Fields medal-winning mathematician Timothy Gowers:
"gpt-5.5 pro solved open mathematics problems at PhD-thesis level in just a few hours, with no serious mathematical input from the human user
if this pace continues, mathematical research and PhD training could face a serious crisis very soon"
Yann LeCun closed $1.03B for AMI Labs on March 10. Three days later, this paper dropped from his NYU collaborators.
15M parameters. Single GPU. A few hours of training.
LeWorldModel is the first JEPA that trains end-to-end from raw pixels. Two loss terms: predict the next embedding, keep the latent space Gaussian. Previous JEPAs needed exponential moving averages or pretrained encoders to avoid representation collapse. LeWM doesn't.
Six hyperparameters down to one.
The numbers are the story. Foundation-model-based world models require hundreds of millions of parameters and serious compute to plan a control task. LeWM plans up to 48x faster while staying competitive on 2D and 3D benchmarks. The whole thing fits on a laptop GPU.
Look at the trajectory. Yann announced his Meta departure in November 2025 after 12 years and called founding FAIR his "proudest non-technical accomplishment." On March 10, 2026, AMI Labs closed the largest seed round in European history at a $3.5B pre-money valuation. Bezos, Nvidia, Samsung, and Toyota all wrote checks.
Three days later: a paper showing that JEPA-from-pixels is no longer fragile and no longer compute-heavy. The engineering scaffolding that made it look like an academic curiosity is gone.
The authors sit at Mila, NYU, Samsung SAIL, and Brown. None at Meta.
Yann's bet was that the path to machine intelligence runs through world models, not language models. He left a public company to build it. Each JEPA paper from his network resets the assumed cost structure for that bet. This one makes world modeling laptop-cheap.
Meta still has the GPUs. The architecture left.
Jane Street AI Engineer revealed how they trained their own LLM for trading to make $22.5B/year
16 minutes. free. straight from tier-1 quants.
bookmark & watch - this is the most honest "AI inside a hedge fund" talk ever published.
forget the "AI trading bot" YouTube grifters. This is the real inside view: data, training, evals, integration.
then start building your own bot using post below.
Instead of watching an hour movie, watch this. In 14 minutes, an Anthropic engineer who wrote Building Effective Agents will teach you more about building agents right than most developers figure out on their own in months.
The Adolescence of Technology: an essay on the risks posed by powerful AI to national security, economies and democracy—and how we can defend against them: https://t.co/0phIiJjrmz
"Demis Hassabis' Advice: Skip Internships, Master AI"
Google DeepMind's CEO advises undergraduates that getting unbelievably proficient with AI tools is now more valuable than traditional internships for leapfrogging into a profession.
I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue. There's a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering. Clearly some powerful alien tool was handed around except it comes with no manual and everyone has to figure out how to hold it and operate it, while the resulting magnitude 9 earthquake is rocking the profession. Roll up your sleeves to not fall behind.