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A Closer Look: Instability Risks from Programmable AI Load Ramping in Low‑Inertia Grids by Prof. Aoife Foley and Dr. Dlzar Al Kez

The global surge in AI development is transforming not only how we compute, but how our power systems behave. In their 2025 study, Instability Risks from Programmable AI Load Ramping in Low‑Inertia Grids, Dr Dlzar Al Kez and Professor Aoife Foley reveal a challenge hiding in plain sight: AI data centres are no longer passive electricity consumers. They are fast acting, logic‑driven electrical loads capable of reshaping grid dynamics in real time.
Drawing on detailed time‑domain simulations of a modified New England test system, the authors show how rapid AI training ramps especially in grids dominated by inverter‑based resources can trigger voltage depressions, RoCoF spikes, and system‑wide instability even without a generator fault. Their work exposes a new class of demand‑driven disturbance and argues that without visibility, coordination, and storage, AI loads could become a critical stressor on tomorrow’s low inertia grids.
This “Closer Look” unpacks the paper’s insights, the authors’ motivations, and the implications for utilities, policymakers, and the rapidly expanding AI sector.
Q: What motivated you to study AI data centers as a potential risk to power grids?
A: What motivated this work was a clear gap between how we model demand and how it behaves today. Traditionally, loads have been treated as something passive, slow-moving, and largely predictable.
But what we’re seeing with AI data centers is very different. These are large, power-electronic loads driven by software, not by system conditions. They can ramp rapidly, and at large scale those ramps can reach system-relevant magnitudes, often without sufficient visibility for system operator.
Within the Avantern Group at The University of Manchester, Professor Foley and my broader work on resilient net-zero infrastructure and low-inertia power systems made it increasingly clear that this emerging form of demand could not be treated using legacy assumptions.
At the same time, system inertia is decreasing as solar and wind replace conventional generators. That combination, faster, less predictable demand and lower system resilience, is what really raised the concern. It’s not just a scaling issue; it’s a change in demand itself. This builds on my continuing research on data centers, fast frequency response, and high-IBR power systems.
Q: In simple terms, what is the main takeaway about how AI workloads could affect grid stability?
A: The main takeaway is that AI workloads can behave more like disturbances than traditional demand.
Instead of slowly varying, they can change rapidly and autonomously, which can trigger frequency and voltage deviations even when no conventional fault or generator trip has occurred.
In low-inertia systems, those fast changes become harder to absorb, meaning instability can emerge purely from how demand behaves, with no generator fault required. That’s a new kind of risk, and one that current grid codes and protection systems weren’t designed with in mind.
Q: Why is battery storage important in your scenarios, and what does it mean when you say “coordinated” storage across sites?
A: Battery storage helps by absorbing or smoothing those rapid changes in demand.
In our scenarios, uncoordinated storage, where each data center acts independently based only on its own conditions, only partially mitigates the problem. But when sites share timing signals or respond to a common grid frequency threshold, the combined effect is much more stabilizing. That alignment is what we mean by coordination: not just having storage, but having it act as part of a coherent system response.
So it’s not just about having storage, it’s about how that storage behaves as part of a wider system response rather than acting independently.
Q: How feasible is it today for data centers to share timing or load information with grid operators, and what are the practical challenges?
A:Technically, it’s feasible. The challenge isn’t really the technology; it’s the framework around it.
A lot of these facilities operate behind the meter, and their internal processes are driven by commercial and operational priorities. There’s often limited obligation to share real-time information about how workloads behave.
So the issue is less about capability and more about incentives, standards, and regulatory structures. Until there’s a clear framework requiring or enabling that visibility, sharing will remain voluntary and inconsistent, which is precisely where policy has a role to play.
Q: If a regulator asked for practical steps, what would be the easiest first actions to reduce the risk you identified?
A: The first step is improving visibility. If operators can’t see how demand is behaving, they can’t plan or respond effectively. Even basic transparency requirements around ramp rates and operational patterns, potentially through extensions to existing grid codes, would be a significant step forward.
From there, introducing soft limits on ramp rates, or requiring facilities above a certain size to demonstrate local mitigation capability such as storage or demand controllability, would bring large AI loads closer into alignment with system needs. These don’t require new institutions; they require existing ones to extend their scope.
