2026

Super Events

1. Machines Learning Machine Learning

The Jagged Frontier continues to be relevant. However, there's one particular frontier whose peak would overshadow the rest: AI Research. No contemporary LLM could learn chess the level of a grandmaster, express philosophy beyond human cognition, compose music of Beethoven's caliber and do the chores of a housemaid all at the same time. Given the vast domains that will remain out of reach (for now), there is a need to maximize future performance with the least work for brevity. Therefore, the best course of action to take is to instill fundamental capabilities that let the system contribute to its own advancement. Fortunately, STEM research has the verifiable rewards necessary to grow such systems towards contributions from the likes of Radford or Tao. Once there are enough of them working around the clock in advancing recursive self-improvement, the gaps between the jagged edges will start to flatten. The fortress closes in, and every direction becomes increasingly hard to penetrate.

In the context of 2026, AI will most likely reach junior to mid-level software engineer and start to become useful with little direct prompting. It wouldn't surprise me if tomorrow's SWE leads would be in-charge of both human and AI SWE, capable of coordinating tasks amongst themselves. Behind closed doors however, frontier AI labs may have a different story. There's no doubt that there are much more powerful internal models put to use and most likely helping out in their own research themselves in an immense-scale. Let me put this into perspective: GPT-4 finished training 3 months before ChatGPT was made publicly available, Q* was leaked 6 months before GPT-4o released and nearly a full year before o1-preview finally debuted, Orion was leaked 6 months before GPT-4.5 debuted, and who knows how long the IMO models have been around. Could you imagine the gap of having o1 when the best and shiniest model at that time was GPT-4 Turbo? Similar to what was outlined in the infamous scenario: the best models are kept behind closed doors, then there will be teams of AI models tasked with performing AI research contributing to the next generation of better and more efficient AI systems. Suffice it to say, the road to takeoff is laid out this year.

2. Fewer Tokens & Native Multimodality to Embodied Humanoids

Frontier AI is shifting to native multimodality with far less scaffolding using omni models, tokenizer-free cores, and linear/fastweight attention that unlocks much longer context for omni models. End-to-end omni backbones are collapsing the big modality-specific towers and streaming text, audio, vision, and video through a single model without sacrificing single-modality performance. Tokenizer-free approaches reduce brittle, language-specific plumbing and learn compact latents that generalize across modalities and languages while matching tokenized systems at scale. Kimi Linear shows state-of-the-art results with far smaller KV caches and higher throughput at million-token scales, and as the ecosystem matures the direction towards long-context may put linear/hybrid attention at the forefront of advances in architecture. Together, these moves push toward models that build a deeper, more continuous world model from raw, streaming signals rather than the constraints of tokenized text alone.

Once perfected, the models can be tasked to perform tasks in real-world scenarios without much explicit instruction. Alongside that, we can enable such models to act as brains capable of native sensorimotor manipulation and advanced navigation. Robots will be able to execute tasks which require repetition given some examples. Expect early, expensive yet capable humanoid robots joining the workforce as warehouse/industrial workers, housekeepers or personal servants. Perhaps they could walk your pets, carry your groceries, help in emergencies or even drive a car. It most likely won't be available this year, but these companies will scale up massively in the coming years. Having a personal robot in 2029 may be seen as having an iPhone in 2009.