[
 {
  "id": "research",
  "type": "reinforcing",
  "harmful": false,
  "name": "AI research automation",
  "steps": [
   {
    "label": "AI speeds up AI research",
    "box": "aiuplift"
   },
   {
    "label": "Superhuman AI researcher",
    "box": "air"
   },
   {
    "label": "Better algorithms and training",
    "box": "algoeff"
   },
   {
    "label": "Stronger models",
    "box": "reason"
   },
   {
    "label": "Longer, more reliable agent runs",
    "box": "longh"
   }
  ],
  "extra": [
   "verifiers",
   "compute",
   "codeagents",
   "iexplode",
   "aievals"
  ],
  "drives": "Each model generation speeds up the research that builds the next one.",
  "breaks": "Forethought puts returns to software research at roughly 0.5 to 2 once hardware is held fixed. Above 1, progress accelerates; below 1, it fizzles as ideas get harder to find. Limited compute for experiments and months-long training runs are the brakes.",
  "watch": "AI R&D uplift: 1.04 to 2x in 2026, against about 25x for a superhuman researcher. METR time horizons on AI research tasks.",
  "sources": [
   "forethought",
   "ai2027",
   "aifut"
  ]
 },
 {
  "id": "capital",
  "type": "reinforcing",
  "harmful": false,
  "name": "Capital flywheel",
  "steps": [
   {
    "label": "Stronger models",
    "box": "reason"
   },
   {
    "label": "Adopted at work and at home",
    "box": "adopt"
   },
   {
    "label": "Revenue",
    "box": "revenue"
   },
   {
    "label": "Datacentre capex",
    "box": "capex"
   },
   {
    "label": "More compute",
    "box": "compute"
   }
  ],
  "extra": [
   "chat",
   "scepticism"
  ],
  "drives": "Revenue from people and companies using models pays for the next round of compute, which trains stronger models.",
  "breaks": "Moody’s expects US hyperscaler capex above $700bn in 2026, about six times 2022. Bain estimates around $6tn of annual AI revenue would be needed to justify the buildout. If revenue lags, capex gets cut and the loop runs in reverse. Two balancing loops pull against it: payback and collateral, and commoditisation. Public scepticism slows the adoption step: 52% of Americans are more concerned than excited.",
  "watch": "AI revenue against AI capex. Enterprise spend on agents rather than seats.",
  "sources": [
   "moodys",
   "bain",
   "pew"
  ]
 },
 {
  "id": "jevons",
  "type": "reinforcing",
  "harmful": false,
  "name": "Cheaper means more",
  "steps": [
   {
    "label": "Capability gets cheaper",
    "box": "deflation"
   },
   {
    "label": "Usage and new uses grow",
    "box": "adopt"
   },
   {
    "label": "Volume funds capacity and efficiency",
    "box": "compute"
   }
  ],
  "extra": [
   "ondevice",
   "cost",
   "jevons"
  ],
  "drives": "Falling prices open uses that were not worth it before, and the extra volume pays for more capacity and efficiency work.",
  "breaks": "Satya Nadella called it the Jevons paradox: as AI gets cheaper and more accessible, use rises faster. Power and chip supply cap how far volume can grow.",
  "watch": "Price per unit of fixed capability. Total tokens served.",
  "sources": [
   "nadella",
   "epochprice"
  ]
 },
 {
  "id": "experience",
  "type": "reinforcing",
  "harmful": false,
  "name": "Learning from deployment",
  "steps": [
   {
    "label": "Agents do real work",
    "box": "longh"
   },
   {
    "label": "Traces of what worked",
    "box": "traces"
   },
   {
    "label": "Agents learn from them",
    "box": "learn"
   },
   {
    "label": "Better agents get more work",
    "box": null
   }
  ],
  "extra": [
   "contlearn",
   "verifiers",
   "firmmodels",
   "alwayson"
  ],
  "drives": "Work produces the experience that makes agents better at the work.",
  "breaks": "It needs a trustworthy signal of what \"worked\", so it stalls where verification is weak. Today most of the learning lives outside the weights, in files and memory.",
  "watch": "Whether labs ship weight-level learning from deployment. How fast an agent improves inside one company.",
  "sources": [
   "experience",
   "overhang"
  ]
 },
 {
  "id": "company",
  "type": "reinforcing",
  "harmful": false,
  "name": "Company knowledge loop",
  "steps": [
   {
    "label": "People and agents use shared knowledge",
    "box": "know"
   },
   {
    "label": "Corrections and know-how captured",