Q: What are the main limitations or uncertainties in your study that readers should keep in mind?
A: Like any modeling study, this is based on representative scenarios rather than exact real-world behavior.
At the Avantern Group we’ve tried to capture realistic dynamics based on available data and reported events, particularly in how we represent fast-ramping load profiles and system conditions, but actual data center behavior can vary depending on design, workload type, and control strategies, and granular operational data from live facilities remains difficult to access, which itself is a limitation worth noting.
The results should therefore be seen as indicative of risk and system sensitivity, rather than precise predictions of specific events.
Q: How generalizable are your findings to other grids, regions, or configurations beyond the specific case you analyzed?
A: The specific numerical results will vary by system, but the underlying mechanisms are broadly applicable. Any grid with growing inverter-based generation and large, fast-ramping loads will face similar pressures, and that increasingly describes systems across GB, Ireland, parts of Europe, and data center-dense regions globally.
The severity depends on system strength, inertia levels, and whether these loads are geographically concentrated, as clustering amplifies their local impact on the grid.
So while our case study is specific, the dynamics it captures are not.
Q: What roles should different stakeholders (data centers, utilities, policymakers) play in addressing these risks?
A: Data centers need to recognize that at scale, they are no longer just consumers, they are active participants in system behavior. That comes with a responsibility for transparency and some level of coordination with system operators.
Utilities and system operators need updated tools to model and monitor these new demand profiles in real time; current frameworks weren’t built with this kind of load in mind.
And policymakers need to update connection standards and market structures, so these risks are properly accounted for, rather than sitting outside the regulatory perimeter. The message is not that AI data centers cannot be connected, but that they need to be treated as active infrastructure, not ordinary passive demand.
Q: What future research directions do you see as most valuable to pursue next?
A: The most immediate gap is real-world load data. High-resolution behavioral data from operating facilities would significantly improve how we model and anticipate these risks, and right now that data is largely inaccessible. This is a core research and innovation direction for the AVANTERN Group: modeling emerging infrastructure risks before they become operational failures.
Beyond that, a key direction is developing coordination mechanisms that are practical enough to implement, not just theoretically optimal. How do you design something that works within commercial constraints, varying ownership structures, and existing market rules?
More broadly, both questions point to the same underlying need: rethinking how demand is represented in planning and operational models. That’s a research agenda that extends well beyond AI data centers.
Q: If you had to summarize the study for a broad audience in one or two sentences, what would you say?
A: Large AI data centers are changing how electricity demand behaves, making it faster, less predictable, and more dynamic and system active. If we continue to treat that demand as passive, we risk missing a growing source of instability in modern power systems. Our study shows that, unless these fast-ramping loads are made visible, coordinated, and supported by storage or grid-forming capability, they could become a new source of instability in low-carbon power systems.
Q: What do you think SSRN contributes to the world of modern research and scholarship?
A: For research like ours, at the Avantern Group, where the policy implications are immediate and the field is moving quickly, SSRN’s value is in closing the gap between when work is done and when it reaches the people who need it. Peer review is important, but it takes time, and decisions about grid codes, data center connections, and energy policy aren’t waiting.
More broadly, SSRN encourages the kind of cross-disciplinary conversation this problem genuinely requires. It sits at the intersection of power systems, computer science, and policy, and having a platform where researchers across those fields can engage with the work early makes a real difference.
ABOUT THE AUTHORS
Professor Aoife Foley
Professor Aoife Foley is Chair in Net Zero Infrastructure at The University of Manchester, with a joint appointment to the Departments of Electrical and Electronic Engineering and Civil and Engineering Management, and is Chief Executive Officer (CEO) of the AVANTERN Group. Her work focuses on the resilience, operation, financing, and future planning of net-zero infrastructure systems across the energy, transport, telecommunications, and digital sectors, examining how emerging technologies, AI-driven demand, renewable integration, and increasingly complex infrastructure networks interact within rapidly decarbonising societies.
