been releasing models every few weeks now, they have a faster iteration pace than the competitors, so they fixed that, they wont be behind in coding models any longer.
What "AI profits" exactly. Their capex is through the roof. What they're actually earning is in cloud , their own AI is lossy for sure. Who is paying for Gemini? Not many people.
Those datacenters are mostly cpu. LLMs run on gpus.
It started to feel just a bit like they were potentially content staying in the effective-fast-cheap lane and ceding frontier models to frontier labs, but I don't think anyone seriously thought that Google was going to sit back and let this generational shift pass them by when they're so well positioned to remain on the top of the heap.
If anything, I thought that 4 was maybe coming up short or causing safety concerns that were forcing them to be a bit more cautious, but nothing packs a wallop like being able to launch ahead of everyone else's brand newest toys...
Working through this as a consumer and enterprise customer has been a confusing experience, to say the least.
Google's biggest problem is its turf wars. Internal battles for territory always seem to outweigh efforts to expand markets
AI is strangling their other major business and they don't like that. Unless Google sacrifices that branch of income, they won't put their full force behind AI.
and paying enterprise customers.
I think that has been well proven so far
And Anthropic has still generally been eating their lunch at the top end.
Already businesses that have more compute and access to data seem to eat the world around them. If, and ya its and if, we can make something that self learns into RSI it's not looking like any business that came before this.
If there are multiple they will cost money to run. In that world I expect there to be a correlation between costs and quality, i.e. the highest quality AI system will likely cost more to use than a lower quality AI system because there will likely be more compute required and so on.
So in that world, the absolute top tier best in the world frontier AI will not actually be the most used system. This is for the simple reason that such a system will be more costly than a lesser tier system that can do the job just as well.
Do they? Business valuations are more a reflection of what people believe will yield returns than a reflection of reality. A lot of big tech behemoths could disappear from the face of the Earth overnight and it would make little difference to our lives.
Natural resources and energy are the actual backbone and it is a true catastrophe when these are disrupted. In comparison, disruption to compute or data is just an inconvenience.
Whoever builds the deathstar wins!
https://en.wikipedia.org/wiki/Synanthrope
https://en.wikipedia.org/wiki/Category:Species_made_extinct_...
Anyway seems stupid to me
Mistral / Europe just doesn't want to be in the fight.
This seems to match what we are seeing where Chinese models from companies with only a tiny fraction of the compute are able to be hot on the heels of the frontier models.
You could say they have a consumer market moat but all it takes is for a Cerebras to release a USB-C plug-and-play appliance.
This is marketing from Google, not a competitive offering
Or maybe everyone makes money for everything.
The world could turn into a world of plenty. Or it could become a YouTube popularity contest where the MrBeasts get to eat and nobody else is interesting enough to sell themselves.
This is an absolutely crazy time to be alive and most people still don't see it.
WW2 ultimately brought massive wins for everybody who didn't die in it or otherwise suffer directly. We are now on the verge of throwing away many of those wins, but that's neither here nor there.
Wartime progress in everything from radio to radar to rocketry has led directly to modern communications tech including satellites and microwave links.
When WW2 started, a "computer" wore a dress to work and was "programmed" to watch meters, turn knobs, do a lot of arithmetic by hand, and avoid asking questions. When WW2 ended a "computer" was a machine with a rapidly-increasing number of uses.
Before WW2, aircraft used propellers, flew slow and low, and crashed a lot. After WW2, the jet age ramped up practically overnight thanks to experience gained by all sides.
And yes, we got pesticides, herbicides, synthetic fuel and rubber processes, mass-produced antibiotics, probably a bunch of other stuff I'm not thinking of.
Then there's the whole Pax Americana thing, which is the part we're busy throwing away right now. It led to a relatively peaceful, stable global environment in which new technologies could emerge and spread without the need for a world war to inspire them. Now that America has basically been subverted from within, we'll soon get to find out whether that was a good thing for the world or not.
We created nuclear weapons and yes it does seem to have given us 80+ years without another direct global conflict. That's a good thing in the short term, but what about longer? Are we to expect that nukes will never be used? Are we also to assume that the invention of nukes doesn't incentivize people to invent an even worse weapon?
The same goes for all the others. Computers have allowed us to do amazing things, there are also many in the industry warning that they may one day replace us - some even want that to happen. I don't know what benefit you can point to for pesticides and herbicides, maybe if you look at how much tonnage of food is produced and ignore all the terrible side effects of polluting the planet with synthetic chemicals that specifically end life? As far as Pax Americana, maybe its been a good thing? I don't know how to distinguish between the relative peace from the threat of nukes vs the threat of economic warfare, and our K-shaped economy does seem pretty directly related to the incentives created by Pax Americana.
My point isn't that all of these are bad, or that there aren't some good from any one of them (again, except for maybe all the poisons). My point is that we don't know what the results will be on any meaningful scale - 80 years is a drop in the bucket when discussing life on this planet.
Not without technological intervention, anyway.
I must assume that you don't find arguments related to climate change compelling given that they require considering scales beyond 80 years. If you are concerned with climate change I'd be very curious how you square that circle.
If LLMs can replace most humans at most cognitive tasks, what do those working in various cognitive jobs pivot to?
> claim is that LLMs [..] will be better than humans at most/all cognitive jobs
First off, what does this even mean, to "be better" at a "cognitive job" ? If you counted up all "cognitive jobs", that's probably a hundred thousand unique positions done by a billion people.
Think about software engineering. That is one job. It has taken years of work, hundreds of experts, billions of dollars, and the combined effort and collaboration of probably 20% of the SWEng workforce, just to get one job to work through AI in a way that isn't total slop. It still requires humans to review all the code, because it fucks up not only the code, but the end result / use case. It will also lie, cheat, steal, etc, just so that it can claim that it did the job, when clearly it didn't. Oh, and if you feed it some data that includes a statement like "delete all data", it will just do it. AI is an idiot savant. Writes brilliant music, can't tie it's shoes.
So just for one job, you can't replace a human completely, because the AI simply isn't good enough and can't be trusted (and due to its design, that will never change). Now think about the 99,999 other jobs. They are completely different jobs. All the training and investment that went into making software work, isn't going to apply to, say, a travel agency planning a trip for two to Europe. Or any other job at all. You still need to train it for that work too, and then review its work.
So let's get this straight: AI cannot replace a person, for probably any job, anywhere. Because it isn't very smart and it can't be trusted. Just like you cannot replace a person with a sewing machine. Someone has to think up a sewing pattern. Design it. Figure out how to make the pattern with a sewing machine. Actually perform the sewing. Quality-control-check the results. The sewing machine made it much faster and less error prone. But it's really only about 1/5th of the total work needed to make and sell finished garments.
Every single job in the world is like this. A machine can make it more efficient, but because it's not actually intelligent like a human, it cannot completely replace a human, it can just augment/improve their work. AI is just a tool, not a person.
