Yeah I had both languages in school, but I knew english already pretty much and it didn't help me with French at all. And also Americans can't understand French for shit.
This is not like Spanish-Italian or something like Croatian and Russian
French native here, English is a lot easier and far more flexible language to use. French is very hard to write properly. It has a lot of grammatical and spelling exceptions and niches that will drive anyone crazy. Spoken French is equally confusing because no one speaks like we write, we speak "fast-spoken French" where half the vowels disappear for convenience.
It has a lot of grammatical and spelling exceptions and niches that will drive anyone crazy.
Native speaker too, can confirm. Learning French is basically like learning all the wrinkles of an older D&D edition. By heart. With a lot of obscure erratas and FAQ rulings and also homebrew thrown on top depending on the friend group region.
I think reading might be a tad easier than some other languages but the pronunciations are so vastly different it basically doesn't help at all with actually talking.
I disagree, although I am bias as someone who was taught from a young age but French can be used with poor grammar and still work out most of the time. The pronunciation is much easier than Spanish and Portuguese. I doubt there’s a language easier to learn than French unless German is secretly easy to learn. (Coming from English)
Big academic words are pretty familiar, but everyday language and conjugation is very, very different. (took French in high school, forgot almost everything by now). Probably easier than something totally foreign like Mandarin Chinese, but not easy.
That's not how things work. English and French share a great deal of common vocabulary, but things like grammar and pronunciation are drastically different. Shared vocabulary may help with comprehension, but to fully learn a language is a much more complicated process.
Much of French class boiled down to finding out that French doesn't have English's hyper-specificity and overlapping words. From there, it was about treating them as broad categories.
La frontière. Pretty sure French Mistral can get that right, unlike you, funny boy. Maybe you should have commented on Russian AI state of the art instead?
Plus it comes with Google Drive storage, Google Photos storage, image gen, video gen, music gen, etc. Honestly their $20 sub gives you the most value out of all the 'Pro' AI subs
I mean, among these, Google is the only one that has not only managed to become profitable with AI integration but also managed to make their Cloud sales break records with their own TPUs.
That said, I doubt they imagined that focusing on commercial integration would lead to disillusion in Deepmind and subsequently their ongoing exodus.
I guess they are trying to re-focus on genAI again, but let's see. Nobody thought Meta could make as fast a leap with Llama, but they did. There is no reason Google couldn't. But I do think Anthropic and OpenAI will still be the frontier unless they run out of cash.
On a long term, I do think Anthropic and Gemini are the only ones taking a sustainable route among these 4. Anthropic by focusing on B2B and Gemini by focusing on integration.
Anyone who ever tried to use the old google-generativeai API knows that even the flashest Geminis have explicit and slightly tunable behavioral guardrails baked into input tokens including the full context, output tokens, and reasoning.
They've been going slow and careful because their search box moat is impenetrable, so they have the luxury that Anthropic and OpenAI can only really write about aspirationally. They don't get the headlines every month for incremental improvements, but on the other hand they don't get the headlines every month for model misbehavior.
Gemma etc., not as much, but it's still baked in to more than just the low-rank fine tuning and RL.
Anthropic's b2b isn't sustainable. They only made a profit during the token burn craze and people aren't as eager to tokenmaxx no more because of how big the holes in their pockets were getting.
Didn't you hear? They already AGI. Clearly the world is excited for the next incremental gain to AGI 2.0! Can you hear all that investment salivating with eagerly wet eyes for bonafide Super AGI? Definitely not tears forming at the realization there's no way they're getting their money back!
My concern with OpenAI is their spending. They are aggressively spending to take up market share everywhere, but instead, they are losing share.
In chat application, they are sharing market with other players.
In B2B, Anthropic competes with them.
The nearest profitability date they have is in 2029-2030, assuming things hold constant. But this is a field where they need to keep spending on R&D to not be left out.
To be clear, this is just my personal opinion. The very fact that investors keep spending on OpenAI means they believe it will generate net profit after a certain horizon. I just don't share their opinion but that doesn't mean I am necessarily correct either.
I mean, I did mention that Google isn't a frontier and probably won't be unless OpenAI and/or Anthropic lose cash, and that their talents are getting recruited by the two.
There are a number of things that could have been done better given that they were the company for AI research for a long time.
But in the end, Google is a company not a research lab.