With more than 30 years of experience spanning engineering, infrastructure delivery, policy, and academia, Professor Foley’s work consistently bridges engineering, policy, and real-world system operation. Before moving into academia full-time in 2011, she worked across major infrastructure, telecommunications, and energy projects in both public and private sectors. She later became Professor in Energy Systems Engineering at Queen’s University Belfast and previously served as Editor-in-Chief of Renewable and Sustainable Energy Reviews (Elsevier). Ranked among the Stanford/Elsevier Top 2% of Scientists globally, her work continues to influence international discussions on resilient infrastructure, future electricity systems, and the wider challenges associated with net-zero energy transitions.
You can see more work by Aoife Foley on her SSRN Author page here.
Dr Dlzar Al Kez
Dr Dlzar Al Kez, CEng, MIET, FHEA, is a Research Associate in Net-Zero Infrastructure at The University of Manchester and Chief Technology Officer (CTO) of the AVANTERN Group. His research focuses on the stability and resilience of low-carbon electricity systems with high penetrations of inverter-based resources, battery energy storage, and rapidly changing electrical demand. He works across power system modelling, IBR integration, low-inertia grid behaviour, frequency stability, grid-code compliance, and the operational impacts of emerging digital infrastructure such as AI data centres. Using DIgSILENT PowerFactory, Python, MATLAB, and system-level modelling approaches, he supports technical assessment of grid connection risks, dynamic performance, system strength, and stability challenges in renewable-dominated networks. Dr Al Kez has authored more than 50 peer-reviewed publications and serves as Associate Editor for IET Smart Grid and Smart Grids and Sustainable Energy, alongside roles as Publishing Ethics Advisor and Subject Matter Expert for Elsevier. His work bridges academic research, industry-facing power system studies, and advisory support for utilities, infrastructure developers, regulators, and organisations managing complex grid integration challenges.
You can see more work by Dr Dlzar Al Kez his SSRN Author page here.
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The Latest Research on Climate Finance

This list includes a selection of the latest research on Climate Finace posted to SSRN in 2026.
- Detailed land cover transitions for better decisions: EO as support for sustainability-linked financing by Ignacio Borlaf-Mena (TU Wien), Angel Fernandez-Carrillo (GMV), Macarena Mérida-Floriano (GMV), Carlos Domenech (GMV), Dieter Wang (World Bank), Veerle de Smit (World Bank) and Clément Albergel (European Space Agency Climate Office)
- Environmental-Economic Accounting, Extinction Risk, and Financing: A SEEA EA Application for Forest Ecosystems in Hawaiʻi by Louis Chua (University of Hawaii at Manoa), Kawika Winter (University of Hawaii at Manoa) and Kirsten Oleson (University of Hawaii Manoa)
- Return to Nature: Biodiversity Loss and Global Stock Returns by Marloes Hagens (Erasmus University Rotterdam (EUR)) and Mathijs A. van Dijk (Erasmus University Rotterdam (EUR))
- A soft-coupling approach to transition risks with a Stock-Flow Consistent Model – Introducing FASMID by Louis Daumas (RFF-CMCC European Institute on Economics and the Environment; Polytechnic University of Milan)
- Climate Regulatory Exposure and the Stock Market: Evidence from the Trump Elections by Salim Baz (Lebanese American University), Lara Cathcart (Imperial College Business School), Alexander Michaelides (Imperial College Business School; Centre for Economic Policy Research (CEPR)), Liying Wang (University of Liverpool) and Yi Zhang (Hong Kong University of Science and Technology (Guangzhou); Imperial College Business School)
- A Term Structure Framework for Green Bond Spreads and Portfolio Strategies by Mohammad Hadi Sehatpour (Finance Department, UTS Business School), Marta Campi (University of Zurich) Christina Sklibosios Nikitopoulos (University of Technology Sydney – Business School; Financial Research Network (FIRN)), Gareth Peters (University of California Santa Barbara; University of California, Santa Barbara) and Kylie-Anne Richards (University of New South Wales (UNSW) – School of Mathematics and Statistics; University of Technology Sydney (UTS) – UTS Business School)