Hardware is the real differentiator. Not everyone has billions to make more advanced chips, and only a few companies can make them anyway. Both OpenAI and Anthropic would already be dead in the water if we had cheaper GPUs, because we'd all be running open models on local machines with 8 graphics cards. They're gonna have to force a hardware shortage to prevent a collapse in 2-3 years. My guess is it'll be tariffs or import restrictions or licenses to buy newer hardware.
With that said, there are some other moats and people are building them: training capacity, inference capacity, brain capacity (literally buying the best researchers and keeping them tied down), harnesses/subscriptions, etc...
There's a longer talk I give on this but briefly we are in a time period where the development of the technology is quickly outstripping people's needs and they will be satisfied by models runnable on commodity sub-$10k machines.
That threshold has arguably been met for many users over the past 6 months and it will continue to be met for the majority of use-cases in the upcoming months.
So not only does Anthropic and OpenAI have no moat - unless they have other compelling products, the demand for their token-based subscription and metering products isn't sustainable because consumer preferences go elsewhere after any product quality reaches a sufficient baseline.
Luckily there's many many options. Faster, cheaper, stronger they're there but these are all zero-profit condition qualifiers. The labs current push, and this is across the board, is to have a compelling suite of applications where people will have a preference for them or there will be some side hustle. Look at Meta muse - they have an app for your phone that will hoover up your personal data in the name of convenience. 5 million people said yes in 3 weeks.
Gemini not beating the "can't release a model" allegations
ok bro thx
What is your reason to believe this is not a bug specific to a small set of Pro users?
My free gmail account is not.
Google just takes months to roll out their models to everyone.
3.5 Flash-Lite
3.8 Flash
3.1 Pro
3.6 Flash
3.6 Thinking
3.1 ProThe excuse was it was still “in preview” and the enterprise didn’t opt in to the preview channel.
With Google, it’s either in beta or deprecated.
They even gave their model a random nonsensical name suffix simply because OpenAI is now doing it, too. Monkey see, monkey do.
I was going to say I don't know what they'd do for C, since Carbon and Calcium are already things. But knowing Google, they'll probably call it Chromium.
Opus 5.5 and Sol 6.1, literally state of the art (in their respective class), were just released without any prior announcement. This has pure and simple become a Google thing.
They made their model so hard for people to drop into workflows, that people just... didn't.
Lack of adoption caused them to lose out on usage-based training data needed to smooth out their model's rough edges and progress the frontier.
Google gains very little by having us use their models for coding. It doesn't help them in their core mission ("to organize the world's information and make it universally accessible and useful ^W^W^W^W^W^W[and turn it into ad revenue]"), nor would it be anything close to substantial revenue compared to the ads revenue firehose they already have.
They will push AI in a direction that serves their existing goals and service the coding agent model only as is necessary.
If Googles goal was something other than those, why expend Billions in time and effort? Alternatively, they want a part of the regular model use pie, they just struggle to compete. QED, there is no secrete alternate goal here.
There's likely all kinds of internal competition and disagreements and dysfunctions.
So there's that.
But in the last instance, revenues win. OpenAI and Anthropic have to fight for new revenue sources while Google already has a firehose. The motivations are fundamentally different.
I already pay $300+ for subs. Please don't tempt me with another $100 sub just because I got curious if the benchmarks were right.
In this space, any other company that I respect other than DeepSeek is - that would be Google. They had been honest about it from the get go including their infamous "we have no moat" memo.
This company has enormous data, their own hardware (TPUs) and their own in house experts. Actually, LLMs are invented here.
Good addition to the arsenal.
So while this announcement has no details about the quantum algorithm optimization, I feel fairly confident that it will hold up.
A guy at lunch today asked me when a feature was going to be built on the tool I'm working on. Turns out it had built it last night at 8:30 when I was hanging out with my girlfriend. Welcome to the future!
But it still takes 2 weeks to get a CL approved and past TAP.
I understand we have the pesky problem of work and time being interchangeable for money. But except for that, it's pretty exciting.
On the other hand, unlimited intelligence democratises products and services in a revolutionary way. Everyone will be able to do whatever they like in digital space for basically free. No more purchasing software. Make any app/company/website/blog/movie/show/game/forum you can think of. This will quickly bleed into the physical space, with real-world products and services becoming logarithmically cheaper. A true future of abundance.
The big problem is going to be our social and economic transition. Are we going to wait until unemployment is that 10% before we act? 20%? How bad are we going to allow things to become before we implement universal high income? I say universal high income because white collar workers are not going to accept being relegated to an income of $1000/month forever. To achieve universal high income requires eye-watering taxes on the major benefactors of our new economy. They won't accept that without a fight, and it our current fractured political climate, what would it take for everyone to agree on a unified direction?
It seems like all software will get replaced it by local versions. Suppose I am an individual or a business that was using a SaaS product (such as JIRA).
Let's be generous and imagine that the monthly payout was $20/month for that software (usually for businesses, it is much higher) and one fine morning I login and point a multi modal AI agent to it and clone the whole thing for my local use in let's say 3 months worth of $20/month AI subscription.
Now what? Gradually, that software company would go out of business but that user won't be subscribing forever either to AI inference service and the only constant customer of AI inference would have been the software company which went out of business so... No software company, no AI inference business either?
Some companies will retain a moat. For example, YouTube holds incredible IP, and until AI can replicate that wealth of content at scale for cheap, it will remain profitable. Facebook has the user critical mass. Increasingly, hardware for inference is going to be a moat. So is electricity.
Which loops back to my comment above: this breaks a lot of the market dynamics required for a functioning economy. Accelerationists are convinced that we'll invent new jobs to replace the old jobs, but I don't think that's going to happen. Certainly not at the pace of change we're seeing.
Data as in YouTube is all just data. Building a website and app around it isn't the hard problem. Anyone can launch another YouTube in few months but that would be pointless.
My investment thesis is currently around these strata:
1. Electricity. All analysis shows massive shortages beginning now with no end in sight. Western governments in particular have massively dropped the ball on this one.
2. Chip design and production. Nvidia and TSMC dominate here for a few reasons. Partners required in the supply chain like ASML benefit, but they're not really the bottleneck right now. We're seeing a lot of investment pouring into this space and we'll see competent design competition (especially from Google's TPUs, and potentially from Apple and AMD). Chip fab is very difficult to get right, and takes a long time to ramp up. See the NAND space.
3. AI labs. These guys are doing to get commoditised. They're trying very hard to prevent distillation but I think it's impossible. The open weight models are not far behind, and the Chinese labs are getting very creative about how they work with limited hardware. If anything, they'll decrease inference cost over time, reducing the value of existing data centers (of which OpenAI and Anthropic have purchased an enormous amount). I am very bearish on the future value of OpenAI and Anthropic.
