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  • “AI Needs to Be Disarmed to Prevent Domination, Exclusion, and Threats to Humanity,” Warns Pope Leo XIV

    “AI Needs to Be Disarmed to Prevent Domination, Exclusion, and Threats to Humanity,” Warns Pope Leo XIV

    IBL News | New York

    In a stark warning and global alarm, Pope Leo XIV called for action during an explosive speech at the Vatican yesterday.

    Marking a rare break from papal tradition, the Pontifex unveiled his first encyclical, Magnifica Humanitas, at the Vatican, warning that artificial intelligence “needs to be disarmed to prevent domination, exclusion, and threats to humanity.”

    Speaking at the Aula Nuova del Sinodo, the pope urged global cooperation to ensure AI serves peace, justice, and the common good. The landmark address compared the AI revolution to the industrial transformations the Church faced over a century ago.

    The encyclical Magnifica Humanitas (or “Magnificent Humanity”), on the protection of the human person in the age of artificial intelligence, was signed by the Holy Father on May 15, 2026, the 135th anniversary of Pope Leo XIII’s encyclical Rerum Novarum (known in English as “Rights and Duties of Capital and Labor.”)

    Here are some of Leo’s themes in the encyclical:

    AI is fundamentally not human.
    We must avoid the misconception of equating this type of “intelligence” with that of human beings. These systems merely imitate certain functions of human intelligence. In doing so, they often surpass human intelligence in speed and computational capacity, offering tangible benefits across many fields. Yet this power remains entirely tied to data processing. So-called artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships, and do not know from within what love, work, friendship, or responsibility mean.

    Leo describes the field of artificial intelligence as swiftly evolving, and with real promise as a “valuable tool.” But he emphasizes throughout the text that, on a profound level, artificial intelligence is not human, however closely it approximates the human mind and even its soul.

    This view clearly differentiates between machines and humans. It directly counters a view of some A.I. researchers and thinkers, including some in the room who have recently raised questions about whether A.I. systems may actually feel or express human emotion.

    • Humane labor practices and just wages remain essential.
    The various kinds of job insecurity, fragmented career paths, and automation must not be evaluated solely in terms of efficiency, but in relation to the dignity of the worker, the right to sufficient remuneration, and the genuine possibility of participating in society.

    AI has already displaced many entry-level jobs, and while the full scope of its impact is far from clear, the mass automation of both white-collar and blue-collar work is likely to significantly reshape most sectors of the labor market.

    Echoing many of his predecessors, including Pope John Paul II, Leo acknowledges that economic and technological systems may undergo radical upheavals over the course of history, but insists that the essential dignity of the worker — which includes fair wages — must remain at the center of any new order.

    In another section, he condemns “new forms of slavery” connected to the digital economy, including the young people who work for minimal pay in jobs like data labeling and content moderation, and the even younger ones who labor under dangerous conditions extracting the rare earth materials the industry requires: “The bodies of these people are scarred, injured and worn down so that computational flow may continue uninterruptedly.”

    • No technology can take away the dignity of ordinary human beings.
    We are living through a rapid phase of transition, a “change of era,” in which — while some are vying for the future of new technologies and others dedicate themselves to reflecting on the matter — most people are watching and waiting, observing from afar and merely hoping for the best.

    The Vatican invited people from Silicon Valley to the formal introduction of the encyclical on Monday, including, notably, Christopher Olah, a co-founder of Anthropic, who participated in the presentation.

    But the encyclical itself reminds readers that the aspiring history-makers in the room are not the only ones who have worth. Most of the world’s population will simply have to live with the fallout of how those leaders steward this technological revolution. “Magnifica Humanitas” insists that each of those people “observing from afar” matters.

    “The value of persons, however, does not depend on what they achieve or produce,” Leo writes elsewhere in the text. “There are rights that apply to everyone simply by virtue of being human.” The document uses the word “dignity” 100 times.