- Mandatory Sustainability (ISSB) Reporting: Early Evidence from Türkiye by Amir Amel-Zadeh (University of Oxford – Said Business School), Thomas Bourveau (University of Oxford, Saïd Business School), Furkan M. Cetin (London School of Economics & Political Science (LSE)) and Jeroen Koenraadt (London School of Economics & Political Science (LSE))
- What Firms Actually Lose (and Gain) from Extreme Weather Event Impacts by Tobias Schimanski (University of Zurich), Glen Gostlow (University of Zurich – Department Finance), Malte Toetzke (Technical University Munich; Max Planck Institute for Innovation and Competition) and Markus Leippold (University of Zurich; Swiss Finance Institute)
- What Is the Carbon Premium a Premium On? * by Marcin T. Kacperczyk (Imperial College London – Accounting, Finance, and Macroeconomics; Centre for Economic Policy Research (CEPR); European Corporate Governance Institute (ECGI))
- Powering AI: How Do Data Centers Affect Renewable Energy Investment? by By Alexander Heiss (Technische Universität München (TUM) – TUM School of Management0, Zacharias Sautner (University of Zurich – Department of Finance; Swiss Finance Institute; European Corporate Governance Institute (ECGI)) and Thomas Schmid (The University of Hong Kong – Faculty of Business and Economics)
To read more research on Climate Finance, subscribe to SSRN’s Climate Finance alerts or view other papers here.
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The Latest Research on Central Banks

This list includes a selection of the latest research on Central Banks posted to SSRN in 2026.
- The Global State of Open Banking and Open Finance Report by Sanya Juneja (University of Cambridge – Cambridge Centre for Alternative Finance), Bill Roberts (University of Cambridge – Cambridge Centre for Alternative Finance), Dana Salman (University of Cambridge – Cambridge Centre for Alternative Finance), Pavle Avramovic (University of Cambridge – Cambridge Centre for Alternative Finance), Alan Ainsworth (Cambridge Centre for Alternative Finance (CCAF)) and Bryan Zheng Zhang Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge; University of Cambridge – Cambridge Centre for Alternative Finance)
- The Moneyness of Stablecoins by Christopher K. Odinet (Texas A&M University School of Law), Andrea Tosato (Southern Methodist University – Dedman School of Law) and Yesha Yadav (Vanderbilt University – Law School; European Corporate Governance Institute (ECGI))
- Crypto-Asset and Stablecoin Regulation: A Comparative Analysis of MiCAR, the United States GENIUS Act, and the UK Regulatory Framework by Filippo Annunziata (Bocconi University – Department of Law)
- The Paradox of Gold Reserves: How Foreign Central Banks’ Gold Holdings Create Indirect Exposure to U.S. Treasuries by Joao Paulo Mayall (QR Capital)
- Bank Governance: Lessons Still Not Learned by Marco Becht (Solvay Brussels School of Economics and Management (ULB); European Corporate Governance Institute (ECGI); Centre for Economic Policy Research (CEPR)), Patrick Bolton (Imperial College London; Centre for Economic Policy Research (CEPR); National Bureau of Economic Research (NBER); European Corporate Governance Institute (ECGI)) and Ailsa Roell (Imperial College Business School; European Corporate Governance Institute (ECGI))
- Climate Change, Bank Liquidity and Systemic Risk by Margherita Giuzio (European Central Bank (ECB)), Bige Kahraman (University of Oxford – Said Business School; Centre for Economic Policy Research (CEPR)) and Jasper Knyphausen (University of Oxford – Said Business School)
- The Contest Between Central Bank Digital Currencies, Stablecoins and Tokenised Deposits: Which Will Likely Win, and Why? by Ross P. Buckley (University of New South Wales (UNSW) – UNSW Law & Justice)
- Beyond Words: Predicting Market Volatility from Multimodal Central Bank Communication by Tiancheng Wang (Stanford University – Hoover Institution), Brandon Yee (Yee Collins Research Group), Tanazzah Rehman (Georgia Institute of Technology – College of Computing) and Eric Lee (Yee Collins Research Group)
- Bank Runs With and Without Bank Failure by Sergio Correia (Board of Governors of the Federal Reserve System), Stephan Luck (Federal Reserve Bank of New York) and Emil Verner (Massachusetts Institute of Technology (MIT) – Sloan School of Management; National Bureau of Economic Research (NBER)
- How Do Central Bank Governor Turnovers Affect Uncertainty and Lending Globally? By Kristle Romero Cortés (UNSW Australia Business School, School of Banking and Finance) and Mandeep Singh (The University of Sydney – Discipline of Finance)
Discover more research on Central Banks in SSRN’s Banking & Insurance alerts here.