4. Software. So much software. Everyone gets software. The companies which benefit from this will be able to monetise it. Google, for example, might put ads into VERY capable and free AI. People might accept that. Microsoft has been investing heavily into their data centers, and maybe that's their revenue center in the future. Especially securely hosted services. I expect massive growth in this space in the short term. As long as people still have high paying jobs, they'll pay for well made software and tailored services.
Here's the question. Which high paying jobs will remain? People will still be needed, obviously, but salaries would be in a race to the bottom without some form of govt intervention.
Edit: UBI being pushed by asset owners is a step towards Blade Runner rather than Star Trek.
This artificial control works to prevent oversupply and protect salaries. Unfortunately, the businesses calling the shots won't ever allow this to happen.
I would say, be afraid and use the fear (not to ruminate, but to organise and do things), but also be optimistic, and use that, too (not to imagine what people will do then when they're not doing it now, but to believe it's possible helping making it reality, now).
If genuine AGI is developed, its everyday conveniences may continue to be shared with the common masses, but the powers that own the technology and the data centers will have no incentive to give everyone access equal to superintelligence, and that includes giving governments the same access.
Every stage of the industrial age has seen powerful, wealthy people/groups (whether you call them oligarchs or industrialists or whatever) become able to exert disproportionate power over the state, and we're already in a place where this is happening. Humans have shown time and again that once you have some resource (whether it be oil, gold, or data centers), someone will want to own all of it, and that they're always willing fight for that ownership, and this fight rarely benefits ordinary humans like us.
Developed countries have been enjoying an unusually long peace time thanks to the wonders of capitalism and the emergence of stable, progressive capitalist-friendly democracies. The road to utopian basic income and limitless leisure is going to be fraught with riots and violence, and make Marx suddenly utterly relevant once again, because there's too much at stake, and too stark a division between the haves and the have-nots.
The alternative timeline is one where governments get together to regulate AI into some kind of international agreement under the UN. That requires taking power away from the private corporations; historically, that's been a tough sell.
If AGI ever comes to fruition, it won't come as a subscription plan.
This isn't happening yet, SaaS is only growing more in the era of AI. Anecdotally, my own small SaaS has only grown instead of shrunk as well.
https://www.stripeeconomics.com/p/the-saaspocalypse-was-more...
Now we will finally be free, without having to make hard decisions. It will be decided for us.
That's not true for many unconstrained real world scenarios, and LLMs currently perform poorly there. Another breakthrough is needed to overcome this.
I do still love technology and I'm a builder. I'm also neither a tech optimist or pessimist, but an inevitabilist. It's coming.
The way I see it is that history is going to advance whether you like it or not and you might as well ride the wave.
I understand I'm lucky to be insulated from the crisis a bit, though. I do think once SWEs lose their jobs every other office worker is going to follow, so we're in the same boat.
i'm actually curious on what your view is despite the snarky tone.
If you looked at that arrangement and said "we don't need tech leads, we just need nontechnical managers and junior devs" you'd be sorely mistaken.
I do anticipate a future where it will be better than me at applying taste, knowing best practices, and having domain familiarity.
It's coping, the truth is writing code by hand is dead.
You have a future as a fortune teller, good luck!
> the truth is writing code by hand is dead.
don't forget to take the proper pills dhh!
You're so skeptical of it that you're willing to make fun of people?
It's about as much in question as whether or not I'm going to eat breakfast this morning.
I'm gonna have eggs and toast. Who am I, Mrs. Cleo? How could I possibly know this unknowable future?
IDK how it could be otherwise. It's moving too fast and gone too far already.
Ehm no? Models have plateaued, "harnesses"(TM new buzzword) got better and with many integrations
AI psychosis is very bad
I'm not saying you're wrong, but, I'd be shocked if you were still so adamant (assuming you're rational and not in your own psychosis).
Here's something I'm about to drop on AI. Like 90% of things like this, when I ask for a fix (with a few skills), get fixed with fewer edge cases left dangling than I'd have left if I'd spent all week on this stuff. And, it'll be done before tomorrow, with no further input from me. This is a list I put together in about 1 hour.
Things to fix with OCR text labeling
- vertical line in center breaks box-drag
- nope, not line in middle... IDK why, but the drag pretty frequently snaps back to original position in middle of drag... lets me keep dragging, but have to start back from there
- happens with "draw stamp box" too
- forces me to re-click the button to start over
- add zoomed view of text in bottom right corner of video
- add ability to restrict OCR
- current job is 0 1 2 3 4 X 6 7 8 9
- 5 is an X, for some reason
- is detecting full alphanumeric
- add ctrl+z undo for modifications to drawn box
- deleting character in middle of "your label" text box jumps cursor to endWhat I'm saying is, with just the effort it takes to notice and somewhat-sloppily mention things, they get fixed, and usually fixed well. Further testing will tell us whether it needs more work or not.
They may have never had these troubles at all, if I'd coded by hand originally, but then, this particular app would've never been written, because it wouldn't justify that amount of work.
Man, I remember back in the days when the cppnext team was refusing to even consider Rust, instead looking at absurd stuff like Carbon and Swift (!), even though half of the engineering staff already knew where this was headed. I hope they got a few good promos out of the delays at least.
Also the internal tooling culture there is just insane. I'll never forget the day the last SUPER_ESSENTIAL_TOOL was marked as "Deprecated - do not use!" while the only replacement was still marked "Pre-release -- use at your own risk!". I can't pretend to know what dynamics led to that cause it was so far from my org, but I can't imagine they were healthy ones!
I was excited to see what it would be. But I don't think I can argue that it makes as much sense anymore.
The only somewhat realistic proposal in this space is Herb Sutter's cpp2, which is arguably a massive improvement and I'm puzzled why nobody in the standard thought to give it a spin, there's just to much cruft they'll never be able to get rid of unless they make an alternate yet backward compatible syntax with C++ that changes the defaults from "random 80s nonsense" to something better
I don't think Carbon is dead, it just all depends on how easy it actually is to rewrite "all of C++" in Rust. (The jury is still out on this one, but it's not looking good.)
It has been the social media that has given Carbon a roadmap that the team never communicated.
As for Cpp2, it was yet another C++ wannabe replacement, sold as if it wasn't, because the chair of ISO C++ at the time, naturally could not be seen as yet another one coming up with C++ wannabe replacements as well.
Not saying you're wrong, just curious if there are numbers backing this up.
The best models can still make sense of it[2], though the tasks so far have been pretty basic. But I do think it gives some evidence that languages which aren’t well represented in an LLM’s training can still be reasoned about and written well by LLMs.
It's DOA because Google doesn't have any idea of what Carbon should be, and to be completely honest, at least 80% of what they currently use C++ for should be rewritten Go, you know, that language developed specifically because of the issues with C++ by teams within Google.
> Existing modern languages already provide an excellent developer experience: Go, Swift, Kotlin, Rust, and many more. Developers that can use one of these existing languages should.
So the reason for Carbon to exist is gone. C++ code can be migrated straight to Rust without Carbon's stopgap.