    • Beware the temptation of erecting a new Tower of Babel.
    With the heart of a shepherd and a father, I ask everyone to abandon the construction of yet another Tower of Babel and to join forces in building up the common good, so that humanity will never lose its beauty, and the world once again will come to recognize the human heart as the place where God desires to dwell.

    The biblical story of the Tower of Babel recurs as a touchstone. The account appears in the Book of Genesis and describes a world in which a unified human population that speaks only one language decides to build a tower “whose top reaches to the heavens” to exert its own power and domination.

    In response, God scatters the people across the earth, in what serves as an origin story for the existence of different languages and cultures.

    Leo uses the Tower of Babel as an illustration of the pitfalls of pursuing uniformity and standardization, and the limits of ambitious undertakings that appear able to compete with the claims of religion. As many aspects of global culture homogenize, and technology becomes a kind of universal language, Leo’s call for humility and diversity stands in contrast. It’s also a reminder that many of the seemingly new ethical and social challenges posed by A.I. have ancient roots.

    • The pope cites research and makes concrete recommendations.
    In recent years, psychological and psychiatric literature has documented with growing insistence how early and unsupervised exposure to digital devices and social media can negatively impact sleep, attention span, control of emotions and relationships, especially during the most vulnerable stages of life, at times with tragic consequences.

    For all its sweeping moral force, “Magnifica Humanitas” is also a practical document, showing how Leo is focused on pastoral care for the church’s hundreds of millions of families. It surveys research on the impact of technology on child development, including how early and unsupervised access to cellphones leaves children vulnerable to addiction, bullying, and sexual exploitation. Other topics include the regulation of data ownership and the use of A.I.-related weapons in war.

    • Human life is beautiful.
    For this reason, humanity — in all its grandeur and woundedness — must never be replaced or surpassed. We can embrace technological progress that alleviates suffering and unlocks new possibilities, provided we do not abandon the very essence of our humanity, namely, the capacity for relationship and love.

    The title of “Magnifica Humanitas” says it all: In the end, Leo is less interested in technology than in humanity. Humans are flawed, vulnerable, and finite, the pope writes. We are increasingly inferior to the technology we have created if we measure only in cold terms of performance. But the pope writes with great affection for humans. The text ends with a wish “that we may bear witness to the grandeur of humanity, in which God has made his dwelling.”

     

  • Google Solidifies Itself as an AI Heavyweight After Overhauling Its Search Experience

    Google Solidifies Itself as an AI Heavyweight After Overhauling Its Search Experience

    IBL News | New York

    Using the Gemini 3.5 Flash model, Google is increasing its search box size and making it more interactive so that people can ask even longer questions, upload photographs and videos into queries, and ask follow-up questions.

    It’s the first overhaul of its iconic search bar in 25 years, since 2001, prompted by the rise of AI.

    In addition to adding a chatbot on the main search page, Google will also offer digital assistants, or agents, to automate searches.

    For example, someone apartment hunting can be notified of a new listing without opening the real estate website Zillow.com.

    Google’s CEO, Sundar Pichai, said at the company’s annual developer conference in Mountain View, Calif., this month, “When people use our AI in search, they use search more.”

    This move shows that Google has solidified its position as an AI heavyweight.

    In addition to its Gemini models, it is producing AI chips and pouring hundreds of billions of dollars into data centers for its cloud computing business.

    Its Gemini app, which can do coding and research, now has 900 million active users — about the same number as ChatGPT.

    Analysts say that these changes are helping Google make more money from advertising. Last year, Google’s ad clicks rose 6 percent, and it charged 7 percent more for each click. The company’s annual profit has more than doubled since 2022 to $132 billion.

    On searches that deliver AI Overviews, people can ask follow-up questions in AI Mode, which Mr. Pichai called “a revelation.”

    Google is also bringing one of AI’s biggest breakthroughs — software coding — to search.

    Google said it was introducing an alternative to the agents powered by Anthropic’s Claude Code and OpenAI’s Codex.

    Called Gemini Spark, the service is embedded in Gmail, Docs, and other Google products, where it can turn meeting notes spread across emails and chats into a single document. It can also read and draft emails.