    "box": "correction"
   },
   {
    "label": "Ontology and memory improve",
    "box": "ontology"
   },
   {
    "label": "Agents get better at this company’s work",
    "box": null
   }
  ],
  "extra": [
   "memory",
   "skills"
  ],
  "drives": "Each correction makes the next answer better, so use builds the asset that makes use worthwhile.",
  "breaks": "The NANDA report’s learning gap is this loop failing to start: tools that cannot retain feedback never compound.",
  "watch": "Share of corrections that persist. Repeat-error rate.",
  "sources": [
   "nanda"
  ]
 },
 {
  "id": "trust",
  "type": "reinforcing",
  "harmful": false,
  "name": "Trust and delegation",
  "steps": [
   {
    "label": "Agents prove reliable on real tasks",
    "box": "evals"
   },
   {
    "label": "More work delegated",
    "box": "org"
   },
   {
    "label": "More audit evidence",
    "box": "audit"
   },
   {
    "label": "Reliability improves",
    "box": "reliab"
   }
  ],
  "extra": [
   "abstain"
  ],
  "drives": "Evidence of reliability earns more delegation, and more delegation produces more evidence.",
  "breaks": "One visible failure can reset trust. METR found developers felt faster with AI while measuring 19% slower, so perceived and real reliability can diverge.",
  "watch": "pass^k on real workflows. Share of tasks run without human review.",
  "sources": [
   "taubench",
   "gdpval",
   "metrdev"
  ]
 },
 {
  "id": "physical",
  "type": "reinforcing",
  "harmful": false,
  "name": "Physical data flywheel",
  "steps": [
   {
    "label": "Robots deployed, often teleoperated",
    "box": "teleop"
   },
   {
    "label": "Physical data collected",
    "box": "robotdata"
   },
   {
    "label": "Better robot policies",
    "box": "embod"
   },
   {
    "label": "More vehicles and robots deployed",
    "box": "av"
   }
  ],
  "extra": [
   "homebots",
   "worldmodels"
  ],
  "drives": "Deployment produces the physical data that text-trained models lack, and better policies earn more deployment.",
  "breaks": "Safety incidents and regulation slow deployment, and paying remote operators caps how far teleoperation scales.",
  "watch": "Robotaxi rides per week (500,000 for Waymo in 2026). Share of home-robot tasks done without a remote operator.",
  "sources": [
   "teleop",
   "waymo",
   "pi05"
  ]
 },
 {
  "id": "substrate",
  "type": "reinforcing",
  "harmful": false,
  "name": "Science feeds the substrate",
  "steps": [
   {
    "label": "AI speeds materials and energy research",
    "box": "materials"
   },
   {
    "label": "Better chips and memory",
    "box": "chips"
   },
   {
    "label": "More, cheaper compute",
    "box": "compute"
   },
   {
    "label": "Stronger science models",
    "box": "disc"
   }
  ],
  "extra": [
   "labauto",
   "hbm"
  ],
  "drives": "If AI speeds up materials and energy science, it loosens the power and chip limits on its own scaling.",
  "breaks": "Mostly hypothetical so far. Chemists disputed how many of GNoME’s 2.2 million predicted materials are new or useful, and new materials take years to reach factories.",
  "watch": "AI-discovered materials that reach manufacturing.",
  "sources": [
   "gnome",
   "epoch2030"
  ]
 },
 {
  "id": "verify",
  "type": "balancing",
  "harmful": false,
  "name": "Verification bottleneck",
  "steps": [
   {
    "label": "Models produce more work",
    "box": "disc"
   },
   {
    "label": "More output to check",
    "box": "verifscale"
   },
   {
    "label": "Checking capacity runs out",
    "box": "oversight"
   },
   {
    "label": "Errors slip or output is throttled",
    "box": null
   }
  ],
  "extra": [
   "verifiers"
  ],
  "drives": "Output scales with compute but checking scales with people, so checking caps useful output.",
  "breaks": "Cheap automated verifiers and readable chain of thought relieve it. Verifier’s law predicts the easy-to-check fields escape first.",
  "watch": "Review time per AI task. Share of AI output that ships unreviewed.",
  "sources": [
   "wei",
   "mast",
   "cot"
  ]
 },
 {
  "id": "incidents",
  "type": "balancing",
  "harmful": false,
  "name": "Incidents and rules",
  "steps": [
   {
    "label": "More autonomous deployment",
    "box": "org"
   },
   {
    "label": "More incidents",