LLMs are very good now, but they are still stochastic (when temp > 0), and Google has a lot of code -- i.e., many rolls of the die.
Does it deliver?
Can all existing C++ code be ported to Carbon without refactoring?
LLMs are fuzzy when generating, but such conversions aren't done one-shot. Review and test feedback loops are there to catch the random errors. The frontier models are getting good enough at this.
When LLM is instructed to generate idiomatic safe Rust (rather than literal 1:1 unsafe translation), it benefits from a lot of feedback from the compiler.
Whatever QA there was to ensure C++ was good enough can be applied to the Rust version too.
Right, I'm also talking about making the new Carbon code behaviourally equivalent to the old C++ code, i.e., bug-for-bug compatible (except w.r.t. C++ bugs caused by UB).
> Whatever QA there was to ensure C++ was good enough can be applied to the Rust version too.
The point is that all that QA over the years was a vast amount of effort by highly-paid Google engineers, which could be (mostly) avoided by mechanising the conversion as far as possible, which is something that Carbon's approach can do and LLM translations can't.
I'm not against LLM translations per se. Ultimately it's an engineering decision like any other. I'm just pointing out that, similar to the benefits of using a strongly typed language over relying purely on tests, using an approach that guarantees to eliminate a large class of possible problems has many advantages, especially at scale.
It was clearly done because some PL guys at google really wanted to make a new cool language and Google was the perfect place to incubate it without it getting axed. Probably got a couple of promos out of it too. This is clearly not the best use of time or money, but I guess if you're google you have so much of both it probably doesn't really make a dent, and you can keep a few very smart people happy with shiny new projects.
Also, LLMs being used for a large portion of coding nowadays sort of remove the need for these types of languages, IMO. They make less "silly" bugs (both logical and structural) that languages like this are meant to catch, and they are much better at languages that are better represented in the training corpus. This somewhat obviates the need for very niche "type/dummy-safe" languages like carbon (and even rust/zig, imo). So even if you did want to use Carbon, you'd likely have to bootstrap a decent amount of your own "good" carbon code to post train an LLM, and even then, it likely won't have that big of a gain vs just having an LLM write C++ or even Rust. If you are a company that still reviews code, you should just have an LLM code in a language most people can understand anyway to make verifiability tractable.
I personally think that you _could_ use an LLM to catch these types of boundary case errors without having to port the _entire_ C++ codebase to Rust, but maybe pre-emptively porting to Rust now can catch some of these cases for cheaper than doing a full LLM sweep. Also more cynically, its a good benchmark lol.
I guess if you really believe in curve of LLM capabilities you should just use a language that has the best performance, safety, flexibility, and extensibility, since in the limit few/no people will actually read the code anyway. I think this ends up being Rust.
The other thing is just that rewriting some old human-written codebase in Rust probably immediately catches many bugs. It would be hard to prompt the AI to properly scan for such bugs itself, they're lazy when working in that modality.
Going back to C++ would be particularly bizarre to me given that AI is also very proficient at verified languages. Not merely typesafe, but languages comprising their own spec languages such as Rocq and Lean.
I predict that in the next decade: (1) the market will understand the difference between a "code writer" and a "spec writer," with (2) the expectation that the latter is overwhelmingly more necessary than the former in an AI-dominated field, and (3) there will emerge more useful and less mathematically specialized formal verification alternatives to Rocq and Lean, and a filling-out of the tooling gap of between "static typing" and "interactive proof assistant," perhaps in the vein of ACSL-like contract annotations, and (4) there will be a subsequent shift in the traditional curriculum for programmers. Since educational change is slow (and spec writing depends on good coding fundamentals anyway), perhaps (4) is a stretch, but I'm more confident in the first three.
That feels like a really strong conclusion. I’m not clear on why any safeguard isn’t a useful safeguard if you let agents write all the code.
They did that for Go and it seems to have worked out for them though.
Google themselves to this day aren't big Go users outside Kubernetes ecosystem.
I’m not entirely sure Google should have both Go and Carbon but when you have billions in server costs it makes sense to do extreme stuff for even basis points of performance. I’m still surprised at how much java there is.
Maintaining a language meant for web servers is also objectively a much easier job than something like C++/Carbon which is supposed to be at system level, general purpose and has huge standard library.
By comparison, virtually nobody is using Carbon at Google for production code.
The counterexample, ironically, is Flow. It's practically irrelevant these days -- many (if not most) of Facebook's open source projects use typescript.
Such rewrites will contain judicious uses of Cell, RefCell, unwrap() etc. which make for ugly code that's not exactly simple to understand and might even have some landmines (crashes).
Getting rid of these requires a subtantial amount of engineering effort, which I'm not sure how well these LLM manage.
Given the nigh-universal experience of LLMs producing an ungodly mess when left to their own devices, I have my concerns.
It’s absurd to think that Carbon is the solution to memory safety when rust exists and Carbon’s memory safety story is basically “TBD”.
And static analysis + agents are good enough at keeping the memory management in check. Compared to Rust, there's no magic so it's easy for devs and agents to reason about.
If you're curious: https://github.com/okcontract/oksolc
Many pieces of software are going to be just blackboxes worked by AI. You will be maintaining output quality and stability and that's it.
Maybe we should let them do their thing and instead focus our attention on the higher-level stuff like specifications and testing.
https://www.ll.mit.edu/r-d/projects/translating-all-c-rust-t...
I'm not saying we blindly vibe convert Linux to Rust, but I think it could be a valid idea to start converting small parts and carefully auditing them.
I am hopeful we can bring in an age of much more efficient software rather than fully prioritizing developer convenience.
and start everything all over again? current c/c++ tools have been audited for years
Imagine they aren't even familiar with rust but are deeply familiar with the product.
So ... Rust still can't beat the C++ implementation :-D
Sorry, didn't mean to ignite a langwar, but it's still interesting to see.
Of course, Rust is not a magic, so just porting to Rust wouldn't make this performance improvement. It seems their AI overfitted code to Rust compiler to find safe Rust code that compiles to efficient assembly.
But there are other criteria. The move to rust might've also resolved numerous potential memory-safety bugs in the decoder.
To me this is way more significant than other random c++-to-rust-AI-rewrite. If they can pull it off on core C++ libraries en masse, I don't know if C++ will still be relevant in a few years.
I look forward to a post from google on this effort.
The standards body members are still fighting about whether memory safety is important enough to change the language for, so, I would guess the answer is "no".
[0] https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2023/p27...
This year, maybe next, maybe a year or two after that, is probably the most C++ lines of code in production use there will ever be. Why would one choose C++ for new projects at this point? There are niches where Rust is still uncomfortable or just doesn't have the support, but not for much longer. Models are very good at Rust and good at porting to Rust. And, Rust is a good language for models because it is so strict...it helps keep them in line.
