Alberto Romero writes The Algorithmic Bridge, a newsletter on AI industry trends, large language models, and company strategy.
Commenters question his predictions, noting his earlier claim that Google was winning every AI front, and debate rumors about AI progress.
You're mentioning only publicly known information. The rumors mentioning radical advances behind closed doors are wild, and then you've suddenly got some stuff like deepseek or phi-4.
Rumors mention recursive "self" improvement (training) already ongoing at big scale, better AIs training lesser AIs (still powerful), to became better AIs, and the cycle restarts. Maybe o1 and o3 are just the beginning of what was choosed to make available publicly (also the newer Sonnet).
https://www.thealgorithmicbridge.com/p/this-rumor-about-gpt-5-changes-everything
The pace of change is actually uncertain, you could have revolutionary advances maybe 4-7 times this year, because the tide has changed and massive hardware (only available to few players) isn't a stopper anymore given that algorithms, software is taking the lead as the main force advancing AI development (anyone in the planet with a brain could make a radical leap in AI tech, anytime going forward).
https://sakana.ai/transformer-squared/
Beside the rumors and relatively (still) low impact recent innovations, we have history: remember that the technology behind gpt-2 existed basically two years before they made it public, and the theory behind that technology existed maybe 4 years before getting anything close to something practical.
All the public information is just old news. If you want to know where's everything going, you should look to where's the money going and/or where are the best teams working (deepseek, others…
That's very interesting. Maybe you are doing this the right way, and my concern as a math educator is for the people who may struggle to stay on the straight and narrow, or know what the straight and narrow is in this brave new world.
Where I see deficiencies is not so much in the calculations. When a problem class has a solution algorithm and 10,000 worked examples online, I'm not too surprised that the LLM generalizes pretty reliably to that problem class.
The problem I find is more when it's tricky, out-of-distribution, not entirely on the "happy path" of what the 10,000 examples are about. In that case, LLM responses quickly become irrelevant, illogical, and Pavlovian. It's the math version of messing up the surgeon riddle when presented with a minor variation that is logically very easy, but isn't the popular version everyone talks about [1].
[1] https://www.thealgorithmicbridge.com/p/openai-researchers-have-discovered
Substack is a really, really good place for this. I don't remember how I found https://paxtier.substack.com/, but it's a great source of news on algae + seaweed technology/science.
https://thealgorithmicbridge.substack.com/ for AI
https://www.parentdata.org/ for studies related to all things parenting
https://blog.jatan.space/ for space (especially moon-related) stuff
https://yourlocalepidemiologist.substack.com/ for Covid/general epidemiology news
Brought to you from the same guy who said in April 2025: "Google Is Winning on Every AI Front" https://www.thealgorithmicbridge.com/p/google-is-winning-on-every-ai-front
Q* is the new "GPT-4 has a hundred trillion parameters".
https://thealgorithmicbridge.substack.com/p/gpt-4-a-viral-case-of-ai-misinformation