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Top Papers on AI in Law Q1 2026

This list includes the top downloaded papers on AI in Law posted in Q1 2026.
- Silicon Formalism: Rules, Standards, and Judge AI by Eric A. Posner (University of Chicago – Law School) and Shivam Saran (University of Chicago – Law School)
- The Artificial in “Artificial Intelligence”: How Imagination Shapes AI Regulation by Claudio Novelli (Yale University – Digital Ethics Center), Luciano Floridi (Yale University – Digital Ethics Center; University of Bologna- Department of Legal Studies), Stefan Larsson (Lund University – Department of Technology and Society) Mariarosaria Taddeo (University of Oxford – Oxford Internet Institute) and Steven L. Winter (Wayne State University Law School)
- Code Is Not Law by Carla Reyes (Southern Methodist University – Dedman School of Law), Andrea Tosato (Southern Methodist University – Dedman School of Law) and Andrew Hinkes (New York University School of Law)
- Power Steering, Not a Brake: How Boards Should Actually Govern AI by Henk de Jong (IESE Business School), Robert Maciejko (Board AI Institute; INSEAD AI (alum-led) ), Sampsa Samila (University of Navarra, IESE Business School) and Christoph Wollersheim (Egon Zehnder)
- Legal Alignment for Safe and Ethical AI by Noam Kolt (Hebrew University of Jerusalem), Nicholas Caputo (Oxford Martin School), Jack Boeglin (University of Pennsylvania Law School), Cullen O’Keefe (Institute for Law & AI; Centre for the Governance of AI), Rishi Bommasani (Stanford University), Stephen Casper (Massachusetts Institute of Technology (MIT) ), Mariano-Florentino Cuéllar ( Carnegie Endowment for International Peace; Stanford Law School), Noah Feldman (Harvard Law School), Iason Gabriel (School of Advanced Study University of London), Gillian K. Hadfield (University of Toronto; Vector Institute for Artificial Intelligence; OpenAI; Center for Human-Compatible AI), Lewis Hammond (University of Oxford), Peter Henderson (Princeton University – Program in Law & Public Policy), Atoosa Kasirzadeh (Carnegie Mellon University), Seth Lazar (Australian National University (ANU)), Anka Reuel (Stanford University), Kevin Wei (RAND Corporation; Harvard University – Harvard Law School), Jonathan L. Zittrain (Harvard Law School; Harvard School of Engineering and Applied Sciences; Harvard University – Harvard Kennedy School (HKS); Harvard University – Berkman Klein Center for Internet & Society)
- Buying Blind: Corruption Risk and the Erosion of Oversight in Federal AI by Jessica Tillipman (George Washington University – Law School)
- Questioning the Digital Markets Act’s Legality by Thibault Schrepel (Vrije Universiteit Amsterdam; Stanford University’s Codex Center) and Godefroy de Boiscuille (University of Côte d’Azur; Paris-Panthéon-Assas University & CRED)
- Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models by Xinyue Liu (Stony Brook University), Niloofar Mireshghallah (Carnegie Mellon University), Jane C. Ginsburg (Columbia University – Law School) and Tuhin Chakrabarty (Stony Brook University)
- Liberal AI by Cass R. Sunstein (Harvard Law School; Harvard University – Harvard Kennedy School (HKS))
- Ethnonationalism by Algorithm by Spencer Overton (George Washington) University – Law School
To read more about AI in Law, subscribe to SSRN’s Artificial Intelligence – Law, Policy, & Ethics Research Updates or view other papers here.
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The Latest Research on Cryptocurrency

This list includes a selection of the latest research on cryptocurrency posted to SSRN in 2026.
