https://ai-rates-calculator.vercel.app/_next/static/chunks/fb998fb…?dpl=dpl_8cYoHQCKC52qNVeUXvH917c1m6SS

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1 capped_sw caps the tuned software rate.",legal_mode:"AIFM effort physics: capped clamps legal research effort at the physics ceiling; backsolve lets it exceed the ceiling to hit the target.",frac_stolen:"Fraction of the legal project's algorithmic progress the covert steals (the windfall plus ongoing software gains).",dormancy_years:"Years the covert lies low after the pause BEFORE starting construction (effective compute frozen; only stolen software accrues). No facility exists yet, so it cannot be detected — the detection clock starts when construction begins.",construction_years:"Years to build the covert datacenter, after the dormancy period (effective compute still frozen; only stolen software accrues). The facility is detectable while under construction.",compute_mode:"How the covert datacenter is sized: frontier_relative (a fraction of frontier compute at the pause) or absolute (a fixed H100e count).",total_h100e_frac:"Covert R&D compute as a fraction of the frontier's total compute at the pause (frontier_relative mode).",total_h100e:"Covert datacenter size in total H100e (absolute mode).",facility_w_per_h100e:"Facility power draw per H100e (W). Converts the covert compute into facility power (MW), which the detection model keys on.",optimal_site_mw:"Per-site power (MW) the covert splits its compute across (n = total_MW / this, floored at 1). Must lie within the detection-grid size range.",max_hw_rate:"Max hardware (training) scaling rate after the pause (OOM/yr). Unused by the actual_hw strategy.",max_sw_rate:"Flat ceiling (OOM/yr) on the legal tuned software rate (capped_sw strategy only); does not cap the setup windfall.",post_scaling_externalities_rate:"After the legal project reaches its target, it keeps DISCOVERING software efficiency at this flat rate (OOM/yr) as an unavoidable side-effect of ongoing alignment research — none deployed (TSP frozen), but it inflates the discovered research stock the covert steals at frac_stolen. 0 ⇒ the legal simply sits after its target. (actual_hw strategy only.)",min_sw_frac_of_tsp:"Mandatory minimum software during the scaling phase: software efficiency must grow at AT LEAST this fraction of the TSP (hardware) growth rate, on BOTH the used and discovered tracks, regardless of the AIFM effort ceiling (a guaranteed floor like the transparency windfall). 0 ⇒ no floor. (actual_hw strategy only.)",enable_deal_breakdown:"Gate for the deal-dissolution scenario: the deal dissolves some years after the pause ends. The consortium disbands (its trajectory freezes and covert stealing stops) and the leading company continues alone as its own project with no leakage.",deal_breakdown_years_after_pause_end:"When the deal dissolves, in years AFTER the end of the setup pause. The consortium freezes at that moment; the leading company takes over from the used (deployed) capability with the full discovered research stock.",breakdown_compute_mode:"What the leading company's post-dissolution compute share is measured against. frontier_at_pause: frontier R&D compute at the pause — compute built or kept during the deal is effectively destroyed at dissolution. world_at_breakdown: the world's compute at the moment the deal dissolves (the input series carried through the deal) — nothing is destroyed; the share is the % of current-world compute the largest post-dissolution project runs on.",breakdown_compute_reduction:"The leading company's post-dissolution compute as a fraction of the basis chosen above. Under frontier_at_pause, 30% ⇒ it restarts with 0.3× the compute the frontier had when the deal landed; under world_at_breakdown, 30% ⇒ it runs on 0.3× the compute the world has when the deal dissolves.",post_breakdown_compute_growth_rate:"Constant growth rate (OOM/yr) of the leading company's compute after the dissolution."},"HORIZON_LENGTH_EXPLANATION",0,"The coding time horizon is the maximum length of coding tasks frontier AI systems can complete with a success rate of 80%, with the length defined as the time taken by typical AI company employees who do similar tasks.","MILESTONE_EXPLANATIONS",0,{AC:"An AC can fully automate an AGI project's coding work, autonomously replacing the project's entire software engineering staff.","AI2027-SC":"A Superhuman Coder milestone from AI 2027 projections.",SAR:"A SAR can fully automate AI R&D.",SIAR:" The gap between a SIAR and the top AGI project human researcher is 2x greater than the gap between the top AGI project human researcher and the median researcher.","TED-AI":"A TED-AI is at least as good as top human experts at virtually all cognitive tasks.",ASI:"The gap between an ASI and the best humans is 2x greater than the gap between the best humans and the median professional, at virtually all cognitive tasks."