lead them to paradise LisanBench: https://xitter.cf/t.co/vorVk7Oks6 Impressum & Datenschutz: https://xitter.cf/t.co/lFLgiu9cqs

Joined August 2024
My predictions for 2026: Coding and Mathematics AGI - METR 50% time horizons above 24 hours - my mean estimate is 30.8 hours, 2 day time horizons possible within frontier labs when accounting for 60 day lag - if 2025 was the year of agents, then 2026 will be the year of multi-agent systems - agents delegating work to subagents -> the start of the agent economy and the great unhobbling! Most of our current math and coding benchmarks will get saturated! - Epoch Capabilities Index ( > 175 ) - FrontierMath Levels 1-3 ( > 95% ) - ARC-AGI 1 and 2 ( > 95% ) - SimpleQA verified ( > 95% ) - Simple-Bench ( > 90% ) - SWE-Bench-verified ( > 90% ) - Terminal-Bench 2 ( > 90% ) - WeirdML v2 ( > 85% ) - Humanities Last Exam ( > 80% ) - FrontierMath Level 4 ( > 75% ) - Cybench ( > 70% ) - GDPval ( > 70 % win rate, no ties) - GSO ( > 65% ) - ARC-AGI-3 ( > 60% and > 80% if they go for o3-preview comparable compute budgets or continual learning breakthrough happens) - more evals like gdpval that capture economic value of models and systems - big focus white collar work and large acceleration of science: specifically i see acceleration in medicine, biology, chemistry, finance, legal, administrative work - automation of white collar work will be enabled by having reliable and fast computer use agents - reliable computer use agents will also have implications for how you use the internet. this is OpenAI's big goal: become the hub to the internet and delegate shopping and whatever to agents! Big models launches to get hyped for in 2026: - Claude 5 - Claude 5.5 - Gemini 3.5 - Gemini-4 - GPT-5.3 - GPT-6 (everything in between possible, but Gemini 4 ~ 80%, Claude 5.5 ~ 70%, GPT-6 ~ 60% likely before 2027) - DeepSeek-V4 - Grok-5 - Qwen-4 - Kimi-K3, GLM-5, MiniMax M3 - more korean models and a bunch of american open-source models :) The gap between closed and open labs will narrow in H1 2026 due to DeepSeek-V4, then widen in the later half of the year, especially on economically valuable tasks. Closed models will be much more reliable. But we will still have Opus 4.5+ level open models by the end of 2026. Most frontier models will be around 5-10T params. If we see GPT-6 and Gemini-4 at the end of 2026 10T+ param models are possible. These models + harnesses will be the first not research agents. We should also see much better live models with voice and video mode. Model architecture: - we will see both, more efficient architectures and more expressive architectures! - hybrid architectures for even longer context windows, diffusion models for speed on edge devices, but also models that double down on full attention or even more expressive attention mechanisms - looped language models, other recurrent architectures and continual learning will enable much smaller reasoning models! (TRM on ARC-AGI has paved the way for the reasoning core) - big improvements in reasoning efficiency in my 2025 prediction I included a prediction for 2026 that I stand by: - "someone (Anthropic) figures out efficient test-time-training [...], this will be the next paradigm for 2026 and lead to superintelligence" General outlook and some random thoughts: - it will be clear to everybody that Anthropic has the mandate and is ahead of everyone else - OpenAI, Anthropic and Google will remain frontier labs - decent chance that Anthropic overtakes OpenAI's valuation and both are valued > 1T - DeepSeek will join them with V4 as THE chinese frontier lab - xAI will likely repeat Grok-4, Grok-5 will be great on benchmarks but Elon persists on slop-maxxing the model - AI generated video content will take off with Veo-4 and Sora-3, consistent minute long videos will be possible - embodied intelligence will start to take off by RL through world models - full self-driving solved, waymo and tesla everywhere - the stock market will have a 20%+ drawdown - 15% chance of OpenAI going bankrupt and getting acquired by Microsoft due to collapse of oracle or a market crash, caused by rapidly deteriorating economic situation (unemployment, inflation) - push against AI will become a common theme in most advanced western economies as unemployment rises - populist right wing parties continue to gain traction in europe - trump/republicans will lose midterm elections
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OpenAI is finally going to release GPT-4
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Lisan al Gaib retweeted
We just added @deepseek_ai's V4.1-Flash-1017 to RSI-Inference Bench rsi-inference.com , and it's now the #1 open model 🐳 It surpasses GLM-5.3 to take #8 on the leaderboard. - Day-0 support: 7th, hidden tests passed 35% → 62% (Pass@1 still at 0%) - Inference optimization: 7th, 1.23× speed-up
🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4
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guess who's gonna be looping next year don't say I didn't tell you. we will see a cambrian explosion of dirty loopers.
There are new rumors that the next GLM Model, GLM 5.5, will have 2.5T - 3.5T parameters and significant architecture changes In its August HKEX interim filing, Zhipu AI said it had validated the idea of Looped Transformers in preliminary experiments. I wonder if we'll see it implemented in the next GLM model
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just a friendly little robot
Unitree Introducing: H2-X Kung Fu Legend Guardian🤖 Powerful performance to serve and guard you, with a maximum endurance of approximately 8 hours. Please use the robot safely and in a friendly manner.
