People always love to think the AI race is a duopoly (OpenAI vs. Anthropic), but from what I have experienced, Gemini was always holding on its own, and I find myself using it more than Claude.
True, I use LLMs as glorified search engines to unearth material to read myself, but Gemini is much more useful if you decide to learn yourself. It also offers interesting expansions to your questions.
While I don't offload my work to LLMs (e.g.: Write this project, I'll be watching TV), I find them useful for closing my knowledge gap by collating information, and Google's Gemini is good at that.
I have Gemini because of my Google One membership. I already was on their 2TB plan and it gave me Pro level, IIRC, and it was generous. Now I'm on Ultra mostly because of Google Drive storage I get, but Gemini also gets a nice boost, as well.
I'm on $20 Claude plan, and it's also ample for my needs, for now.
The $20 plan gives you ~120 minutes of high thinking time from Argon weekly. Thinking time isn't an exact metric, context size matters a lot, but I have been using it on the $20 and still have 25% left with ~85 minutes of thinking time on high for a project I have been working on.
I think this is because of capacity issues. Higher paying per token users probably get preferential capacity allocations to those on monthly plans. The allowance for what you get in the monthly plan increases as the models age and the likely capacity problems improve.
They have an insane amount of resources but are so bloated, have a ton of bureaucracy, and have lost a lot of their top talent. Looks like they have figured things out now though.
They have top of the line research, I mean, they invented the thing, and they are still doing stuff that goes beyond making a bigger LLM, as it seems to be the only thing the others are doing, research-wise.
They also have hardware capabilities no one else has, they invented the TPUs, and have lots of them.
They also have the brand recognition, ecosystem, user base and lots of money.
So why are they lagging behind on the actual products, and by lagging behind? I mean they are always in the position where they are just behind the frontier, but still in the game.
It seems almost deliberate. Maybe they are playing the long game, waiting for the bubble to burst. Maybe they don't want to compete against the more profitable parts of the company. Or maybe their researchers are not interested in making an actual product unless being forced to.
Not everything is writing code and search. My uncle works in mortgages and the (very large) firm he works for is entirely Gemini. The way he talks about it is with wonderment in his eyes while he goes into detail on how it went through multiple documents and found unaccounted for cases. I think Google is doing fine in these kinds of industries.
I wouldn't even bet that Google is better than OpenAI and Anthropic for these categories, but they seem to be well used outside of our direct space.
People always love to think the AI race is a duopoly (OpenAI vs. Anthropic), but from what I have experienced, Gemini was always holding on its own, and I find myself using it more than Claude.
True, I use LLMs as glorified search engines to unearth material to read myself, but Gemini is much more useful if you decide to learn yourself. It also offers interesting expansions to your questions.
While I don't offload my work to LLMs (e.g.: Write this project, I'll be watching TV), I find them useful for closing my knowledge gap by collating information, and Google's Gemini is good at that.
Gemini is just too expensive for what you describe. Their 20$ plan only gives you access to the free tier model
I have Gemini because of my Google One membership. I already was on their 2TB plan and it gave me Pro level, IIRC, and it was generous. Now I'm on Ultra mostly because of Google Drive storage I get, but Gemini also gets a nice boost, as well.
I'm on $20 Claude plan, and it's also ample for my needs, for now.
The $20 plan gives you ~120 minutes of high thinking time from Argon weekly. Thinking time isn't an exact metric, context size matters a lot, but I have been using it on the $20 and still have 25% left with ~85 minutes of thinking time on high for a project I have been working on.
I think this is because of capacity issues. Higher paying per token users probably get preferential capacity allocations to those on monthly plans. The allowance for what you get in the monthly plan increases as the models age and the likely capacity problems improve.
Thanks David Guetta!
They have an insane amount of resources but are so bloated, have a ton of bureaucracy, and have lost a lot of their top talent. Looks like they have figured things out now though.
A lot of people do though by looking at the current stock price.
Never bet against their contempt for paying customers.
The situation with Google is a bit unexpected.
They have top of the line research, I mean, they invented the thing, and they are still doing stuff that goes beyond making a bigger LLM, as it seems to be the only thing the others are doing, research-wise.
They also have hardware capabilities no one else has, they invented the TPUs, and have lots of them.
They also have the brand recognition, ecosystem, user base and lots of money.
So why are they lagging behind on the actual products, and by lagging behind? I mean they are always in the position where they are just behind the frontier, but still in the game.
It seems almost deliberate. Maybe they are playing the long game, waiting for the bubble to burst. Maybe they don't want to compete against the more profitable parts of the company. Or maybe their researchers are not interested in making an actual product unless being forced to.
Really? Been 3 years since ChatGPT and Google still going from misery to another in their AI products.
Even if these numbers are correct, they don’t have any useful product for coding or search. Not sure what you’re referring to
Not everything is writing code and search. My uncle works in mortgages and the (very large) firm he works for is entirely Gemini. The way he talks about it is with wonderment in his eyes while he goes into detail on how it went through multiple documents and found unaccounted for cases. I think Google is doing fine in these kinds of industries.
I wouldn't even bet that Google is better than OpenAI and Anthropic for these categories, but they seem to be well used outside of our direct space.
It worked for Microsoft with cloud. Existing industry penetration goes a long way.