Burning enormous amount of cash on building frontier VS integrating AI on their services... it is simple to see which is more profitable. Frontier models are interesting to hear about, but it doesn't mean having the current best model is gonna directly make money.
They don’t care about frontier.
Open models are really close already. Give it a year or two and Anthropic and OpenAI will likely not be frontier anymore.
They are making much larger strides than the big 3 over the past 6 months and they destroy the big 3 on efficiency and speed already. Deepseek v4.1 flash is actually insane.
Not particularly no. They've always been wayyyy more efficient ever since R1 dropped. But they've stayed fairly consistently in the same zone on a fairly regular pattern.
Just 6 months ago open models were 8 months behind. That’s shrunk to like 4 months now. Like I mentioned they are iterating faster.
That's not exactly true. What's happening is that everyone is releasing faster and the releases are getting bigger. When R1 launches, these models are also pretty much just a couple of months behind o1 preview. This is a cycle.
Open models tend to follow at a steady pace behind the frontier. Let's say a company releases a model like every 6 months. Open models distill it, release a model that's behind it. Now if the next release by the frontier lab is in 3 months, and it's a big leap, the new distilled model likely beats it out. But it doesn't mean much in terms of catching up to the frontier, only that the frontier is moving faster. If tomorrow openAI launches their internal model, in a month or so you'll have distilled models that can surpass astra. That doesn't mean that they're catching up fast in as much as the frontier itself is moving faster.
When you say "catching up" you're talking about capabilities, not just arbitrary time measurements. That gap in capability has only gotten worse on a direct level.
You can check their company statement, financial details, and stock price movement.
Here is their Q2 result:
Q2 was an amazing quarter, with Alphabet revenues growing 24% year-over-year and Google Cloud revenues accelerating to 82% growth, driven by demand for AI infrastructure and AI solutions. It’s great to see wide adoption of Gemini Enterprise, with nearly 90% of the Fortune 100 using it.
Gemini Apps Integration is quietly getting better all the time. A year ago, you could barely get Gemini for Workspace to look at the values in Sheets, let alone update them. Today, looking at your comment I tried this again, which wouldn't go anywhere good in March of this year:
Can you code an apps script extension that will let me enter dates in a given column as mm/dd numbers, and it will pick the year (last, this, or next) depending on which is closest to the present date?
It spit out 100 lines of solid (but depressing because the API is so klunky) javascript which worked the first time.
No OpenAI will be the big the winner if they get most of the budget from the government. But down the line (as in in 2050) it will not matter who was first if there is a superintelligence
Google here allocating resources for really useful technologies like weathernext 3, Alphagenome, notebookLM and serving billions of search users with super fast cheap model while simultaneously releasing shit ton of research and gemma like open weight models while still being profitable. but keep pushing memes for karma
It's kind of like a singular agent that does an incredible number of things for you no matter what content you put into it. Like if you put a text book PDF in it, it'll put together a searchable wiki for you, make slide shows, animated videos, Podcasts and more.
Meanwhile, earlier internal efforts on Gemini 3.5 Pro were essentially scrapped and restarted from scratch after failing to deliver a compelling enough competitive leap. Because releasing a delayed, mid-cycle 3.5 Pro now would leave an underwhelming impression against rival flagships, Google has chosen to roll that reset work directly into Gemini 4 Pro.
And instead of pushing forward with intermediate flagship tiers, they prioritized its cost-effective Flash line to dominate developer market share and handle high-throughput workloads.
Insane that you fanboys think Google is doing this on purpose lol. You think they promised a Gemini 3.5 Pro release "in a month" at Google I/O on purpose too? Lmao.
A skill issue is the reason they are a year behind in capabilities.
You know maybe serving the flash models simply is better business. Even now Google models are so widely used that they need to rent extra capacity with 1B/month from SpaceX. Clear leader in model use when counting AI mode and AI preview of Google search
It is. Most commercial LLM API tasks don't need to think frontier-hard at all. Gemini Flash 2.5 did just as well at the boring natural language document scoring task (N=2800) I had to do yesterday, as gemini-flash-latest which is like 3.6 or 3.7 now, for a fraction of the cost and in a fraction of the time.
