Anthropic's Sonnet 5.5 reproduces complex physics simulations and ray tracing at five times lower cost than earlier frontier models, suggesting that frontier-level AI will soon run on laptops and phones. Dr. Károly Zsolnai-Fehér argues that training efficiency matters more than model size, pointing toward democratized AI research.
Sonnet 5.5 is an incredibly important AI release. I ran my experiments and was completely stunned that it was able to reproduce this research work called AVBD that simulates the physics of a lot of objects colliding. That is incredibly difficult because it has to keep collisions, friction, joints physically stable without anything exploding numerically. And all this while running extremely fast. So, how did it do? Well, you are already seeing it right now. Look! Now, part of the source code is available for the research work, so what it had to do is to port it into a single HTML file and set up the experiments to match the original work. It's not the whole thing, but it's really tough. And it can also do ray tracing. Look at that beauty. Caustics, And now, volumetric caustics. Oh my! Super good! And while I cut in some of the amazing video games and simulations I made with it, I told you, Sonnet 5.5 is an incredibly important release. But probably not in the way you think. You see, first, we got Fable, an AI so incredibly good, many people couldn't believe what they saw. But then, just 3 weeks later, Opus 5.5 appeared. This is an AI system that is roughly 2.5 times cheaper, but it roughly matches it in quality. Insanity. But then, even better, Sonnet 5.5 appeared soon after, which is roughly 5 times cheaper than Fable. Cheaper likely means they are much smaller too. So, let's stop there. Because that is amazing news for us Fellow Scholars, and has huge implications. What exactly? Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér. So, similar intelligence keeps getting possible in smaller and smaller systems. Does that mean that— yes! It seems that intelligence is not in the number of parameters. It's in how well you train your parameters. And that is amazing news for the little man. Amazing news for us. Now, hold on to your papers Fellow Scholars, because the frontier-level AI systems you see now We will be able to own and run them ourselves on a beefy laptop. And later, maybe even on a phone. Now, I could be wrong. I am just a student trying to learn with you. But I think there will be no need to spend this insane kind of money. We are all going to be part of this new age where all of us will be empowered to build things that used to require huge teams, but Not anymore. We'll be able to learn new skills easier and cheaper. And this is what Sonnet 5.5 teaches us. What a time to be alive! I use Lambda to reproduce AI research papers, often in minutes. It's also great to train your own models, or fine-tune an existing one. Run inference, or text to image or video? Easy peasy. Running a DeepSeek chatbot or agent? Super fast, super reliable. Lambda gives you powerful NVIDIA GPUs to run your own experiments. I test ideas from the papers I cover, and moments later, results. Love it! Seriously, try it out now at lambda.ai/papers.
