{"resourceId":"sta-national-ai-in-research-survey-compute-training-20261020","versions":[{"version":"external-4f03683b3f1b25d5ecf5a37176de9c9e2801ea44a27d4dc2edb55c505e2a2a48","resource":{"id":"sta-national-ai-in-research-survey-compute-training-20261020","title":"Science & Technology Australia National AI in Research Survey (1,034 responses): about three-quarters of researchers use AI, 54% call institutional training inadequate, and the ~10% who sized compute needs total 53.5M CPU-hours and 14.3M GPU-hours","organization":"Science & Technology Australia (STA), with MetaMelb at the University of Melbourne","sector":"Research enterprise (higher education, public research agencies, medical research institutes)","geography":"Australia (international; methods mirror a UK survey)","publishedAt":"20 October 2026 (STA media release, issued as STA gave evidence to the Joint Select Committee on Artificial Intelligence)","publicationDate":"2026-10-20","eventDate":"2026-10-20","sourceName":"Science & Technology Australia","sourceLabel":"STA media release, \"AI use widespread among researchers but support urgently needed\", 20 October 2026","sourceUrl":"https://scienceandtechnologyaustralia.org.au/ai-use-widespread-among-researchers-but-support-urgently-needed/","evidenceClass":"independent-research","outcomeClass":"mixed","topics":["knowledge-work","infrastructure","accessibility-workforce","governance-procurement","data-security"],"finding":"STA released first results from Australia's National AI in Research Survey, run with MetaMelb at the University of Melbourne, which collected 1,034 complete responses across disciplines and career stages. Nearly three-quarters of respondents reported using AI (coding, literature search, specialised scientific applications and generative tools). More than half (54 per cent) considered their organisation's AI training inadequate, and institutional guidance was often described as unclear or poorly coordinated. Researchers generally accepted AI help drafting and editing grant applications but considered using AI to set funding scores or assess researcher or institutional performance unacceptable. Security and privacy concerns were the leading reported barrier to using AI-related computing; fewer than one in ten respondents quantified 12-month compute needs, but those estimates totalled about 53.5 million CPU-hours and 14.3 million GPU-hours. STA notes 2026 national allocation applications sought more than 2.2 billion hours, almost three times public capacity, and says commercial capacity should complement, not replace, public investment (NCRIS, NCI, Pawsey).","sledRelevance":"Gives research offices and funders a large, recent, cross-disciplinary baseline on researcher AI use, training gaps, acceptable-use boundaries in grant review and compute barriers. U.S. institutions designing AI training, research-computing allocation, and policies on AI in grant review or performance assessment can use it as a comparator, with jurisdictional limits.","evidence":"Opened and read the STA media release (20 Oct 2026) in full and the linked STA opening statement to the Joint Select Committee on AI (20 Oct 2026). Recorded the 1,034 responses, ~three-quarters using AI, 54% training inadequate (release), security/privacy as leading compute barrier, <10% quantifying compute, 53.5M CPU-hours and 14.3M GPU-hours, and the 2.2 billion requested hours. The opening statement adds that 80% of responses were from higher education, 70% reported moderate-to-extensive generative-AI experience, and the survey closed 25 September; it gives 45% (not 54%) for inadequate training. The linked initial-analysis PDF could not be retrieved (TLS error from the box).","architectureImplications":"Compute, secure data, storage and specialist workforce are framed as one planning unit; security and privacy are the main barrier to AI computing, pointing to secure research environments rather than raw capacity alone.","governanceImplications":"Respondents draw a clear line: AI may assist drafting grant applications but should not determine funding scores or performance assessments. Institutions and funders can encode that boundary in policy.","securityPrivacyImplications":"Security and privacy concerns are the top reported barrier to AI computing use; researchers want support to protect sensitive information.","caveats":"Initial analysis released by an advocacy peak body alongside parliamentary testimony; self-selected survey, no response rate or weighting reported in the release. The two STA documents differ on the inadequate-training share (54% in the release, 45% in the opening statement); the underlying PDF was not opened. Compute estimates come from fewer than 10% of respondents and STA says they are not a national forecast.","streamIds":["research"],"roles":{"sales":"Lead with readiness gaps rather than adoption. STA's survey of 1,034 Australian researchers found nearly three-quarters use AI, but 54 per cent call institutional training inadequate and security and privacy top the barriers to AI computing. Research VPs, provosts and research-computing leaders can use this to frame a support programme. A bounded engagement benchmarks local use, training and compute barriers