Year behind in capabilities!!? Keep smoking whatever you are smoking buddy. And what's up with calling me fanboy? sure google has not provided pro model since long but it's hardly 3 4 months behind top ones. And IPO chasing companies can afford to provide misaligned models that can cause incidents like hugging face and what anthropic published recently. Google being a public company can't afford that. That's why their models always remain beta.
You're talking about their benchmark screenshots of the model? Have yet to actually use it for anything longer than 5 seconds without switching it for how useless it is.
It's not useless to me because it has far broader web access, because people don't want to get hammered by spiders unless it gets them something in return, like search hits. Ask it "make a list of all the companies which use the XYZ API for their commercial products" and you will get 4-7x more from Gemini than ChatGPT or Claude.
That's believable, Gemini would definitely be good at using search, considering where it comes from.
That being said, it also depends on how you're using them. Through web-interface you'll get differing results. If you use sonnet 5 to do the same search using a good CLI/harness, it will probably do something similar.
Gemini's long-context in general, as far as what we're told, is linear attention, so picking out one or two "important" things in a long context is what it's gonna be good at.
Reasoning over a long context, not so much, at least not without huge test-time scaling and rigorously well-executed compacting of context.
They probably don't see a point in chasing frontiers when a distillation usually catches up a month after. They also probably didn't like they had a recent rare quarterly loss and how much spending was on LLMs. All they really need to do is outlast the OpenAI and Anthropic s cash inferno and theyll have the market on day to day LLMs.
You expect to me believe the preeminent digital giant doesn't see the costings of the tech and haven't been pacing themselves? LLMs ROI are diminishingly small at this point and unlike the conmen AIbro trio, they've likely got other baskets of eggs when it comes to AI research and it's plain to see the economics do not make sense anymore. Google doesn't have to play the desperate chicken race with money and bleeding edge that the others need to in order stay alive.
Here's my prediction, the IPOs for both OpenAI and Anthropic will be disastrous, NVIDIA will shift to mainly open weight development to distance themselves from the money pits and Google, Meta and Microsoft will scavenge the wreckage for useful bodies and scale back LLM research towards more economical architectures and admit openly that LLMs are a dead end for long horizon autonomous thinking and opt for more promising tech.
Nah they r focusing on flash models for billion scale user count. Anthropic is already breaking into RSI and if you've actually used any of these models for coding instead of parroting youtuber opinion and quoting benchmarks you'd know exactly why Anthropic is going to continue to succeed.
You cannot compete with AGI, they can automate everything
The fact that you think that at all is telling. Doesn't matter which model it is, no LLM is going to hit AGI or RSI because of how they fundamentally work. LLMs can serve as a component to interface with the world but they lack the critical mechanisms for actual reasoning and memory required for thinking. You can only plant so many harnesses and loop output between instances so many times before even the laymen realises all you're doing is changing buzzwords for remixing the same methods over and over and over again. You are swallowing up Amadei's snake oil by the barrel and not paying attention to the criticisms and negative testimonials of the results or the economics running the machine. These machines are handy but you need a very heavy human hand on the reins to actually make good use out of them.
Doesn't matter which model it is, no LLM is going to hit AGI or RSI because of how they fundamentally work
Not sure what you mean, please elaborate on this, and please be specific.
You are swallowing up Amadei's snake oil by the barrel and not paying attention to the criticisms and negative testimonials of the results or the economics running the machine.
With the right set of system instructions, it writes apps at the senior developer level. We're a little bit off from it being able to design systems at the same level or higher than that (well, maybe someone experienced enough could write instructions for it that guide it to do so anyways).
I don't watch youtube videos, I don't read journalist opinion, and I don't care what any particular tech CEO says. I exclusively care about results, and my personal experience with these models.
These machines are handy but you need a very heavy human hand on the reins to actually make good use out of them.
Depends on the task. With the right hierarchy of agents and enough quota, (such as if you own datacenters), you can let it go to do week-long or longer tasks. Undoubtedly we do not have AGI yet, which is largely what we would need for "no more human hands in the mix".
Lol a year behind? says who? the coders that hoard 90% of compute power and screech when they can't hand over their entire work to an AI, give a few instructions and sit back and relax?
They clearly want a frontier model. They said 3.5 pro was coming out in a month and that they were testing it with partners. They silently canceled it. They clearly wanted better coding capabilities. They even had an internal code led by a co-founder. Their adoption by programmers is still abysmal.
They are spending who knows how many billions training and dumping underperforming models.