    Google’s AI-driven shopping cart will also recommend discounts when products go on sale and warn people when they select items that could be incompatible.

    Koray Kavukcuoglu, the chief technology officer at Google DeepMind, the company’s AI lab, said that “plugging Gemini into Google’s products will help the company stay ahead of competitors by providing information about users’ needs.”

     

    • Google Issues “Gemini Omni “, a New Model that Can Create Anything from any Input

  • Anthropic Releases Claude for the Legal Industry, with 20+ New MCP Connectors and 12 Plugins

    Anthropic Releases Claude for the Legal Industry, with 20+ New MCP Connectors and 12 Plugins

    IBL News | New York

    Anthropic announced this month the launch of over 20 new MCP connectors and 12 plugins tailored to specific legal work and practice areas, for paying attorneys, law students, and others in the legal sector.

    These tools link Claude to dedicated legal software to tackle specific workflows. They are intended for handling day-to-day legal document work, including contract review, drafting, redlining, extraction, and comparison. o

    One feature, called “Commercial Counsel”, reviews vendor agreements, while another tool is intended to help with studying for the bar exam.

    This setup connects to third-party software services commonly used in law, such as DocuSign, Thomson Reuters, and even a competing AI legal service like Harvey.

    Each agent template can be installed in Cowork or Claude Code with a click and produces outputs that match institutional drafting standards.

    The plugin and skill ecosystems are open protocols, and early contributors, including Box, Legal Quants, Lawve AI, and Thomson Reuters, have already shipped their own skills, plugins, and style conventions. Any partner can submit connectors and skills through the Directory. ‍

    Anthropic is also competing with OpenAI in other industries, such as financial services and health care.

    Both AI companies are trying to convince potential customers of their offering on security, data privacy, and questions related to IT teams.

     

     

  • Anthropic, OpenAI, and Google Cloud Moving Downward into the Enterprise Implementation Business

    Anthropic, OpenAI, and Google Cloud Moving Downward into the Enterprise Implementation Business

    IBL News | New York

    AI frontier models are seeking to move beyond infrastructure, software licensing business, and API services in an AI-first world.

    They are launching private equity firms backed by institutional capital and are willing to enter the enterprise workflows business by moving closer to implementation, orchestration, and transformation work — an area that has long been dominated by IT outsourcing firms.

    These AI-native companies increasingly want to own more of the enterprise workflow layer, while enterprises themselves want greater control over strategic technology capabilities, specifically execution, governance, and accountability.

    On May 4, Anthropic unveiled a $1.5 billion venture backed by investors, including Blackstone, Goldman Sachs, Hellman & Friedman, and Sequoia Capital.

    On the same day, it was disclosed that OpenAI was raising over $4 billion for its own initiative, “The Development Company,” at a reported valuation of $10 billion.

    Weeks earlier, Google Cloud announced strategic partnerships with Vista Equity Partners and CVC. It is also reportedly exploring arrangements with Blackstone, KKR, and EQT.

    Analysts say this approach increasingly resembles the “forward-deployed engineer” model popularised by Palantir, where software companies move beyond selling technology and embed themselves deeply within enterprise operations.

    The model is based on deploying forward-deployed engineers — a kind of AI deployment engines — to work side by side with portfolio companies of private equity firms, helping them build and optimize AI solutions on top of its models and broader AI stack.

    Karthik Narain, Chief Product and Business Officer at Google Cloud, said that these partnerships would accelerate AI adoption across sectors and drive industry-wide digital transformation. Anthropic is targeting mid-sized companies in sectors like healthcare, manufacturing, financial services, retail, real estate, and infrastructure.

    “Demand for hands-on AI implementation in sectors is significantly outpacing what is available across the industry today,” he said.

    Krishna Rao, chief financial officer at Anthropic, said this month that the company’s partnerships remain central to how Claude reaches large enterprises, and that it continues to “invest deeply” in those relationships.

