This incredibly critical part of "early 2026" doesn't seem to hold up very well:
Overall, they are making algorithmic progress 50% faster than they would without AI assistants
Yes the AI labs are using LLM-assisted development, but I am not aware of LLM-assisted research into better deep learning algorithms. "50% faster algorithmic progress" is a strange thing to predict about neural networks: historically it seems like algorithmic progress is very infrequent, and tends to be disruptive across the field. Likewise with the underlying algorithms in Codex and Claude Code - they aren't that sophisticated in the first place, and vibe coding an implementation 50% fasfer doesn't count as AI-assisted algorithmic progress. Maybe they mean cost?
And ironically they didn't predict the actual 2026 reality that Mythos sucks as an agent but commanded global attention as a cybersecurity tool.
Also this just seems childish and clearly hasn't really panned out:
But China is falling behind on AI algorithms due to their weaker models. The Chinese intelligence agencies—among the best in the world—double down on their plans to steal OpenBrain’s weights.
China has the exact same "algorithms"! The "algorithms" are on arXiv and the specific architectures are typically public. What China lacks is compute, and the US labs had a big head start on training data + RLHF.
The article hinges on AI automating R&D and finding something fundamentally more powerful and reliable than the current transformer LLMs. But that hasn't panned out at all. What has panned out is better scaffolding around running the LLMs in an iterative loop.
It actually seems to me AI 2027 holds up badly, unless your only takeaway is "AI gets better."
And ironically they didn't predict the actual 2026 reality that Mythos sucks as an agent but commanded global attention as a cybersecurity tool.
Also this just seems childish and clearly hasn't really panned out:
China has the exact same "algorithms"! The "algorithms" are on arXiv and the specific architectures are typically public. What China lacks is compute, and the US labs had a big head start on training data + RLHF.The article hinges on AI automating R&D and finding something fundamentally more powerful and reliable than the current transformer LLMs. But that hasn't panned out at all. What has panned out is better scaffolding around running the LLMs in an iterative loop.
It actually seems to me AI 2027 holds up badly, unless your only takeaway is "AI gets better."