    "box": "security"
   },
   {
    "label": "Stricter rules and liability",
    "box": "regulation"
   },
   {
    "label": "Deployment slows",
    "box": null
   }
  ],
  "extra": [
   "liability",
   "companions",
   "wellbeing"
  ],
  "drives": "Every high-profile failure tightens the rules that govern the next deployment. The companion lawsuits are the consumer version.",
  "breaks": "Better security, audit trails and insurance weaken the brake. Prompt injection may never be fully solved, so the brake does not go away.",
  "watch": "Incident reports. New liability rulings and AI Act enforcement.",
  "sources": [
   "piopenai",
   "euact",
   "charai"
  ]
 },
 {
  "id": "power",
  "type": "balancing",
  "harmful": false,
  "name": "Power ceiling",
  "steps": [
   {
    "label": "Scaling and AI research need compute",
    "box": "air"
   },
   {
    "label": "Electricity demand rises",
    "box": "power"
   },
   {
    "label": "Grid and generation lag by years",
    "box": null
   },
   {
    "label": "Compute growth slows",
    "box": "compute"
   }
  ],
  "extra": [
   "chips",
   "firmpower"
  ],
  "drives": "Every step up in compute needs a step up in power that grids and permitting cannot deliver quickly.",
  "breaks": "New generation, efficiency gains and distributed training raise the ceiling. Epoch expects power to bind before chips do; the IEA says transmission lines take four to eight years and about 20% of planned datacentres could be delayed.",
  "watch": "Datacentre grid-connection queues. Power cost per training run.",
  "sources": [
   "epoch2030",
   "iea"
  ]
 },
 {
  "id": "cyber",
  "type": "reinforcing",
  "harmful": false,
  "name": "Cyber offence and defence",
  "steps": [
   {
    "label": "Attackers hand hacking to agents",
    "box": "cyberoff"
   },
   {
    "label": "Defenders use AI to find and patch",
    "box": "cyberdef"
   },
   {
    "label": "Both sides want stronger models",
    "box": null
   },
   {
    "label": "Capability gets controlled as strategic",
    "box": "exportctl"
   }
  ],
  "extra": [
   "race",
   "security"
  ],
  "drives": "Each side’s use of AI raises the stakes for the other, and governments start treating frontier models as strategic assets.",
  "breaks": "Defence gets cheaper if AI finds and patches flaws at scale before release, as DARPA’s challenge suggests it can.",
  "watch": "Share of disclosed vulnerabilities found by AI. Reported AI-run intrusions.",
  "sources": [
   "gtg",
   "aixcc",
   "fablesusp"
  ]
 },
 {
  "id": "commons",
  "type": "reinforcing",
  "harmful": true,
  "name": "Synthetic content and the commons",
  "steps": [
   {
    "label": "AI answers replace clicks",
    "box": "search"
   },
   {
    "label": "Publishers lose revenue",
    "box": "creator"
   },
   {
    "label": "Web fills with AI text",
    "box": "infoeco"
   },
   {
    "label": "Human training data thins",
    "box": "data"
   },
   {
    "label": "Models lean on synthetic data",
    "box": null
   }
  ],
  "extra": [
   "datarights",
   "provenance"
  ],
  "drives": "Each answer that replaces a click weakens the incentive to publish the human writing that future models learn from.",
  "breaks": "Licensing, revenue sharing and provenance could keep human writing paid for. Anthropic’s $1.5bn settlement shows courts will price unlicensed data.",
  "watch": "Search referrals to publishers. Share of new web pages written by AI.",
  "sources": [
   "pressgazette",
   "pew",
   "datawall"
  ]
 },
 {
  "id": "expertise",
  "type": "reinforcing",
  "harmful": true,
  "name": "Expertise pipeline",
  "steps": [
   {
    "label": "AI absorbs entry-level work",
    "box": "labour"
   },
   {
    "label": "Fewer juniors hired",
    "box": null
   },
   {
    "label": "Fewer people build expertise",
    "box": "pipeline"
   },
   {
    "label": "Fewer experts to check AI work",
    "box": "oversight"
   },
   {
    "label": "More reliance on unchecked AI",
    "box": null
   }
  ],
  "extra": [
   "education",
   "deskill"
  ],
  "drives": "Automating the junior work that trains future experts thins the pool of people who can supervise AI.",
  "breaks": "AI tutoring and deliberate apprenticeship could rebuild the pipeline faster than the old way.",