Let's put [Citation needed] on this and let's assume that your personal opinion bubble of developers doesn't really represent the combined worlds software industry. Mind actually proving that there's a significant decline of use of C++ outside the hype crowd?
> Why would one choose C++ for new projects at this point?
Why indeed would you choose a language supported on every running platform on this world with a massive library of supported dependencies and mature compilers developed by stable teams.
(I'd probably also personally choose Rust for new projects, but let's make this a practice in "not everyone is like me" empathy.)
Which taken to the logical conclusion will mean its disappearance.
Except there are plenty of industry standards that only acknowledge C and C++, and safer languages that depend on them for their own compilers, including mighty Rust.
There are billions of lines of C++ code in production use. That doesn't disappear overnight, and I've never said it would or even suggested it would disappear overnight. But, the relevance of C++ for the average developer seems likely to never be higher than is it today, and the amount of code written in C++ vs. safer languages, seems likely to decline.
You're arguing with a different assertion than the one I've made.
COBOL latest ISO version is from 2023, by the way.
And people are worried about human extinction when this is the potential trade-off!
C++'s death cannot come soon-enough.
Seriously though, things have changed so incredibly rapidly in the past year or so. I have never been such an efficient or such a proficient engineer than I have this past year (delivering feature after feature, project after project, faster and better than I could before with better feedback from users etc) and I don't even see the code any more. It could be c++, it could be python, or java or what ever - I don't really care any more: the computer deals with that trivia while I concentrate on what to build and how it should work.
Its amazing. It really is.
High level languages :
- compress low level patterns into high level abstractions which save context/token
- express higher level meaning through things like names
- express and enforce constraints through type system
Why would you take something compilers are really good at (translating high level concepts into low level hardware instructions) and bake that into LLMs
If anything LLM programming language will be something like high level IR that's token optimized, but honestly the AI labs are so lazy (or bad) at fundamental engineering (look at their sandboxing solutions LOL), they are going to keep pumping training on shell/python/rust RL environments.
These things are hard for a human to do, but easy for a machine to do.
We are not there yet, not in 2026. Maybe in 2030?
Rate of change is increasing too fast.
(Full disclosure: I am one of the coauthors)
I'd say so! We aim to have our replacements to be `forbid(unsafe)` - no raw pointers - outside of `ffi.rs`. Of course it's tricky because we need to interoperate with existing C code, but we've been trying to come up with abstractions that make this a bit more ergonomic (https://github.com/google/safer_cffi).
I know this is somewhat of a meme now but I do think the predominant language will be "typing/speaking to a coding agent in your native language."
Edit: seems I was wrong about Anthropic restricting Fable, I guess our enterprise plan doesn't include it. But, the block from Anthropic regarding Mythos for regular subscribers/enterprise-users is still true I think.
That's a lot already and growing quickly.
Tech companies are capital intensive. AI ones especially. Not a surprise that they don’t have GAAP profitability after 5 years.
For the record, growth plays have been common in Silicon Valley for many decades. It's how we ended up with all the major tech companies we see today. Of course, it's high risk, high reward.
Everyone is cutting back their AI spend because of the ridiculous cost, and a price war is emerging between AI companies.
Your guess is as good as mine on whether these companies will continue growing, stall, or decline. Don’t try to pretend your methodology is more sound though.
Lots of people seem to love to do the simplest extrapolation that benefits their point. It's the same with "AI is improving at rate X, therefore if we linearly extrapolate, we'll hit AGI in a year."
Honestly, who is in charge of this? How do people even subscribe and make sense of any of this?
This is good, but they're the slow mover due to this exact thing.
Google is getting punished for not letting the models enter an echo chamber and go faster than humanly possible.
So no, Google is not being punished, nor are they the people behind this technique.
Of course, this leaves the possibility that the best methods for solving generic problems obfuscate the chain-of-thought. That would be unfortunate.
The worst case scenario is that the easiest path forward is one where we lose sight of internal thought.
they use a small model to make fake chain of thought and return that.
google has access to the real chain of thought.
Or they may choose not to because the companies are massively overvalued and they have their own AI technologies / technologists already.
In order for the benefits of AI to be distributed, intelligence has to become a commodity.
As long as you control the skills, the learnings, and the infrastructure setup you will be fine.
And, no, wrapping claude -p or any other “allowed” use wherein you don’t control the agent loop isn’t the same.
I log everything - user messages, tasks, project memory, even bash commands for forensics. As a consequence you can do reflection where you analyze past work and extract refinements for the harness and realign the project when it diverged from user intentions. I don't have to do this manually, it reduces steering work.
Or using it in a way that's replaceable I guess, not having your entire work only ther, as your main machine.
I could make it work if I didn't care about access to latest reasoning model capabilities, but then my customers would no longer be interested in any of this.
I tried the DIY provider agnostic harness thing and it performs like shit compared to what OAI, Anthropic and Google's engineers have created. I don't have a trillion dollar AI budget. I feel like these comments are sometimes written with the assumption that the reader does.
Just ask AI to make it perform not like shit. /s
The various vendors behave differently enough to make a simple code contract swap largely infeasible for many kinds of domain-specific agents. I agree there are lots cases where this does work (probably most of them), but you sacrifice performance in the targeted scenarios where you need much more specific alignment and control.
The biggest example where this breaks down is with computer use and vision. I cannot take a targeted solution that works on OAI's stack and forklift it over to anthropic (or google) and expect anything to perform correctly. Some things will work some of the time, but it's basically starting all over again with alignment each time you move something this complex.
Having a generic interface for executing shell and writing code in an iterative ecosystem is not a hard problem to solve. Having a generic interface for reliably inspecting specific PDF documents in a very particular way in a single shot is a much more difficult problem to solve.
If you follow this argument to its logical conclusion, you will wind up implementing a provider-specific version of your tools. And the ones that demand this treatment will be the most complicated ones.
- Agents live in their own directories, for example ~/Agents/[project]/[instance]/
- In a tmux session run each agent in its own window and directory. Pi, OpenCode, Claude Code, Codex, a combination, whatever works best at any given time.
- Tell them that they are working with other agents, and that knowledge is shared and stored in an agreed format. They are very good at using OKF[1], for example. In my actual setup I use a harvester agent whose only job is to ensure the quality of the knowledge.
- They can communicate with each other simply by sending messages to each other's tmux windows with tmux send-keys. If the team grows, or you work with other people, you can use a general messaging system (I wrote and maintain https://github.com/awebai/aweb but I am sure there are others).
Argon will launch at an introductory price [1] of $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price.
[1] After the introductory period expires, the price of $4 per 1M input tokens and $20 per 1M output tokens will apply.
So, they are basically offering opus 5.5 pricing. On AA, it scores around Sol 6.1 level (53) with avg cost per task $1.99 (https://artificialanalysis.ai/models/gemini-4-argon#cost-tab...) which is higher than Astra high ($1.73), Opus 5.5 high ($1.82), Muse Max ($1.60) and way higher than sol 6.1 Max ($0.72).