Cryptoasset Ecosystem in Latin America and the Caribbean by Roman Proskalovich (University of Cambridge, Judge Business School, Cambridge Centre for Alternative Finance), Christopher Jack (University of Cambridge – Cambridge Centre for Alternative Finance), Alex Zarifis (University of Cambridge – Cambridge Centre for Alternative Finance), Diego Montes Serralde (University of Cambridge – Cambridge Centre for Alternative Finance) and Damaris Njoki (University of Cambridge, Judge Business School, Cambridge Centre for Alternative Finance)
When Markets Never Sleep: Intraday Liquidity Patterns and Volatility Effects in Cryptocurrency Trading by Aleksander R. Mercik (Wroclaw University of Economics and Business), Barbara Bedowska-Sojka (Poznań University of Economics and Business)
OmniFormer: A Patch Transformer for Joint Long-Term Multi-Dimensional Cryptocurrency Time Series Forecasting by Trung Nam Nguyen (Ho Chi Minh City University of Economics and Finance), Nguyen Quoc Anh (Hitachi Digital Services), Son Ha (RMIT University), Phien N. Nguyen (Ton Duc Thang University), Trung Phan Hoang Tuan (FPT University), Anh N. Le (FPT University) and Nguyen Gia Chan (FPT University)
Code Is Not Law by Carla Reyes (Southern Methodist University – Dedman School of Law) Andrea Tosato, (Southern Methodist University – Dedman School of Law) and Andrew Hinkes (New York University School of Law)
Tokenized Gold by Campbell R. Harvey (Duke University – Fuqua School of Business; National Bureau of Economic Research (NBER)), Chen Lin (The University of Hong Kong – Faculty of Business and Economics) Daniel Rabetti (National University of Singapore (NUS); Harvard Business School) and Che Zhang (Tsinghua University)
The Moneyness of Stablecoins by Christopher K. Odinet (Texas A&M University School of Law), Andrea Tosato (Southern Methodist University – Dedman School of Law) and Yesha Yadav (Vanderbilt University – Law School; European Corporate Governance Institute (ECGI) )
Pairs Trading in Crypto by Sasha Stoikov (Cornell Financial Engineering Manhattan), Dora Xu (Cornell University – Cornell Financial Engineering Manhattan), Shijie Shao (Cornell University), Yourui Wang (Cornell University) Tongshu Zhang (Cornell University) and Jinxuan Hu (Cornell University)
Stablecoins and Banking: Deposit Dynamics, Financial Stability, and Regulatory Design by Lin William Cong (Nanyang Technological University; Cornell University)
Regulating Decentralized Stablecoins: Comparing MiCAR and the GENIUS Act by Christopher K. Odinet (Texas A&M University School of Law) and Andrea Tosato (Southern Methodist University – Dedman School of Law)
The Contest Between Central Bank Digital Currencies, Stablecoins and Tokenised Deposits: Which Will Likely Win, and Why? by Ross P. Buckley (University of New South Wales (UNSW) – UNSW Law & Justice)
Discover more research on cryptocurrency in SSRN’s Cryptocurrency Research Updates here.
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SSRN Strategic Update: Renewed Focus on Core Research Sharing Mission

At SSRN, our mission is to rapidly share preprints and other early-stage research, empowering global scholars to help shape a better future. Today we are announcing an important change that reflects where we believe we can make the greatest contribution to that mission.
We have decided to focus entirely on SSRN’s core function as a free, world-class preprint platform. As a result, we will be closing our commercial products (Research Paper Series, Sponsored Networks and Site Subscriptions, paid Conference Proceedings, Data Analytics Dashboards, Partners in Publishing, Jobs and Announcements, and Data Feeds) by the end of December 2026.
This is not a decision we have taken lightly. These products have supported thousands of researchers and institutions over many years, and we are extremely grateful to all our institutional partners. However, running a commercial operation alongside a free research platform has required very tough trade-offs in technology investment, operational focus, and our ability to keep pace with what researchers actually need from a preprint server in 2026. Stepping back from commercial products will allow us to us put everything into what SSRN does best.