    Currently, enterprises need engineers to migrate infrastructure, modernize applications, integrate systems, and manage increasingly complex digital estates. At the same time, implementation work, coding, testing, support, maintenance, and orchestration are increasingly becoming automatable.

    Boosting AI adoption among enterprises is a strategic necessity for Anthropic and OpenAI as they seek to bolster revenue growth and justify their sky-high valuations ahead of their highly anticipated initial public offerings, which can come this year.

    For Google, the move is equally crucial to maintain the rapid growth momentum at Google Cloud, which is emerging as a key growth engine for the tech giant’s revenues and profits.

    During the company’s earnings call this month, Alphabet CEO Sundar Pichai said that enterprise AI solutions became the primary growth driver for Google Cloud for the first time in the past quarter.

     

  • At Least 18 Class-Action Lawsuits Against Instructure After Being Hacked

    At Least 18 Class-Action Lawsuits Against Instructure After Being Hacked

    IBL News | New York

    At least 18 federal class-action lawsuits have been filed against Canvas LMS’s parent company, Utah-based Instructure, following last week’s data breach, while the company issued an apology after final exams at numerous universities were halted.

    “Over the past few days, many of you dealt with real disruption. Stress on your teams. Missed moments in the classroom. Questions you couldn’t get answered. You deserved more consistent communication from us, and we didn’t deliver it. I’m sorry for that,” Instructure CEO Steve Daly wrote in a new post.

    The hacking group behind the outage, ShinyHunters, also stole data on potentially tens of millions of students across nearly 9,000 schools.

    The company’s ongoing investigation found that “usernames, email addresses, course names, enrollment information, and messages” were exposed. That’s slightly different from Instructure’s initial findings, which said that “names, email addresses, student ID numbers” had been affected. That said, usernames and email addresses can still expose a student’s full name.

    Daly added: “We’re still validating all findings, but we want to be clear about what we understand was and wasn’t affected.”

    Instructure’s CEO also revealed that hackers exploited a “vulnerability regarding support tickets in our Free for Teacher environment,” a service that enables teachers to use some Canvas services at no cost.

    In response to the hack, Instructure has temporarily shut down the Free-for-Teacher service.

    The great 2026 Canvas-ocalypse by Bryan Alexander

    Instructure Is Risking the Trust That Built Canvas by Phil Hill

  • The AI Agent Conference Unveiled “The Agentic List 2026,” Signifying the Experimentation Is Over

    The AI Agent Conference Unveiled “The Agentic List 2026,” Signifying the Experimentation Is Over

    Mikel Amigot, IBL News | New York

    The AI Agent Conference 2026, curated by Firsthand VC in partnership with NYSE Wired, Bright Data, and theCUBE, drew over a thousand senior executives, AI engineers, and investors to Midtown Manhattan, May 4–5, 2026, for two days, who effectively declared the experimentation phase of agentic AI over.

    The message from the stage and the hallway alike was the same: the conversation had moved from “should we deploy agents?” to “how do we govern, secure, and scale them without getting fired?”

    However, one of the most telling figures of the conference was this: while 79% of organizations report some level of agent adoption, only 11% are running agents in production.

    Another cautionary figure indicated that 40% of projects are at risk of cancellation, and only 6% of organizations qualify as true AI high performers. Closing that gap was the conference’s defining theme.

    Organized around three interconnected tracks — Agentic Enterprises, Agentic Engineering, and Agentic Industries — plus a specialized Web Discovery & Execution track, the conference landed at the exact inflection point where enterprise adoption is crossing from pilot programs into operational commitment.

    Alongside it, The Agentic List 2026 was unveiled: a curated ranking of 120 companies across three themes (Enterprises, Engineering, Industries), selected from over 5,000 nominations across nearly 2,000 screened companies, collectively backed by billions in funding.