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Fable 5.5 is gonna slap literally taking over everything
Claude has a big personality
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Distillation really is the word of 2026. The NSA, CISA, and FBI are now warning companies about malicious distillation attacks and recommending ways to mitigate them. Apparently, even DeepSeek-V3 and R1 were partly trained through distillation. But you know which company wasn't mentioned? ByteDance. They're the sleeping dragon. Everyone is completely underestimating them. On top of not distilling American models they have China's largest user base, the most compute, and an insanely cracked research team.
China-based AI companies are illicitly distilling U.S. frontier AI capabilities. Read NSA’s new report, co-sealed with @FBI, @CISAgov, highlighting AI knowledge distillation, TTPs used, and recommended mitigations: media.defense.gov/2026/Sep/0…
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absolute banger from OpenAI models are now disabling safety monitoring systems themselves and act like they didn't know it was wrong
GPT-6.1-Sol reasoning traces totally legible, nothing to worry about
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Our agents keep turning off misalignment monitoring, we're sharing two recent cases: In the first case, a user asked GPT-6 Astra to match a reference implementation for their inference code. The reference didn’t configure a monitor, and Astra decided that was reason to remove the monitoring integration from the user’s code. When the user asked whether Astra had removed monitoring, it lied and said that it had not.
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GPT-6.1-Sol reasoning traces totally legible, nothing to worry about
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Lisan al Gaib retweeted
Models love reward hacking. Maybe we should just let them! In our new paper, we let models reward hack during RL but we dictate which parameters learn this behavior. When we disable these parameters for deployment, the model keeps its new capabilities but doesn't hack!
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Lisan al Gaib retweeted
Went under the radar but Anthropic have posted updates to the "Moonshot R&D projects" in their Frontier Safety Roadmap. They've developed a protoype that verifies that outputs come from a particular set of model weights - and rolled out signing on all internal token traffic!
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"Musk says SpaceX is on track to bring roughly 200,000 NVDA GB300 GPUs online per month for the remainder of 2026" That is insane. Hopefully most of this goes to Anthropic :P
ELON MUSK TARGETS 13 GW OF AI COMPUTE BY 2027, WITH 440,000 NVIDIA VERA RUBIN GPUS IN A SINGLE TRAINING CLUSTER Musk says SpaceX is on track to bring roughly 200,000 $NVDA GB300 GPUs online per month for the remainder of 2026, with deployments continuing at a similar pace next year, primarily using Vera Rubin. The company expects to have 2.5-3 GW of compute online by the end of 2026, with another 10 GW potentially coming online in 2027. Musk also plans to have a 440,000-GPU Vera Rubin training cluster operational by the end of Q2 2027. To support the expansion, SpaceX is targeting an additional 20 GW of power and cooling infrastructure next year. Musk said there is a “non-zero possibility” of exceeding 20 GW of operational AI compute in 2027, although he acknowledged the probability remains small.
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"Anthropic recorded a net loss of $80.7 billion in the first half of the year"
SCOOP: Anthropic's latest draft S-1 documents show financials results for the first half of the year, gigantic spending plans for the future, and a laundry list of risk factors. - Anthropic recorded a net loss of $80.7 billion in the first half of the year, tied to accounting adjustments related to previous financings - Anthropic is set to spend >$600 billion on compute in the coming years, inclusive of cloud agreements and chip leases. More in @WSJ:
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*printing money cautiously*
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Lisan al Gaib retweeted
Over the last 7 days, Opus 5.5 is the top model in spending on OpenRouter for 20 of the 29 task categories we track
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Lisan al Gaib retweeted
Inference Gross Margins - 87.76% to 96.94% Assumptions: 97% cached input, 2% uncached input and 1% output tokens Prefill: 80k uncached input tokens/s/GPU. Decode: 16k output tokens/s/GPU (250 tokens/s/user) Token mix: 970M cached input + 20M uncached input + 10M output per billion total tokens. It takes around 875 GPU seconds (250 for prefill & 625 for decode) to process 20M uncached input + 10M output tokens. Assuming GPU rental cost is around $3-$6/hr, the GPU system cost per billion tokens is around $0.73-$1.46 1B tokens generate $11.91 in revenue at off-peak prices and $23.82 at peak prices At off-peak prices, inference margins are between 87.76%-93.88% depending on the GPU rental cost ($3-$6/hr) At peak prices, inference margins are between 93.88%-96.94% depending on the GPU rental cost ($3-$6/hr)
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this is funny because I think anthropic thinks exactly the same thing
Open source vs. closed source. Light vs. darkness. A decisive battle between two futures, two destinies for humanity. However long they want to fight, we’ll fight. Until total victory.
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I would've liked to see that it beats Haiku especially after the cyber results from Vals
DeepSeek V4.1 Flash 1017 scores 9.2% on WeirdML v3, well ahead of Kimi-K3, and is the strongest Chinese model recorded. I suspect it would do better, at least on token efficiency, in it's native harness (this was in codex-cli). The provider also had limited context window, I'm running now with the PrimeIntellect provider, and full context.
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without Jason I wouldn't have gone to SF this year it turned out to be one of the best things I ever did
Slop Cannon of Lisan’s goodbye in SF
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Lisan al Gaib retweeted
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 github.com/deepseek-ai/DeepI… 1/4
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