We hope that in the future this will mean free Research Alert subscriptions, faster posting times, improved CrossRef metadata, stronger transparency through versioning, ORCID integration, and clear links between preprints and published versions. SSRN will remain publisher-neutral and committed to serving researchers across all disciplines.
What this means for existing customers
Every existing contract will be honored in full through its term or until December 31, 2026, whichever comes first. Our team will be in touch with each customer directly to talk through the timeline, answer questions, and plan the transition, including any applicable refunds. We will not be onboarding new customers for these commercial products, and automatic renewals will not proceed.
If you have questions about your contract, data, or transition planning in the meantime, please reach us at ideas@ssrn.com.
Looking ahead
SSRN has been part of the research ecosystem for over 25 years. This change is about making sure it will survive and thrive for the next 25. We’d like to thank all our commercial partners for their support for SSRN over the last two decades: we really wouldn’t be here without you. However, we now look forward to building an SSRN that is completely focused on the needs of researchers around the world. As always, we’d love to hear your thoughts at ideas@ssrn.com.

FAQ:
Why are SSRN’s commercial services being discontinued, and why now?
The preprint landscape has changed significantly. Expectations around posting speed, licensing, metadata transparency, and discoverability have all increased, and SSRN has had to make difficult trade-offs to maintain commercial products alongside its free platform. Stepping away from our commercial products will allow us to simplify our model and prioritize investing properly in the things that researchers tell us matter the most to them. The timing reflects both the strategic direction set by our parent organization and a genuine belief that now is the right moment to make this shift.
When will the process be complete?
We will complete the transition by the end of December 2026.
Will SSRN continue to operate after the transition?
Yes. SSRN will continue as a free, world-class preprint platform. Sunsetting our commercial products is about sharpening our focus, not shrinking our ambition. We intend to strengthen SSRN’s platform with faster posting, better licensing options, improved discoverability, and higher research integrity standards. We hope that many of our commercial customers will transition and make full use of our ongoing free services.
Which services are being discontinued?
Research Paper Series (RPS), Sponsored Networks and Site Subscriptions, paid Conference Proceedings, Data Analytics Dashboards, Partners in Publishing, Jobs and Announcements, and Data Feeds will all close by the end of December 2026. All existing content will remain permanently archived and accessible.
What happens to my existing contract?
All existing contracts will be honored through their term or until December 31, 2026, whichever comes first. A SSRN manager will be in touch to confirm the details for your specific arrangement and to work through the transition with you.
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The Latest Research on Medical Law

This list includes a selection of the latest research on medical law posted to SSRN in 2025-2026.
- The Neuroscience of Brain Injury in Criminal Cases: An International Scope by Deborah W. Denno (Fordham University)
- Expand Right to Die Options for Older Americans: Eleven Ways to Avoid Late-Stage Dementia by Thaddeus Mason Pope (Mitchell Hamline School of Law)
- Data Distortions by David A. Simon (Northeastern University)
- Putting the L in ELSI: Legal Methods for Bioethics Research by Anya Prince (University of Iowa), Benjamin Berkman (National Institutes of Health), Donald L. Ford (University of Iowa), Dov Fox (University of San Diego), Christi Guerrini (Mid Sweden University), Amy Koopmann (University of Iowa), Natalie Ram (University of Maryland), Jessica L. Roberts (Emory University), Kayte Spector-Bagdady (University of Michigan at Ann Arbor), & Sonia M. Suter (George Washington University)
- IVF as a ‘Hope Technology’ by Emily Jackson (London School of Economics)
- A Non-Person Theory of the Fetus by Greer Donley (University of Pittsburgh)
- Patient Records to Client Files: How the Legal Profession’s Confidentiality Standards Can Inform Healthcare Corporations’ Approach to Artificial Intelligence to Minimize HIPAA Violations by Parker Brown (University of Mississippi)
- Unwanted Pregnancy: Sex, Contraception, and the Limits of Consent by Deborah Tuerkheimer (Northwestern University)
- Putting an End to Protective Privilege: Georgia Should Recognize the Psychotherapist’s Duty to Warn by Jan M. Levine (Duquesne University)
- The Weight of Stigma by Rebekah A. King (Saint Louis University) & Michael S. Sinha (Saint Louis University)
- Health Care Financialization by Erin C. Fuse Brown (Brown University) & Hayden Rooke-Ley (Brown University)
- Abortion Shield Laws in Action by David S. Cohen (Drexel University), Greer Donley (University of Pittsburgh), & Rachel Rebouché (University of Texas at Austin)
- (Un)Common Knowledge & Experience by Jasmine Harris (University of Pennsylvania)
- Euthanasia as Medical Therapy in Canada by Trudo Lemmens (University of Toronto)
- It’s Magic?: Ozempic, Addiction Treatment, and the Law by Andy Grewal (University of Iowa)
Discover more research on medical law, subscribe to SSRN’s Medical-Legal Studies eJournal here or or view other papers here.