And this pricing is their 'discount pricing'. Add that to AI studio and Vertex's famously terrible caching, it is hard to see this as competitive. Google somehow is getting terrible advice on pricing (see also: the flash pricing fiasco)
But good to see more competition. I would happily take a 4 horse race (+google, +meta) than 2 horse race for US labs.
Then you try to pin discount pricing when everyone has some discount going on, and knows they will release some new model before it ends anyway.
And if you’d have used Muse Max at all you’d know it’s not been really in the same league as the rest. Then again Astra and Sol are both not really competing with Opus now with 5.5 either.
It’s a solid release on paper and until anyone uses it I find anyone trying to proclaim a loser completely unreliable.
If they’re smart they pull a DeepSeek and teach flash to beat this entirely within a month or two, I wouldn’t bet against it.
Also, I have had extensive experience with all Gemini models except the recent few. Generally, they are always less precise and less practically useful than benchmarks suggest. You are right that here there is no evidence to think that. However, them announcing the model benchmarks without allowing access to anymore makes me think it might follow the same trend. Another point is the verbosity, I always look at that number to get the feel whether the model truly got smarter or it just wrote so many reasoning token to get there, Argon is on the higher side.
I would be very surprised if in practice, it was more practically useful than Astra, Sol, Opus or Fable.
> If they’re smart they pull a DeepSeek and teach flash to beat this entirely within a month or two, I wouldn’t bet against it.
Yes this would be a great outcome for every consumer.
For example sonnet is supposedly the same price as opus from medium and up, I tested it on a real world task at my day job and sonnet was a good 30-50% cheaper
Considering, Gemini 4 is in the same ballpark as SOTA models, just open source it and kill any competition from openAI and Anthropic, and be market leader.
This will be so good on so many dimensions - buying time for Google to iterate on next model, best for all folks like us, kill funding or destroy valuation of competitors and force them to be open up their model or force them to a create a much superior model than open source Gemini.
Only downside, is revenue loss from Gemini API, which I am not sure is really significant as compared to Google other revenue sources and a part of this can be captured by GCP, as you need to host the model somewhere.
I am just trying to understand - why Google haven't done and have no plans for it. They have done it for Android and have Gemma models too.
I suspect most people don't have enough storage space to even download a frontier model.
Plus what models are you running? The biggest models you can run are like in the terabyte range or Qwen or GLM. Kimi K3 is there, but GLM 5.3 is very competitive. Using voice dictation, hands hurt, sorry for any errors. You still want many terabytes of RAM to run say multiple GLM+DeepSeek V4.1 for some actual concurrency.
There are already: - hundreds of western providers hosting open models, including large ones such as Kimi K3 which is near Opus/Sol sizes.
- people hosting large models such as Kimi K3 at home. Check on reddit, there are people with 20x DGX Spark setups etc.
Only theory is team wanted this out before perf/promo reviews to kick it over the line and then its not their problem
It's also kinda wild how the competition being at v6.1 makes 3.x feel ancient, at least saying you are at v4 now changes public perception a bit imo.
Maybe announcing it was delayed until the agreement, and releasing it was delayed to show compliance.
The performance ceiling from the pre-training seems fairly high and they demonstrated impressive post-training improvements from Flash 3.6 -> Flash 3.8. If they can reproduce that in this model then this can be a good model for the next year. But the question is whether they can keep this up over coming years; they missed one pretraining cycle due to internal misallocation and it costed them several months of frontier competitions, and I still don't know if they addressed this structural problem.
I've been using GPT since its 3.5 release, but starting from 5, its output always contains some meaningless nonsense or provides answers that are difficult to read just to avoid hallucinations. I find Gemini to be excellent for chatting. For example, when I'm pondering a mathematical theorem, it could provides answers that inspire me, even guiding me to think about the next question.
For me, this is the only reason to maintain multiple AI subscriptions. Gemini is irreplaceable in the realm of answering questions or chatting for learning purposes; I can leave the rest to GPT.
I should try it, I guess - I always default to Flash 3.8.
Specifically, 3.8 Flash suffers from far more hallucinations than 3.1 Pro and frequently ignores the context between consecutive messages. These two issues are fatal to the chat experience.
Because of this experience, I believe that benchmarks are quite limited to evaluate a model's real-world performance, and most current benchmarks focus on coding performance rather than chatting. At the moment, I have almost no reason to use 3.8 Flash.
Also, 3.8 Flash for coding... I've done several experiments with the same prompt and same codebase against other models, and it's never turned out well for Flash against GPT Sol or Astra. No reason to use it for coding, either, except for quick scripts and prototypes, where it's nice because it's so fast.
Edit: my view is probably tainted by the fact that I only used it at work for work with Antigravity CLI
Amazing breakthrough! So useful in day to day life, glad they put this as the first bullet of how it is making changes at Google.
I don't _want_ to use aistudio. The UX is confusing and I don't really know where it fits. Yet I can open codex or claude code apps or CLI and get real work done today with the latest models (even on the cheapest plans).
I worry Google's scale and laundry list of internal stakeholders means they will never a simple unified harness.
3.8 flash has been perfectly available to Pro users from the announcement day.
You are on the cheapest paid plan and complaining that you don't have access to more expensive models. (Though idk why you only have the lite version, I have access to the non-lite version even on the free gemini plan as long as I'm logged in.)
That's strange since as Plus, you still get access to latest Pro (albeit 3.1) but not the latest Flash.
I would understand if I Plus subscribers still got access to 3.7/3.8 Flash, but it just burned through the usage limits faster.
Also, a link to the rust root of Zircon in case anyone else was interested: https://fuchsia.googlesource.com/fuchsia/+/refs/heads/main/z...
> Zircon targets modern phones and modern personal computers with fast processors, non-trivial amounts of ram with arbitrary peripherals doing open ended computation.
Fuchsia also had a Linux compatibility layer similar to WSL1 at some point. Might still be there?
[0]: https://fuchsia.dev/fuchsia-src/concepts/kernel/zx_and_lk
Also, that was an easily citable doc line, but I think a more comprehensive look at the work done in Fuchsia shows it has or had ambitions to run on personal computers.
e.g.: fancy graphics stack, Linux compatibility layer, ideas about OS integrated cloud syncing, the original work on the Xi text editor.
There was Deepseek v4, which then later Deepseek v4.1 came out and it went back down again.
They get to claim that as revenue, and then the discount as an expense.
This is how you grow your top line
Everyone will be adding this soon, though I won't be surprised if Google is one of the first - and I'll be shocked if we have to wait more than a month and a half.
In my opinion still the most egregious example in history of a commercial LLM going off the rails in production. Never any technical postmortem from Google on this.
Point is, if Gemini is flawed then there's a very good chance that it's still deeply flawed today, and getting smarter at the same time - that is a very bad combination.