    Notable inclusions were:

    Agentic Enterprises: Glean ($765M raised), Perplexity ($976M), Ramp ($2.8B), Apollo ($251M), Clay ($202M), Sierra ($635M), Decagon ($481M)

    Agentic Engineering: Mistral AI ($3.2B), Cohere ($1.5B), n8n ($254M), Cognition ($596M), Augment Code ($252M), CrewAI ($18M), LangChain ($160M), Tavily ($25M, recently acquired by Nebius)

    Agentic Industries: AlphaSense ($1.4B), Hippocratic AI (healthcare), Hebbia (legal), Harvey (legal)

     

    The Numbers That Framed Sessions

    The conference leaned hard on data, and speakers referenced these staggering figures throughout both days:

    • $10.8–12 billion: Projected global agentic AI market size in 2026, growing at a 43.8–46% CAGR toward $139–196 billion by 2034 (Grand View Research, Precedence Research, Allied Market Research)
    • $301 billion: Total global AI spending in 2026, with agentic AI representing 10–15% of enterprise IT budgets (IDC)
    • 40%: Enterprise applications that will embed task-specific AI agents by year’s end — up from under 5% in 2024 (Gartner)
    • 171%: Average ROI from enterprise agentic AI deployments globally; 192% for U.S. enterprises specifically (Deloitte 2026 State of AI in the Enterprise)
    • 5.8x: Average ROI on AI investment within 14 months of production deployment (McKinsey)
    • $4.6 million: Average annual savings per enterprise from AI-driven process automation across 3+ departments (McKinsey / IDC)
    • 88%: Organizations now using AI in at least one function, up from 78% the prior year (McKinsey / Gartner)
    • 100%: Of surveyed enterprises planning to expand agentic AI usage in 2026 (CrewAI survey of 500 C-level executives at $100M+ revenue organizations).”
    • 6%: Organizations that qualify as true AI high performers with more than 5% of EBIT attributable to AI (McKinsey)

     

    Day 1: Vision Meets Infrastructure

    Opening keynotes set the tone with two back-to-back sessions spanning the full arc of the enterprise agent story.

    Ameet Talwalkar, Chief Scientist at Datadog, kicked off the event, presenting how Datadog is rebuilding its observability platform to treat agents as first-class citizens alongside human users and traditional applications.

    Arvind Jain, Founder and CEO of Glean — which surpassed $200 million in ARR at a $7.2 billion valuation after its $150 million Series F — took the stage alongside Sapphire Ventures’ Jai Das, defending that enterprise agents need a unified context layer connecting LLMs to internal business data, and saying that this approach has become the blueprint for how many large organizations when dealing with an agentic deployment.

    “The real bottleneck is not the models themselves but connecting their reasoning power to the context inside your company,” he said.

    Joe Moura, Co-Founder and CEO of CrewAI, said, “Enterprise adoption of agentic AI is accelerating faster than anyone anticipated. Organizations aren’t just experimenting — they’re building, shipping, and scaling agents into production.”

    UiPath’s Raghu Malpani, Chief Product and Technology Officer, stated: “We’re at a pivotal moment where AI, deterministic automation, and orchestration are coming together to reshape how work gets done.”

     

    Day 2: No Protocol War

    During the Agentic Engineering track, a consensus emerged around two interoperability protocols that are rapidly becoming the backbone of enterprise agent infrastructure.

    1. MCP (Model Context Protocol), created by Anthropic in November 2024 and donated to the Linux Foundation’s Agentic AI Foundation, has become the standard interface connecting agents to external tools, databases, and APIs. By early 2026, MCP had crossed 97 million monthly SDK downloads, with adoption from every major AI provider, including OpenAI, Google, Microsoft, and Amazon. Its architecture is client-server via JSON-RPC 2.0.
    2. A2A (Agent2Agent Protocol), launched by Google in April 2025 and now governed by the same Linux Foundation body, handles the other half — how agents communicate with each other across organizational and platform boundaries. Google announced at Cloud Next 2026 that A2A has reached version 1.2 and is running in production at 150+ organizations, including Microsoft, AWS, Salesforce, SAP, and ServiceNow. Its architecture is peer-to-peer via HTTP and Server-Sent Events.