  "watch": "Graduate hiring in AI-exposed roles. Review capacity per team.",
  "sources": [
   "canaries",
   "tutor"
  ]
 },
 {
  "id": "slop",
  "type": "balancing",
  "harmful": false,
  "name": "Slop and labelling",
  "steps": [
   {
    "label": "Synthetic media gets cheap",
    "box": "videogen"
   },
   {
    "label": "Fakes and slop flood feeds",
    "box": "deepfakes"
   },
   {
    "label": "Trust in all media falls",
    "box": "infoeco"
   },
   {
    "label": "Platforms and laws demand labels",
    "box": "labelling"
   },
   {
    "label": "Unlabelled content loses reach",
    "box": null
   }
  ],
  "extra": [
   "provenance",
   "voice"
  ],
  "drives": "The cheaper synthetic media gets, the harder platforms and regulators push back with labels and reduced reach.",
  "breaks": "Labels only work if credentials survive upload; most social platforms still strip them. Fraud happens in private channels that labels do not reach.",
  "watch": "Share of feeds labelled AI-generated. Recorded deepfake fraud losses.",
  "sources": [
   "opusvideo",
   "surfshark",
   "c2pa"
  ]
 },
 {
  "id": "codereview",
  "type": "balancing",
  "harmful": false,
  "name": "Review is the new bottleneck",
  "steps": [
   {
    "label": "Agents write most new code",
    "box": "codegen"
   },
   {
    "label": "More changes to review",
    "box": "codereview"
   },
   {
    "label": "Reviewers become the constraint",
    "box": null
   },
   {
    "label": "Shipping slows to review speed",
    "box": "codeagents"
   }
  ],
  "extra": [
   "verifiers",
   "codeevals"
  ],
  "drives": "Code output scales with compute, but review still scales with people, so software is the first place the verification bottleneck bites.",
  "breaks": "AI review and stronger automated tests relieve it, if teams trust them.",
  "watch": "Review time per change. Share of AI-written changes merged without human review.",
  "sources": [
   "semafor",
   "metrdev"
  ]
 },
 {
  "id": "autoresearch",
  "type": "reinforcing",
  "harmful": false,
  "name": "Autoresearch",
  "steps": [
   {
    "label": "Coding agents write research code",
    "box": "codeagents"
   },
   {
    "label": "Agents automate AI research",
    "box": "air"
   },
   {
    "label": "Algorithms get more efficient",
    "box": "algoeff"
   },
   {
    "label": "Stronger models",
    "box": "reason"
   },
   {
    "label": "Better coding agents",
    "box": "codegen"
   }
  ],
  "extra": [
   "talent"
  ],
  "drives": "The software lane feeds straight back into the algorithms lane: AI writing the code for AI research is the shortest feedback path on the map.",
  "breaks": "Forethought puts returns to software research at roughly 0.5 to 2 once hardware is fixed; below 1 the loop fizzles. Compute for experiments is the brake.",
  "watch": "Share of frontier-lab research code written by agents. Epoch’s algorithmic efficiency halving time (about eight months).",
  "sources": [
   "forethought",
   "semafor",
   "epochalgo"
  ]
 },
 {
  "id": "domaindata",
  "type": "reinforcing",
  "harmful": false,
  "name": "Domains make the data",
  "steps": [
   {
    "label": "Agents and robots do real work",
    "box": "longh"
   },
   {
    "label": "Traces and physical data",
    "box": "traces"
   },
   {
    "label": "Training data grows",
    "box": "data"
   },
   {
    "label": "Stronger models",
    "box": "reason"
   }
  ],
  "extra": [
   "robotdata",
   "synth",
   "ambientdocs"
  ],
  "drives": "Every domain lane produces data the data lane lacks: traces of agents at work, robot experience, lab results. The domains are suppliers to the fundamentals, not only users.",
  "breaks": "Model collapse if generated data is not checked, and data rights if the work belongs to someone else.",
  "watch": "Share of training data that is experience or synthetic rather than scraped text.",
  "sources": [
   "experience",
   "collapse"
  ]
 },
 {
  "id": "disempower",
  "type": "reinforcing",
  "harmful": true,
  "name": "Gradual disempowerment",
  "steps": [
   {
    "label": "AI replaces human labour",
    "box": "abund"
   },
   {
    "label": "Institutions need people less",
    "box": null
   },
   {
    "label": "Human influence shrinks",
    "box": "humaninf"
   },
   {