against this survey. Interpretation: this is a self-selected initial analysis from an advocacy body, so it cannot support claims about productivity gains.","engineering":"Position the fit around secure research environments. Respondents rank security and privacy as the leading barrier to AI computing, and the few who sized needs estimated 53.5 million CPU-hours and 14.3 million GPU-hours. Pre-sales should check whether the institution offers a governed environment for sensitive data with GPU access, and how requests are sized. Interpretation: the compute estimates come from under 10 per cent of respondents and are explicitly not a forecast, so local telemetry must drive sizing.","delivery":"Treat delivery as a combined training, guidance and secure-compute programme. Work includes a local researcher survey modelled on this one, role-based AI training, clear guidance on acceptable use including a bar on AI-set funding or performance scores, and an intake path to secure GPU environments. The research office owns policy with research computing and the library. Interpretation: acceptance should track training reach and secure-environment uptake, not claimed productivity."},"roleDetails":{"sales":{"customerProblem":"Researchers already use AI widely but lack adequate training, clear guidance and secure compute, creating integrity and data risks.","stakeholders":["Deputy vice-chancellor or vice president for research","Research computing director","Research integrity office","Library and research-skills team","Grants office"],"discoveryQuestions":["Do you have local data on researcher AI use and training gaps?","Where can researchers run AI on sensitive data today?","Does policy address AI in grant scoring or performance review?"],"valueHypothesis":"A local baseline and targeted training plus secure compute access could reduce unsafe workarounds and clarify acceptable use.","potentialEngagement":"A six-week researcher AI readiness baseline (survey plus interviews) with recommendations on training, guidance and secure compute.","unsupportedClaims":["AI raises research productivity by a measured amount","Australian results transfer directly to U.S. campuses","Compute estimates represent total demand"]},"engineering":{"fit":"Fits institutions planning secure research environments with GPU access and needing evidence on barriers.","architectureIntegration":"Integrate a governed secure research environment with GPU partitions, identity controls and data-classification-aware access.","prerequisites":["Data-classification policy","GPU capacity or burst arrangement","Researcher intake and sizing process"],"constraints":["Self-reported, small compute sample","Sensitive-data rules vary by jurisdiction and funder","Initial analysis only; full report not yet reviewed"],"security":"Security and privacy are the top reported barrier; design for controlled data ingress/egress, logging and approved-model lists.","proofOfValue":["Measure researcher uptake of the secure environment over a term","Compare sized requests with actual usage","Survey whether training changes self-reported confidence and safe practice"]},"delivery":{"work":"Run a local baseline survey, launch role-based AI training, publish acceptable-use guidance and open a secure GPU intake path.","dependencies":["Research office sponsorship","Secure environment availability","Training content and staff"],"ownership":"Research office (policy) with research computing (platform) and library (training).","skillsAdoption":"Majority-scale training gap means programmes should cover tool evaluation, output verification and data protection, as STA describes.","governanceCheckpoints":["Research integrity review of acceptable-use guidance","Data-protection sign-off for the secure environment","Policy decision barring AI-determined funding or performance scores"],"acceptanceCriteria":["Proposed: agreed share of active researchers complete core AI training within a year","Proposed: secure-environment onboarding time under an agreed target","Proposed: guidance published and referenced in grant processes"],"risks":["Survey self-selection bias","Training that lags tool change","Conflicting figures across source documents"]}},"retrievedAt":"2026-10-23T05:26:14-05:00","enrichedAt":"2026-10-23T05:32:52-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"A majority-scale training gap and unclear guidance suggest institutions need structured AI skills programmes and expert support staff, not just tool access.","procurementImplications":"STA positions commercial and hyperscaler capacity as a complement to sustained public research infrastructure, and expects firms operating locally to contribute to R&D capability.","operatingModelImplications":"Pairs research-computing planning with training, secure data services and research-integrity guidance under one roadmap (the 2026 National Research Infrastructure Roadmap).","sourceVerification":{"openedUrl":"https://scienceandtechnologyaustralia.org.au/ai-use-widespread-among-researchers-but-support-urgently-needed/","referenceExcerpt":"However, their estimates totalled approximately 53.5 million CPU-hours and 14.3 million GPU-hours.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}