},"MILESTONE_FULL_NAMES",0,{AC:"Automated Coder",SC:"Superhuman Coder",SAR:"Superhuman AI Researcher",SIAR:"Superintelligent AI Researcher","STRAT-AI":"Strategic AI","TED-AI":"Top-Expert-Dominating AI",ASI:"Artificial Superintelligence"},"SMALL_CHART_EXPLANATIONS",0,{automationFraction:"The fraction of coding work involved in frontier AI research that can be efficiently automated.",aiCodingLaborMultiplier:"A multiplier of 2x would mean that AIs were increasing coding productivity by as much as if the AGI project had an extra copy of all of their employees.",serialCodingLaborMultiplier:"The speedup in coding work from AI assistance. 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1are efficiency growth rate and the training system performance growth rate.",predictedRevenue:"The leading AGI developer's annualized revenue is predicted by extrapolating from May 2026 revenues and growth rates as an exponential trend in effective compute. (Annualized revenue is the most recent month's revenue multiplied by 12.) The red diamonds are Anthropic’s reported annualized revenue; they are shown for comparison only and do not influence the trend.",capabilityEci:"The Epoch Capabilities Index (ECI) score predicted by assuming that ECI is a linear function of log(effective compute).",capabilityEciRate:"How fast the Epoch Capabilities Index is rising, in index points per year. This is the slope of the ECI trend, computed analytically as points-per-OOM × the effective-compute progress rate rather than by differencing the plotted curve, so the two always agree exactly. At the ECI reference date it equals the present-day ECI growth-rate setting by construction.",excessSerialCodingLaborUplift:"The most capable model’s serial coding-labor uplift minus 1, measured with human labor and inference compute held at present-day levels. Unlike the revenue and time horizon trends, this is not an independent extrapolation; it is what the model produces.",softwareProgressRate:"By how many orders of magnitude software efficiency grows per year.",trainingSystemPerformanceGrowthRate:"By how many orders of magnitude training system performance grows per year.",trainingComputeH100e:"The number of H100-equivalents running the leading AI company's frontier training run at a given time — the instantaneous hardware training rate. Multiplied by FLOP_PER_H100E_YEAR it is the training rate in FLOP/yr (the hardware part of Training System Performance); cumulative training compute is its integral over time.",totalRdComputeH100e:"Total R&D compute at the leading AI company, in H100-equivalents: the sum of training compute, internal inference compute (for coding automation), and experiment compute — i.e. everything except commercial serving. All amortized over the year.",worldComputeH100e:"Total AI-relevant compute in the world, in H100-equivalents.",companyShareOfWorldPct:"The percentage of the world's AI-relevant compute operated by the leading AI company (its R&D compute plus commercial serving compute).",spendPctCumCommercial:"How the leading AI company splits its total compute, as percentages: inference for coding automation, training, experiments, and commercial (serving customers — the total minus everything else).",softwareEfficiency:"Software efficiency measures how efficiently the training process at a given time can convert training compute into performance. It is multiplied by training compute to get effective compute.",etsp:"Effective Training System Performance (ETSP) is the instantaneous effective-compute rate, in OOMs. It is the sum of training system performance (the hardware rate) and software efficiency (the algorithmic contribution); the stacked bands show how each contributes.",trainingSystemPerformance:"Training System Performance (TSP) is the instantaneous hardware training rate, in OOMs of FLOP/yr — i.e. the frontier run's H100e converted to FLOP/yr. Effective compute accumulates as the integral of ETSP = TSP + software efficiency.",trainingCompute:"Cumulative training compute (a stock), in OOMs of FLOP: the running integral of the training system performance. Effective compute is the same integral but including software efficiency.",experimentCapacity:"The number of experiments the leading AI company can run per unit time, where experiments are weighted by how much compute they use and how labor-intensive they are to code.",inferenceCompute:"The amount of compute used by the leading AI company for automating coding, in H100-equivalents.",experimentCompute:"The amount of compute used by the leading AI company for running experiments, in H100-equivalents.",humanLabor:"The number of human coders at the leading AI company."}],98403)},21719,e=>{"use strict";var t=e.i(43476),r=e.i(932),n=e.i(71645);let a=(0,n.createContext)(void 0);function i(){let e=(0,n.useContext)(a);if(!e)throw Error("useParameterHover must be used within a ParameterHoverProvider");return e}

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