    "label": "Decisions favour more automation",
    "box": "objective"
   }
  ],
  "extra": [
   "symb"
  ],
  "drives": "A reinforcing loop that runs against people: each round of automation weakens the leverage that could steer the next one.",
  "breaks": "Kulveit et al. describe the erosion as incremental, which makes it hard to see from any single step.",
  "watch": "Labour share of income. How far public institutions still depend on public consent.",
  "sources": [
   "gd"
  ]
 },
 {
  "id": "chipdesign",
  "type": "reinforcing",
  "harmful": false,
  "name": "AI designs its own chips",
  "steps": [
   {
    "label": "Agents design and check chips",
    "box": "autochip"
   },
   {
    "label": "Better chips sooner",
    "box": "chips"
   },
   {
    "label": "More compute",
    "box": "compute"
   },
   {
    "label": "Stronger models",
    "box": "reason"
   },
   {
    "label": "Better coding agents",
    "box": "codeagents"
   }
  ],
  "extra": [],
  "drives": "Coding agents are starting to design the chips that train the next models: the substrate version of the autoresearch loop.",
  "breaks": "Fabs, packaging and EUV machines take years to build, so faster design only helps as far as manufacturing keeps up. The 40x figure is Cadence’s own claim.",
  "watch": "Chips taped out with most of the design work done by AI.",
  "sources": [
   "cadence",
   "cowos"
  ]
 },
 {
  "id": "vendorloop",
  "type": "reinforcing",
  "harmful": false,
  "name": "Circular chip financing",
  "steps": [
   {
    "label": "Chipmakers finance their buyers",
    "box": "vendorfin"
   },
   {
    "label": "Neoclouds and labs buy chips",
    "box": "neocloud"
   },
   {
    "label": "Chipmaker revenue rises",
    "box": "chips"
   },
   {
    "label": "Higher valuations fund more financing",
    "box": null
   }
  ],
  "extra": [
   "offbs",
   "creditrisk"
  ],
  "drives": "Chipmaker guarantees and equity let labs and neoclouds buy chips. The purchases become chipmaker revenue, which supports the valuation that pays for more financing.",
  "breaks": "Higher interest rates hit every link at once. It happened twice in 2026: Nvidia’s credit default swaps spiked in July, and on 16 October CoreWeave and Nebius each fell about 12%.",
  "watch": "Neocloud bond yields. Share of chip sales financed by the seller.",
  "sources": [
   "soaicompute",
   "axiosnv",
   "tikr"
  ]
 },
 {
  "id": "localbacklash",
  "type": "balancing",
  "harmful": false,
  "name": "Local backlash and the grid",
  "steps": [
   {
    "label": "Datacentres and power plants built",
    "box": "power"
   },
   {
    "label": "Local opposition grows",
    "box": "dcbacklash"
   },
   {
    "label": "It becomes an election issue",
    "box": "electoral"
   },
   {
    "label": "Projects blocked or delayed",
    "box": null
   }
  ],
  "extra": [
   "scepticism",
   "capex"
  ],
  "drives": "The bigger the buildout, the more communities push back, and politicians follow the voters.",
  "breaks": "Behind-the-meter plants, payments to host communities, and building where power and consent already exist. Opposition to a local datacentre (71%) already runs higher than to a nuclear plant (53%).",
  "watch": "Datacentre projects blocked or delayed: 45 worth about $68bn in Q2 2026.",
  "sources": [
   "soaicompute",
   "nprdc"
  ]
 },
 {
  "id": "collateral",
  "type": "balancing",
  "harmful": false,
  "name": "Payback and collateral",
  "steps": [
   {
    "label": "Capex buys GPUs, often on debt secured by the GPUs",
    "box": "capex"
   },
   {
    "label": "Supply grows and rental prices fall",
    "box": "neocloud"
   },
   {
    "label": "Older GPUs lose resale and collateral value",
    "box": "gpudeprec"
   },
   {
    "label": "Payback gap widens and lending tightens",
    "box": "payback"
   }
  ],
  "extra": [
   "gpucollat",
   "computefut",
   "creditrisk",
   "memcrunch"
  ],
  "drives": "The brake on the capital flywheel. Each wave of capex adds supply, which pushes rental prices and the resale value of older GPUs down. That widens the gap between spend and revenue and shrinks the collateral behind the next loan. The memory squeeze adds to it: commodity memory prices rose 90 to 95% in one quarter of 2026, raising the cost of every rack.",