    Multiple panelists emphasized that these are not competing standards. MCP handles vertical integration (agent-to-tool), while A2A handles horizontal coordination (agent-to-agent). The dominant question was how fast developers can implement both.

    Google’s broader moves were discussed. The rebranding of Vertex AI to the Gemini Enterprise Agent Platform, the launch of Workspace Studio as a no-code agent builder, and the introduction of Agentic Data Cloud collectively represent Google’s bid to own the full stack from chip to inbox.

    Salesforce’s Agentforce loomed over many conversations. It has reached $540 million in ARR with 18,500 enterprise customers. Speakers shared that Agentforce already autonomously resolves 70% of customer chats for clients like 1-800Accountant during peak seasons.

     

    Dominant Theme of Security and Governance

    CrewAI’s survey found that security and governance ranked as the #1 priority (34%) when enterprises evaluate agentic AI platforms. Ease of integration came second at 30%, and reliability came third at 24%.

    Enterprises question whether agents can be deployed safely at scale without exposing unacceptable risk. Data backed this concern:

    • 40%+ of agentic AI projects are at risk of cancellation by 2027 due to governance and ROI gaps (Gartner)
    • 25% of enterprise breaches by 2028 will be traced to AI agent abuse (Gartner)
    • 76% of enterprises cite data privacy and security as their top AI risk concern (IDC)
    • 68% cite lack of identity security controls for AI specifically (IDC)
    • Only 21% of organizations have a mature governance model for autonomous AI agents (Deloitte)
    • 64% of CEOs acknowledge that FOMO drives AI investment before fully understanding the value (IBM)
    • $2.1 billion in regulatory fines related to AI misuse were issued globally in 2025 — a 7x increase from 2023

    CrowdStrike’s Atul Tulshibagwale and AgentCloak’s Peter Yared presented on agentic identity and the real attack surface of connecting agents to thousands of external MCP servers — including tool-poisoning attacks and data-exfiltration risks.

    The concept of “Guardian Agents” — autonomous systems whose sole purpose is to monitor, oversee, and constrain the behavior of other agents — drew both enthusiasm and skepticism. Gartner projects 40% of CIOs will demand Guardian Agents by 2028.

     

    Finance and Healthcare Lead Industry Deployments

    Steve Hasker, President & CEO, Thomson Reuters, demonstrated that many legacy information giants are betting their futures on autonomous workflows, not incremental copilot features.

    Rob Wisniewski, CTO of Credit & Insurance, Blackstone, discussed how agentic AI is moving beyond back-office automation into core deal-making and underwriting.

    Sirisha Kadamalakalva, MD, Global Head of AI/ML Investment Banking at Citi, covered agent deployment in regulated financial workflows.

    Karun Appapogu, Head of AI Technologies Architecture, Vanguard, and Kevin Hearn, SVP, Axos Bank, added retail and digital banking perspectives.

    • Shipali Jangra, Director of Global Digital Product Management at American Express, spoke to operational scale.

     

    Over 78% of financial services organizations have adopted AI agents.

    Commerzbank’s Microsoft-powered banking assistant resolves 75% of customer requests across 30,000+ monthly conversations.

    Allianz Partners is targeting 90% autonomous operations across claims and invoice processes spanning 1,000+ FTEs.

    Healthcare showed equally compelling results.

    • AtlantiCare‘s agentic AI clinical assistant achieved an 80% adoption rate among test providers, cut documentation time by 42%, and freed approximately 66 minutes per clinician per day.

    • AI-powered imaging solutions are expected to prevent up to 2.5 million diagnostic errors annually. At the enterprise level, around 65% of healthcare organizations have adopted AI agents.

    Commerce had its own dedicated sessions featuring Felipe Romano (PayPal), Robin Chiang (OpenTable), and Richard Cohene (Lightspeed Commerce).

    In manufacturing, Samsung has committed to transforming all its facilities into AI-driven factories by 2030. Fujitsu’s AI development platform, launched in early 2026, reduces software modification time from three months to four hours, according to the company.