- The Neuroscience of Brain Injury in Criminal Cases: An International Scope by Deborah W. Denno (Fordham University)
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SSRN’s New Advanced Search Makes Finding Papers Easier and Faster

Michael Parsons
SSRN’s search has been sorely in need of some love for a while, so we’re very happy to share that we’ve recently been able to make some improvements to how it works. Previously, if you wanted papers about corporate governance but not banking, you couldn’t say so. If your search terms were slightly off, you may have struggled to find what you’re for. We’ve now added two new search modes to the Advanced Search page: Fuzzy Search and Boolean Search. They solve different problems, and you can choose between them depending on what you need.
Fuzzy Search: A Broader Net
Fuzzy Search is more forgiving than a traditional keyword search. It will tolerate slight variations in your search terms, typos, and near-matches, returning results that a strict search might miss. You’ll typically see a larger set of results, which makes it useful for exploratory searches, when you’re not yet sure of the precise terminology a field uses.
It works across all three search field options (Title Only, Title Abstract & Keywords, and Title Abstract Keywords & Full Text) and applies to the Author(s) field as well. If your query is in the right neighbourhood, fuzzy matching will try to get you there. A word of calibration: fuzzy search broadens your results, but it isn’t a spell-checker. It works best when your terms are close to the target. The further off your query, the noisier the results
Boolean Search: A Precise Tool
Boolean Search lets you build structured queries using standard logical operators:
AND requires both terms to appear.
"corporate governance" AND disclosurereturns only papers addressing both topics.OR broadens to either term.
"machine learning" OR "deep learning"captures papers using either framing.NOT excludes a term.
cryptocurrency NOT bitcoinfinds crypto papers that aren’t specifically about Bitcoin.Parentheses let you group expressions.
(fintech OR regtech) AND regulationfinds regulation papers that mention either fintech or regtech, without running two separate searches.As with Fuzzy Search, Boolean mode works with all three advanced search options. Narrow your scope to Title Only for precise results; expand to Full Text to surface papers where the terms appear anywhere in the document.
Two Modes, One Search
These are separate modes, not features you stack. You choose one via the radio buttons on the Advanced Search page. The right choice depends on where you are in your research.
Use Fuzzy Search when you’re exploring a new area, working from partial memory, or casting around for how a topic is discussed in the literature. It’s the mode for early-stage discovery, when missing a relevant paper is worse than wading through some noise.
Use Boolean Search when you know what you’re after and want to carve out a specific slice of the literature. It’s the mode for systematic reviews, targeted citation searches, and any query where you need to include or exclude particular terms with confidence.
Both modes share the same controls: scope selection, the Author(s) field, date filters, and sort options. The only difference is how the search engine reads your query.
A Two-Step Workflow for Search
You can now think in terms of a two-step workflow for Search on SSRN. Start in Fuzzy Search with a broad query. Scan the first page of results to pick up the vocabulary the literature actually uses. Then switch to Boolean Search and build a precise query with those terms and operators. The two modes complement each other when used in sequence.
In Boolean mode, wrap multi-word phrases in quotes:
"corporate governance"behaves differently from the two words searched separately. And remember that the Author(s) field is independent of the main search box. If you want a specific person’s work on a specific topic, use both fields rather than cramming everything into one query.These changes are live now on papers.ssrn.com, and we really hope you find them useful. Send us your feedback at ideas@ssrn.com, we’d love to hear from you.