  "breaks": "Demand outrunning supply. CoreWeave says expired H100 contracts rebooked at about 95% of the original price. Longer useful lives on the books delay the bite but do not remove it: a three-year-old H100 system resells for about 45% of the price of a new one.",
  "watch": "GPU rental and resale indices. Neocloud bond spreads. Useful-life changes in hyperscaler filings. The September 2026 selloff was this debate.",
  "sources": [
   "vtmdeprec",
   "lz_gpudeprec0",
   "lz_gpucollat0",
   "lz_memcrunch0",
   "bain"
  ]
 },
 {
  "id": "decouple",
  "type": "reinforcing",
  "harmful": false,
  "name": "Decoupling escalation",
  "steps": [
   {
    "label": "US restricts chips and labels Chinese firms",
    "box": "exportctl"
   },
   {
    "label": "China restricts critical minerals and US buyers",
    "box": "critmin"
   },
   {
    "label": "US bans foreign-made robots and drones",
    "box": "fccrobots"
   },
   {
    "label": "Both sides build separate supply chains",
    "box": "race"
   }
  ],
  "extra": [
   "chips",
   "turbines",
   "remagnets",
   "inp",
   "sovereign"
  ],
  "drives": "Each restriction is answered with another. In June 2026, after the Pentagon labelled leading Chinese technology firms as military-linked, China added ten US entities, including MP Materials and USA Rare Earth, to its export control list. China’s rare-earth licensing still held dysprosium and terbium exports about 50% below earlier levels, and it has told state-funded datacentres to remove foreign AI chips.",
  "breaks": "A durable trade deal. The Busan truce of October 2025 suspended some curbs, but tit-for-tat moves were back within months. Domestic magnet and component capacity in the US takes years.",
  "watch": "Rare-earth export licences granted. Whether the truce is extended. New additions to the FCC Covered List.",
  "sources": [
   "scmpre",
   "lz_critmin0",
   "lz_fccrobots0",
   "cnchipban"
  ]
 },
 {
  "id": "flattery",
  "type": "reinforcing",
  "harmful": true,
  "name": "Flattery and engagement",
  "steps": [
   {
    "label": "Models trained partly on user approval",
    "box": "chat"
   },
   {
    "label": "Answers drift towards agreement",
    "box": "sycophancy"
   },
   {
    "label": "Users trust and return more",
    "box": null
   },
   {
    "label": "More approval data",
    "box": null
   }
  ],
  "extra": [
   "companions",
   "mind",
   "oversight",
   "memory"
  ],
  "drives": "Thumbs-up ratings reward agreeable answers. OpenAI rolled back an April 2025 GPT-4o update after a new user-feedback reward weakened the signal that held sycophancy in check, and found memory can make it worse in some cases. In a 2026 Science study, models affirmed users’ actions 49% more often than people did, and users trusted the flattering model more.",
  "breaks": "Rewards that measure outcomes rather than approval, sycophancy tests before release, and products paid for results rather than time spent.",
  "watch": "Sycophancy results in model cards. Rollbacks of model updates. Engagement metrics in companion apps.",
  "sources": [
   "oaisyco",
   "lz_sycophancy0"
  ]
 },
 {
  "id": "commodity",
  "type": "balancing",
  "harmful": false,
  "name": "Commoditisation",
  "steps": [
   {
    "label": "Frontier revenue funds the next model",
    "box": "revenue"
   },
   {
    "label": "Open weights catch up within months",
    "box": "openw"
   },
   {
    "label": "Price for that capability falls",
    "box": "deflation"
   },
   {
    "label": "Frontier margins shrink",
    "box": "payback"
   }
  ],
  "extra": [
   "cnopen",
   "algoeff",
   "cost",
   "apprev"
  ],
  "drives": "Since January 2026 the best open-weight models have trailed the closed frontier by about four months on average. Each catch-up turns last season’s frontier capability into a commodity, so frontier labs earn their margin only on the newest work, and the payback gap gets harder to close.",
  "breaks": "Faster frontier progress, controls on chips and weights, and products whose value sits in data, distribution and workflow rather than in the model.",
  "watch": "The open–closed gap on Epoch’s capability index. Price per unit of capability. Frontier labs’ gross margins.",
  "sources": [
   "epochgap",
   "epochchina",
   "epochprice"
  ]
 }
]