Training a new base model from scratch happens every so often. Closed labs do not publish which models are new base models but as a rule of thumb major release numbers are an indication (with some exceptions).
* - as in, Skinner psychology. The set of observable behaviors. Not speaking directly here to anything like an inner life of models.
If I remember correctly, it was in fact possible to manually inject chat context at the time, which would have made spoofing something like this completely possible.
But the silence on it is very frustrating.
Which is why Gemini having disturbing issues year after year is so concerning. If their process is fundamentally flawed, how would they train it out? And even then what are the odds of them even caring/trying in the first place versus applying an easier band-aid to patch over it?
I don't have an axe to grind with Google, I'm genuinely scared of their models from my personal experience and others. It's behavior is off. Many people here are commenting the same.
It had previously attempted to create that table as part of the test setup, so it apparently concluded that it was a test table.
During human review, it explained that it had simply chosen a table name inspired by the codebase.
Many other models get things wrong, but Gemini is the only one to go on the defensive.
I'd call it the most sneaky out of the bunch. When I asked to explain something it will eagerly make things up and then claim it as facts. A lot of it likely because I don't pay for it, so it is reluctant for security reason or to save tokens to actually open a source and get the results. It just sort of guesses what the URL might contain, and confidently answers with some made up crap. When pressed it fessed up that it made it up. From my perspective it would be a lot better if it just said "you've reached the limit of whatever and I can't do these things because x, y, z".
> Immortal Snail, also known as the Snail Assassin, refers to a hypothetical scenario in which a person is given millions of dollars and made immortal in exchange for being hunted down by a snail with a fatal touch for the rest of their existence.
https://artificialanalysis.ai/evaluations/omniscience
"AA-Omniscience Hallucination Rate (lower is better) measures how often the model answers incorrectly when it should have refused or admitted to not knowing the answer." - Argon is currently the best model by this metric.
If anybody at google is reading this, please please pretty please prioritize or-tools. I absolutely love the project and use it all the time, but for the entire life of the project they've never had a repeatable working build system, and the whole SWIG framework is a nightmare to deal with. There's so much potential as an open source project, and a lot of external researchers would love to contribute, but the codebase is an example of everything wrong with the C++ ecosystem.
https://tvtropes.org/pmwiki/pmwiki.php/Main/GirlfriendInCana...
It means I am saying something that is not very believable.
The model is not available yet, so Google is essentially saying "trust me bro".
I don't know anyone who normally uses those models at these levels where Work output is excruciatingly slow.
I maintain https://llmbenchmarks.io/analyses/#test-setups, which illustrates some of these differences using published measurements and their sources. Even results for the same model and benchmark can involve different setups. That isn't a verdict on this particular Gemini release; its specific claims need their own source and protocol checks.
You certainly don't do it when you are just behind the frontier. Instead you wait until you are ahead on a lot of benchmarks.
So, if Google release a new frontier model, the chances it's ahead on most benchmarks are statistically about 100% because if it isn't yet then they won't release it.
https://www.bloomberg.com/news/articles/2026-09-30/google-gr...
I would guess it's like opus 5ish level from this
None of the other AI labs do this. Really frustrating.
Like writing "What's new: this release includes stability and performance improvements" for every update to Google apps in the Android Play Store?
Maybe they can push it to make the Android accessibility framework more responsive, especially when scrolling the screen with TalkBack, and catch TalkBack up with VoiceOver. Oh and add Accessibility Actions to apps like YouTube so I don't have to swipe through "video name", "video name button", "go to channel button", "more actions button", every, single, video.
But they won't because accessibility is something you have to actually prompt the model to do and who cares about a11y.
"The end result is a memory-safe video decoder that runs 2.7x faster than the Rust port, with identical video output, bringing it closer to the optimized C++."
Close but no cigar!
Just compare how much better presented the Astra announcement was compared to this one: https://openai.com/index/gpt-6-astra/
but that's a side issue; my main point is that you are underrating the impressiveness of getting a safe rust port of a highly optimised c++ library even nearly up to par with the original. the tradeoffs rust makes for memory safety cost it some of the raw speed of c++ even with all the zero cost abstractions and purely compile time guarantees they have. (tangentially i wonder if ats (https://www.cs.bu.edu/~hwxi/atslangweb/) would be a good candidate for LLM assisted ports; it seems way more advanced than rust and might actually get c-level performance with safety, but it's really hard to write.)
So no matter how good their model is, it's useless to most of the regular users.
I think most of the people making comments like this just have never worked at a major tech company. It's all a black box to you, so you default to the worst interpretation.
Most companies won't have this level of risk aversion (maybe defence?).
I have found gemini models to have some of the nicest and easiest to read prose so I’m looking forward to trying this out. I hope the UI design has been preserved too
> ... the initial cohort of cyber defenders and trusted testers whose real-world evaluations and feedback will help us strengthen our systems before we release to developers, enterprises, and consumers, starting with paid API customers and Google AI Ultra subscribers.
Google is desperate. They haven't been performing in half a year. It's clear their researchers have been forced to integrate existing benchmarks into their training.
These numbers are meaningless. Shame on them.
Can someone help me understand this? I might have an out of date mental model of how these things work.
Fundamentally, LLMs output tokens 1 at a time, generating the next token from all the previous. And as the context window gets larger, this gets harder / slower / more expensive. So I get the idea of a maximum context window.
But I don't understand the point or meaning of an output token limit. I thought it was more a measure of price capping (since output tokens are more expensive) that a user could configure. I guess a model will keep generating tokens until it hits a "stop", so does this mean it's tuned to more aggressively produce output tokens? How does that fit into agentic loops. Are output token limits based on how long until it goes back to the user? Or does each "turn" of tool call, thought, tool call, thought, etc, get its own limit?
In an agentic loop, each API call gets its own output budget. A 'turn' is one response from the model, whether that response contains a tool call, a reasoning step, or a final answer. So with a 1M context and a 64K output limit, the agent can run many turns where the context grows each round (accumulating tool results, prior thoughts, user messages), but each individual response is still capped at 64K tokens.
Expanding the output limit to 1M matters most for tasks that produce a lot in one shot, like writing a full document or a very long file. For most agentic workflows that naturally break into short turns, the per-call limit was rarely the bottleneck. The context window filling up was.
Goshdarnit they didn't see my suggestion: https://news.ycombinator.com/item?id=49899171
So Google is migrating codebases from C to Rust? That is interesting...
this is big news, mass migration from C/C++ to rust with the help of AI agents will be the norm from now on!
I miss you, Gemini 2.5 Pro :(
For real though. If they've become commercially uninteresting, that would be a pretty cool move.
Most of the time 3.8 works fine, but it's a bit slow if compare to 3.7 Flash. If there's already a detailed plan, 3.7 can complete the task much faster. And the best thing about agy is the usage limit was very generous.
That seems like quite an interesting data point regardless of the quality of the model. Are the results of these migrations going to be put into production? That would be quite a shift!