    Seven Trends That Defined the Conference

    1. Multi-Agent Orchestration Goes Mainstream
    66.4% of the agentic AI market now focuses on coordinated multi-agent systems rather than single-agent solutions. LangGraph, CrewAI, and Google’s ADK are converging around graph-based and role-based orchestration models. Multi-agent systems are projected to grow at a 48.5% through 2030.

    2. Context Engineering Replaces Prompt Engineering
    Salesforce and others emphasized that agent performance depends less on how you ask a question and more on the information architecture surrounding the agent — which data sources it can see, how context is structured, and what gets retrieved when.

    3. Agent Identity Becomes a Product Category
    CrowdStrike, AgentCloak, Descope, C1,
    and others are building standalone products for authentication, authorization, and audit trails specifically for autonomous agents.

    4. Headless AI and API-First Architectures
    Salesforce’s Headless 360 and Google’s agent platform signal a shift from traditional UI-driven software to API-first architectures where agents access platforms programmatically rather than through dashboards. By 2028, one-third of user experiences will shift from native apps to agentic front ends.

    5. The Agent Framework Landscape Consolidates
    Framework adoption nearly doubled, rising from 9% to 18% of organizations (Datadog). LangGraph, CrewAI, and OpenAI’s Agents SDK are emerging as the dominant choices.

    6. The Workforce Transformation Is Real
    McKinsey estimates 44% of U.S. work could be performed by AI agents with current capabilities. 66% of enterprises are reducing entry-level hiring as they deploy AI. 77% of employers plan to upskill workers. AI/ML engineers’ salaries reached a median of $185K in the U.S., with demand up 74%.

    7. The Governance Gap Is Existential
    Over 40% of agentic AI projects risk cancellation by 2027. Only 21% of organizations have mature AI governance. $2.1B in AI-related regulatory fines were issued in 2025 alone. The EU AI Act is already forcing 42% of global enterprises to adjust practices.

     

    What Enterprise Should Do

    Several sessions converged on a practical playbook:

    1. Audit your current agent deployments — move beyond pilot metrics to production-grade KPIs, including completed tasks, escalation rates, and resolution speed
    2. Implement both MCP and A2A protocols to future-proof agent infrastructure for cross-vendor interoperability
    3. Establish an agent governance framework now — define identity, scope, audit trails, and escalation thresholds before scaling
    4. Prioritize context engineering over prompt engineering — invest in retrieval quality, data architecture, and information design
    5. Start where ROI is clearest: customer service, eCommerce, finance automation, and software engineering are the proven winners
    6. Budget for agentic AI security — tool poisoning, prompt injection, and data exfiltration are real attack surfaces
    7. Fix data foundations first — 52% of organizations cite data quality as their primary blocker, and IDC predicts a 15% productivity loss by 2027 for companies that fail to establish AI-ready data foundations

     

  • Owned AI Infrastructure and Data Sovereignty Emerge as Dominant Themes Across Industries

    Owned AI Infrastructure and Data Sovereignty Emerge as Dominant Themes Across Industries

    IBL News, Boston

    “Data quality, not model size, is the primary bottleneck in AI performance,” said Datology’s CEO, Ari Morcos, at the ODSC AI East 2026 in Boston this week. “Better training data and smaller models outperform larger ones trained on slop,” he explained.

    Ami Bhatt, FDA Chief Innovation Officer and Chair of the American College of Cardiology, discussed AI in clinical decision support and the FDA’s evolving framework for validating AI systems in healthcare. “At the FDA, we are building regulatory infrastructure for AI — not blocking it, but demanding rigor.”

    Across healthcare, finance, government, legal, education, manufacturing, and energy, a pattern emerged: the organizations moving fastest on AI are the ones that have solved the data privacy and deployment ownership equation, performing at frontier quality while keeping patient data locked down. They’re not asking “which model” but “where does it run, who owns the data, and can we audit every decision?”

