These announcements have started to look like a template.
- Our state-of-the-art model.
- Benchmarks comparing to X,Y,Z.
- "Better" reasoning.
It might be an excellent model, but reading the exact text repeatedly is taking the excitement away.
- Our state-of-the-art model.
- Benchmarks comparing to X,Y,Z.
- "Better" reasoning.
It might be an excellent model, but reading the exact text repeatedly is taking the excitement away.
It's interesting how the recent AI announcements are following the same trend over a smaller timeframe.
These LLM models were supposedly limited by the training run, but these point-version models are mostly post-training driven, which seems to be taking less time.
If models were tied to a specific hardware (say, a "AI PC" or whatever) the cycle would get slower and we'll get a slower summer which I'm secretly wishing.
once you get all your apps, wallpaper, shortcut order and same OS, you really quickly get the feeling you spent 1000$ for the exact same thing
But it needs to be seamless to remove any friction from the purchase, but at the same time if it feels the same then we felt like we wasted money.
So what I usually do is buy a different colored phone and change the wallpaper.
My MacBook was the same. Seamless transition and 2 hours later I was used to the new m4 speeds.
So, I think these results are very interesting, if you know what features specifically you are using.
Excited to see if it works this time.
This is the commodification of models. There is nothing special about the new models but they perform better on the benchmarks.
They are all interchangeable. This is great for users as it adds to price pressure.
They are not so good at measuring reasoning, out-of-domain performance, or creativity.
As big players look to start monetizing, they are going to desperately be searching for moats.
Right now, in my opinion, OpenAI has actually a useful deep research feature which I've found nobody else matches. But there is no moat to be seen there.
I might be mistaken, but originally the reasoning was fully hidden? Or maybe it was just far more aggressively purged. I agree that today the reasoning output seems higher quality then originally.
It's called the "first step fallacy", and AI hype believers continue to fall for it.
The rate of announcements is a sign that models are increasing in ability at an amazing rate, and the content is broadly the same because they’re fungible commodities.
The latter, that models are fungible commodities, is what’s driving this explosion and leading to intense competition that benefits us all.
If these companies start failing to beat the competition, then we should prepare ourselves for very creative writing in the announcements.
> mturk workers in Nigeria
Also, I think google's winning the race on actually integrating the AI to do useful things. The agent demo from OpenAI is interesting, but frankly, I don't care to watch the machine use my computer. A real virtual assistant can browse the web headless and pick flights or food for me. That's the real workflow unlock, IMO.
> I've moved entire workflows to Gemini because it's just _way_ better than what openai has to offer, especially for the money.
Upload a complicated PDF of presentation and ask for insights that require some critical thinking about them.
> Do you think these "moved entire workflows" offset a full worker, or X% of a full worker
It can replace many junior analysts IMO.
The Achilles heel of LLMs is the distinct lack of practical real-world applications. Yes, Google and Microsoft have been shoving the tech into everything they can fit, but that doesn't a product make.
If I'm being incredibly generous I will concede that this could have been the case for the first few weeks when it was making headlines, but it clearly isn't true now.
It would be literally impossible to keep up these figures for as long as ChatGPT has without a ton of repeat users. There simply aren't enough people/devices.
Practical, real-world application.