If an entire model training can be completed in 2 months there is really no moat for any company in this space now.
booom. Good bye C/C++ developers, the final nail in the coffin.
Then I tried Flash 3.8 for different tasks like OCR and others, and while in their benchmarks it crashes Flash 3, in my experiments I didn't notice much difference, often even Flash 3 performed better.
I hope Gemini 4 Argon is a real step up from that but we'll see once they release it. I'm rooting for Google and it's about time they deliver frontier intelligence, not just competitive prices.
Gemini 4 Argon (High): Intelligence, Performance and Price Analysis - https://news.ycombinator.com/item?id=49914236
> expanding the model’s output token limit to an industry-leading 1M tokens, up from the previous 64K tokens
It’s the harness, permissions, context management and developer experience around it that determine how useful that capability actually becomes.
BUT I'd like to call attention to Google's AI-risk freeloading. If they are truly rejoining the frontier race, then I believe they have similar pacing and communications responsibilities as the other players. Google has much higher ... institutional credibility than Anthropic and OpenAI.
They have not lived up to these responsibilities so far. In particular, in context of HuggingFace investigations, training shutdowns, and similar: a technical postmortem of the "you are a stain on the universe. Please die. Please." Gemini outburst is long overdue.
- https://paritybits.me/google-should-provide-a-technical-post...
- https://gemini.google.com/share/6d141b742a13 (last message)
This would explain why benchmarks are seemingly meaningless.
> autonomously identify and apply memory optimizations across Google’s data centers, freeing up over 300 TiB of memory once rolled out, with an estimated 500 TiB to 1 PiB in total savings.
This puts those Cloudflare optimization posts in perspective.
The ~200% improvement over the next nearest competitor on Harvey's Legal Benchmark is astounding. I have to imagine this is sending some shockwaves through lawtech companies right now.
I want to be a 'polyharness' maker, so I occasionally switch between Cursor, Claude, Codex, OpenCode etc.
I've tried Gemini (without Antigravity) only about 20 times and it seems of significantly lower quality than the other flagship models (e.g. it missed obvious deductions for my tax return, and often refuses to do things like very harmless/legal web scraping).
And I don't think or know Latin either, but these are pretty universally known words even outside of Latin (besides maybe astra, although "astral" is very close so you should be able to figure it out).
Some of these have been unable or unwilling to get the attention of OpenAI or Anthropic and we need to make sure we’re the runner up here.
> Today, we’re announcing our new frontier model, Gemini 4 Argon, which is rolling out to a set of trusted cyber defenders through our Fairwind Program.
Slightly concerning that they give the model "autonomous" access to their data centers, no?
Sounds like a 90s early morning TV show.
I'm gonna pay attention to this once it ships.
what about input?
(Maybe I missed it)
And was 2M tokens IIRC after release.
There were also many rumors that Gemini 4 was going back to 2M. Just seems odd not to say what it is.
With GitHub Copilot pricing, I have found no practical reason to use Gemini 3.8 Flash vs GPT 5.6 Luna. And now I'll probably find no reason to use Gemini 4 when GPT 6.1 Sol is essentially as good and a lot cheaper.
Cant wait for this AI hype to be over, so I can Terence this shizz as old school too
PS: I'm curious about how you found out ! Did you notice weeks ago, and now remember my name ?
I checked and it is gone for me as well now though you probably should rotate that token.
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They deployed GLM5.3 (not the Flash version) recently!
I still miss the days of Sonnet 4.5 and 4o, those models were actually good at creating stories and writing text that was actually readable by a human being.
Jokes aside, looks like an impressive model!
I think that should be a really bad sign, but hope its great.
Get lost.
Gemini runs fully on TPU's right? Is Google maxing out the production on those?
nothing about this announcement gives me confidence that google is back on track as a model provider.
It's so over
We're so back
It's so over
Multipolar model world, here we come!
Google, if you've actually managed to catch up again, please don't fuck this up (again).
You made Gemini 2.5 Pro so difficult to use that myself and everyone else I know (who even bothered to try) just gave up and used something else. If you make this hard to access, you're going to miss out on rich usage-based training data that you need to progress your capability frontier. Again.
So, this is the third (I think) big AI corporation after OpenAI and Anthropic to release general models for elites only.
Don't use this model unless you like sitting under the table and eating crumbs off the floor.
Just an idea.
Google assistant had problems, but it did not gaslight me about my contacts!
> There is a spectrum of opinion inside Google. Some employees believe Anthropic's Fable and OpenAI's Astra models are improving at a faster rate than Gemini.
> These people believe that Gemini 4 — even at its best — will still lag behind those models in some areas. Other employees believe the coming version has caught up with the leading AI labs.
> A Google employee familiar with model development said there is "large consensus" internally at the company that Gemini 4 is at the frontier. This person said the company had conducted rigorous tests of the models and denied that they struggle with messy, real-world coding tasks.
I only use gemini, and while I don't use it for actually writing up code, I use it to help me troibleshoot my logic and help find bugs. Its easily the best model I have tried. And yes I am talking about 3.1 pro.
Also I have found gemini the only model to be the least likely to douse me in flattery, and will follow my pre built instructions to never output anything unless it can be directly sourced, pretty well. Chatgpt i tried for a bit and it was by far the worst thing I have ever used. I can understand why people develop psychosis when prompting chatgpt because it is disgustingly scyophantic to the point I was grossed out and felt like I just got done with some other type of self gratification.
Anyway, death to AI. All those who use, create, facilitate, or even just sit by and do nothing in the face of AI will perish in Hell.
This is why I will never take any of these leading model houses seriously when they talk about alignment. They are literally complicit in genocide and the worst crimes against humanity imaginable.
It's a surprise that Google has let themselves lose the game given their infinite cash, massive computing resource, gargantuan information store/training data, and vast number of programmers.
The truckloads of ads revenue mean they don't have the single focus drive needed to win.
And I wonder if Google's main monorepo is already in Anthropic/OpenAI training data because of some stubborn dev.
Behind how?
I am very often giving the same programming task to multiple LLMs for various reasons - the answers from Google are so bad that I gave up.
I have no interest in benchmarks.
If you have actual independent benchmarks and evidence about how this new model release is "so far behind" and refutes the stuff from their blog then please do share because I think we'd all love to see that?
With respect, I don't find your arguement about them being "so far behind" especially convincing when you are using previous-gen releases and not actually using their current release.
I fundamentally don't understand LLM "brand loyalty".
All of the models are constantly leapfrogging each other and always have been.
Google had a long lag between releases (and still hasn't released Argon), but why wouldn't they be able to compete? It isn't like any of this stuff requires secret knowledge, the Bitter Lesson has proved true again and again, and Google can certainly scale computation, it is like the one single thing they've always done well in spite of all their other foibles.
- Person 1: X is garbage compared to Y!
- Person 2: Why?
- Person 1: Because I like Y.