VOLOVLOAutomation risk & transition, task by task
AskOccupationsMajorsBusinessFoundersChangesNotesMethod
Search occupations, majors…
EN
  • English
  • 简体中文
  • 日本語
  • Español
  • Português
  • Français
VLO
VOLO

Understanding how automation changes work — task by task, with the evidence shown and the uncertainty admitted.

AskOccupationsMajorsBusinessFoundersChangesNotesMethodAboutRole diagnosisPrivacyTerms
© 2026 VOLO
Occupations
All occupations
AI / software
Translator / InterpreterBank tellerCopywriterContent moderatorCustomer service representativeAdministrative assistantSoftware tester / QA engineerGraphic designerParalegalVideo editorAccountant / BookkeeperMarketing specialistFrontend developerData analystInsurance claims handlerTechnical writer / documentation engineerJunior software developerHR / recruiterLoan officer / credit officerFinancial analystProcurement / supply chain specialistJournalistSales / account managerReal estate agentIT support specialist / helpdeskAuditorManagement consultantBackend developerAI researcherProduct / UX designerBusiness systems ownerE-commerce operations specialistRadiologistData engineerLawyerMedical assistant / clinic assistantMachine learning engineerExperienced software engineerDevOps / platform / SRE engineerProduct managerPharmacistPartnerships / channel managerSecurity analyst (SOC)Compliance officerArchitectFirst-line manager / team supervisorCounsellor / therapistRetail salesperson / shop assistantSecurity guardSchool teacherGeneral practitioner / primary care doctorAirline pilotWaiter / restaurant serverAuto mechanic / vehicle technicianAir traffic controllerPhysiotherapist / rehabilitation therapistConstruction workerRegistered nurseCare worker / nursing assistantAI implementation lead
RPA / self-service
Government service clerkOperations coordinatorMetro train driverReceptionist / front desk
Robotics
Retail cashier / shop assistantContainer port workerWarehouse workerAssembly line workerMedical laboratory technicianChef / cookCleaner / janitorElectrician
Autonomous driving
Ride-hail / taxi driverTruck driverDelivery rider / courier
Majors
All majorsEnglish / Foreign languagesComputer scienceAccountingPsychologyJournalism / CommunicationFinanceLawVisual communication designMarketingNursingBusiness administrationEducation and teacher trainingArchitecturePublic administrationMedicineHospitality and tourism managementEconomicsInformation systems
Guides
Ask VOLOFor businessFor foundersRecent changesNotesRole diagnosisMethod & evidenceAboutFollow an occupationSearch
You are reading as:I have a jobI am studyingI run a companyI am building something
On this pageWhich technologiesHow it got hereMethod and sources
Occupations›Architect›How we know

Architect — how we know

The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.

Assessed
2026-09-10
With evidence
3/5
Verified events
3

Which technologies matter here#

Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.

Cognitive automation
Renderings and presentation imagesConstruction documentationUnderstanding the brief and finding the ideaApprovals and coordinating the teamSite inspection and construction-phase decisions
Process & self-service
Construction documentation
Physical automation
Site inspection and construction-phase decisions

How it got here#

The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.

Reconstructed · platform inferenceEstimated today for each past checkpoint — not measured at the time. 18 → 41.
1007550250
US architectural services employment was 203,900 in November 2022 and 204,200 in July 2026, never leaving a 201,100–207,500 band in betweenHonolulu reported permit decisions on its AI plan-check software averaging 32.5 days against 73, and corrections falling from 23.5 to 7.7 per applicationSingapore's BCA soft-launched CORENET X on 18 Dec 2023: one coordinated BIM model replaces separate agency submissions, ~20 approval stages become 3 gateways; automated geometric checks in development123456789not assessed
2022 H22024 H2Now

—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed

● 3 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.

CAD and BIM set a modest baseline. The one steep stretch is 2023–2024 and it is narrow: rendering. Image generation absorbed a specialist sub-trade almost entirely, while the parts that carry liability — what to build, and what to do when the ground is not what the survey said — did not move.

12022 H218General-purpose text generation reaches the public. Before this point, exposure came from automation that was already deployed — OCR, RPA, machine vision, self-checkout, dispatch algorithms. ChatGPT research preview (2022-11-30) ↗
22023 H122A general model that passes professional exams. First-draft quality crosses the threshold where professional work starts using it. GPT-4 (2023-03-14) ↗
32023 H228Vision input, long context and tool calling. Models can be pointed at documents and connected to systems, which is what moves process work rather than writing work. GPT-4 Turbo:128k 上下文、视觉、工具调用(DevDay) (2023-11-06) ↗
42024 H133The same capability gets much cheaper and faster. Nothing new becomes possible; a lot becomes affordable at volume, which is when deployment decisions change.
52024 H236Reasoning models that work through multi-step problems, and the first models that operate a computer by looking at the screen. The second one is what reaches software-operating jobs. OpenAI o1(推理);同期 Claude 的 computer use 进入公测 (2024-09-12) ↗
62025 H138Agents begin operating real software end to end rather than producing text for a person to paste. This is also when the first public reversals appear — organisations that automated and partly undid it. Claude 3.7 Sonnet 与 Claude Code:混合推理 + 命令行编码代理 (2025-02-24) ↗
72025 H239Long context and tool use become the default rather than a feature. Capability gains continue; the visible constraint shifts from what models can do to liability, procurement and cost. GPT-5(2025-08-07);Claude Opus 4.5(2025-11-24) (2025-08-07) ↗
82026 H140Long-horizon agents land inside specific industry workflows. Adoption becomes sector-specific rather than general. GPT-5.5:「专为实际工作打造」 (2026-04-23) ↗
9Now41The current assessment — this point is the impact index published on the occupation's page, so the curve is anchored to a number the site already stands behind. Worth noting for the flat curves: in the same weeks, a research preview of a shared specification for AI agents to operate physical devices was opened to research labs and manufacturers. That is the first capability class pointed at the physical occupations whose lines here barely move. GPT-6 Astra(2026-09-03);Claude Fable 5.1 / Mythos 5.1(2026-09-01);Model Hardware Standard 研究预览(2026-08-27) (2026-09-03) ↗

A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.

Written about this#

These pieces argue from the same records this page holds, and each of their sections names what it rests on.

  • How many jobs has AI actually taken? Every verified number

Method and sources#

Assessment date
2026-09-10
Basis of the task judgements
3 evidence-backed · 2 platform inference · 0 not enough evidence
Verified events
3

How we assess an occupation →

← Back to ArchitectThe other layer: every task, one by one →