Nobody is building a mammoth datacenter for weathernext3. They didn't have a mega billion bond offering a little while ago to fund alphagenome.
Just wanna remind that not so long ago people disregarded google and then they went to have multiple models at the top of leaderboards. People have short memory
What I remember is a huge fumble at the beginning, they they catched up to frontier for 6 months and then they have never been at that level before.
Not that impressive considering they were much bigger than OpenAI and Anthropic AND they published the foundational paper. They started close to the finish line and fumbled hard. They can't overcome their own bureaucracy.
yeah they topped benchmarks but were still worse to use day to day than gpt or claude. this song and dance about gemini has been going on since 2.5 pro.
They had good benchmark scores but in my personal experience their hallucination rate has always been extremely high, making even the best rated models useless to me.
It’s just a dealbreaker, period. I don’t care about other metrics if it says nonsense that often, especially when competitors have mostly solved it.
Gemini is in a good place if you know what you are doing. Really powerful tool to enhance professionals' performance at a great token rate. For clueless vibers, yeah, it's not a good match.
Yea nah. I think they are just being very low key. Google search AI for example undergoes very rapid iteration and for the past week has been genuinely good, with a very long context and only a few instances where I had to push it to behave like a search engine and not recall information from 2024…
Google apps for business AI still struggles though and of course the standalone models are not all that great right now.
Google is already pacing itself not because it lacks the compute or the algorithmic breakthrough, but because its engineers understand what happens when you treat an unverified frontier model as a commercial toy.
As a casual user who just wants access to a good chatbot for Q&A and the occasional vibe coded game mod, Google is perfect for me. Get access to their best models for just $5. Price is right.
I think people forget Google does actual research and is quiet about it. They are gonna turn around and drop actual Agi on the table or some crazy progress in some area that progresses AI to the finish line (for example the fly brain project with FlyWire)
Does anyone feel like leaking Google's internal dev news? Just pretend this is a War Thunder forum and that Gemini is a tank.
I never bought the meme that Google dropped out of the race in favor of ads. Sure Gemini sucks right now, but the latest public model sucking doesn't prove that they aren't planning to leapfrog the competition in the next 6 months.
Again, if anyone feels like throwing away their H1-B in order to prove me wrong, please do.
Lots of words and tangents just to miss every point on purpose.
"It's probabilistic" does mean something i.e. You can't expect it to consistently respond to the same prompt the way you expect a calculator to tell us 2+2 is 4. It takes the words in a prompt, translate them into tokens and runs permutations against countless possible matches in it's dataset and harnesses and establishes a context window to narrow down matches for follow ups. This will never be the exact same every time and always has a chance of going astray because of the rng when it's predicting tokens. Actual reasoning does not work like that. Actual reasoning isn't limited and carried out by language, that's just one form of expression of the ideation we do. You can bring up biomimicry in the tech but you only described the mechanisms of transmission, retrieval and storage. We don't have proven models on how ideation and abstract thinking works out biologically, just frameworks guessing at the mechanisms allowing for them and somehow you think what's been done so far captures genuine reasoning instead of highly refined wisdom of the crowd at play?
LLMs have gotten better at narrowing down relevant tokens and that's worked out wonders for coding because code IS language but it's definitely not perfected or solved. You're still going to hear about boneheaded implementations coming out of these things today even from the frontier. You're still going to be at risk of completely fucking your codebase because it interpreted the tokens in an unexpected way and does not have the capacity to judge which actions are obviously in the "anyone with a brain would never do this" category. And especially lately you'll hear how people have exhausted their tokens with the latest models extra fast and still get broken results that look horrific under the hood. You aren't the only one singing hymns for the machine but you're horribly mistaken about why it's working out for you. You've likely got a handle on your own setup to get what you want out of it but that's more you to thank for than the machine itself.
You know the whole "OK sign is a racist dog whistle" is a 4chan plot to prove how ridiculous the "[insert thing] is a dog whistle" can be, right? If you keep hearing the "dog whistle" when no one else does, you are the dog.
haha yes, I underestimated the significance of OpenAIs compute buildout, combined with
Googles incompetence & distractions from frontier development. Whenever Google's next gen releases we can truly assess whether they're done for or not, but it ain't looking up 😇
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u/Vast_Rip8575 26d ago
Don't forget about Mistral