Current Status & Resolution Mechanics Safe Superintelligence (SSI) remains a stealth research lab with no released model or product. The Artificial Analysis Intelligence Index top-5 is currently occupied entirely by Anthropic and OpenAI variants requiring mid-to-high-50s scores artificialanalysis.ai. Given SSI’s explicit "straight-shot, no products before superintelligence" doctrine 2 sources, a public leaderboard entry via an externally testable API (prong a) is highly unlikely. I
Thinking Machines Lab (TML) has secured massive 1 GW compute access but currently produces models well below the top-5 frontier threshold 2 sources. Given the 60% probability that incumbent dominance forces challengers into extended hardware deployments, TML is likely to reach the frontier only after successive scaling attempts, much like other LLM-focused neolabs , placing the median in December 2030. A 25% breakthrough branch—where their 2027 dep
Aligned against related compute and benchmark forecasts, the upper tail was drawn slightly forward to reflect the timeline of staggered hardware rollouts.
The Nature of Discovery Loop Discovery Loop was announced today (August 5, 2026) as a public benefit corporation by Jeff Dean, Sanjay Ghemawat, Oriol Vinyals, and Quoc Le, with the mission of automating the experimental loop of ML, science, and engineering research 2 sources. The seed round is co-led by Radical Ventures and Khosla Ventures, with Alphabet participating 2 sources. Notably, the company explicitly frames itself as consuming frontier AI models and large-scale comp
Reflection AI operates with significant compute but must catch a rapidly advancing frontier . Evaluated against a neolab landscape where catching incumbents often takes longer than anticipated, the dominant branch (60%) is that incumbents maintain their scaling pace. This requires Reflection to execute multiple multi-billion-dollar hardware cycles, pushing the median to August 2029. A 25% early breakthrough scenario—where their massive Colossus cluster yields immediate parity with the op
Set against related questions on the company's capital raise and compute strategy, the timeline was extended slightly in the right tail to account for the increased risk of a capital squeeze.
Final estimates: p10=2030-01-01, p25=2032-01-01, p50=2036-01-01, p75=2042-06-01, p90=2053-01-01. Initial logic and parameters are validated: Ineffable Intelligence's focus on experience-based reinforcement learning wired.com and its parallel to other paradigm-shifting bets serve as established context. This stable trajectory aligns with the broader view that experience-based learning will take a decade to reach frontier parity despite earlier intermediate
Kept stable, as this aligns with the broader view that experience-based learning will take a decade to reach frontier parity despite earlier intermediate milestones.
AMI Labs explicitly pursues non-LLM, JEPA-style world models 2 sources. Because this structure blocks direct competition on standard text leaderboards, resolution requires independent expert validation. Aligning with peers taking paradigm-shifting bets and related questions modeling a delayed commercialization path, there is a heavy concentration of outcomes in the 2030s, as novel non-LLM architectures will require significant time to match incumb
Set against related questions modeling a delayed commercialization path, this distribution was adjusted to reflect a heavy concentration of outcomes in the 2030s, as novel non-LLM architectures will require significant time to match incumbent frontier generality.
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