    By industry sector, the ODSC East 2026 generated several outcomes:

    In Healthcare and Biopharma, AI in drug discovery dominated. Generative AI for molecular design is now producing novel candidate compounds in hours instead of months. Multiple sessions covered AI-driven biomarker discovery — using foundation models to identify disease signatures in genomic data that traditional bioinformatics pipelines miss entirely.

    At major health systems, medical imaging AI has evolved into predictive modeling for clinical outcomes.

    Healthcare sessions circled back to the issue of data privacy, as foundation models require massive datasets to perform, but HIPAA, patient consent, and institutional data governance impose hard constraints on data sharing. Synthetic data generation and federated learning emerged as the most discussed workarounds, but neither is mature enough for enterprise-scale deployment yet.

    In Government and Defense, federal agencies are moving from “should we use AI?” to “how do we deploy AI in air-gapped, classified, and compliance-heavy environments?” FedRAMP, NIST 800-53, and ITAR requirements dominated the conversation.

    The agencies that are moving fastest — DOD, intelligence community, DHS — are the ones with the most acute operational pain points and the budget to solve them.

    Google’s signing a classified AI deal with the Pentagon (reported during the conference week) added urgency to the discussion. The open question was whether the government AI infrastructure should be owned by hyperscalers or if agencies would build sovereign capability.

    An important takeaway was that only American-made models and compliance-first would have a massive moat.

    In Financial Services, a recurring theme was zero tolerance for AI hallucinations, as they could be a compliance violation. Multiple sessions addressed guardrails, output validation, and human-in-the-loop architectures specifically designed for regulated environments.

    Another takeaway is that the shift to open source in finance was real, and self-hosted, private deployment is becoming the default architecture. Several financial services practitioners described their institutions moving from proprietary APIs (OpenAI, Anthropic) to self-hosted open-weight models (Llama, DeepSeek, Mistral), driven not by cost but by data sovereignty.

    When your trading strategies, M&A documents, and client portfolios are the data, sending them to a third-party API is a non-starter.

    Financial services want frontier-quality AI but absolutely cannot accept the data exposure inherent in shared platforms.

    In Manufacturing and Supply Chain, AI for robotics reflected the convergence of foundation models with physical systems. Sessions covered reinforcement learning for warehouse automation, computer vision for quality inspection, and multi-agent coordination for logistics.

    The manufacturing story is about integration, not intelligence. The models are capable enough — the bottleneck is connecting AI to legacy ERP systems, SCADA networks, and supply chain databases that were built decades before APIs existed. Several sessions addressed the “last mile” problem of getting AI outputs into SAP, Oracle, and custom MES systems.

    Participants agreed that manufacturing AI is a data integration challenge first and a model challenge second. The organizations winning here are the ones who’ve invested in data infrastructure, not just model training.

    In Legal and Compliance, the recurring pattern was that law firms and in-house legal teams were deploying LLMs for document review, contract analysis, and legal research, but with extreme caution.

    Attorney-client privilege is the hard constraint. Unlike other industries where data privacy is a regulatory concern, in legal it’s a constitutional one. Multiple speakers from regulated industries described building air-gapped AI systems specifically so privileged communications never touch external infrastructure. The phrase “private deployment” came up more in legal-adjacent sessions than anywhere else.

    AI governance frameworks — as Shoshana Rosenberg presented — are being adopted fastest by legal departments, not IT departments. Lawyers understand regulatory risk intuitively and are building the policies and controls that other functions are still debating.

    Regarding Energy, Utilities, and Infrastructure companies, practitioners highlighted the need for AI that can be deployed reliably and deterministically on local hardware, without cloud dependencies, given that operations often occur on factory floors, in substations, and on drilling platforms, where connectivity is unreliable or prohibited.

    In terms of Software Engineering and Devtools, developers at the conference analyzed how to evaluate whether AI-written code is correct, secure, and maintainable. Evaluation systems, test generation, and multi-agent code review (in which multiple AI agents check each other’s work) were the most-discussed engineering patterns.

    Karen Zhou from Anthropic’s Claude Code team and Robert Brennan from All Hands AI (OpenHands/OpenDevin) enlightened the discussion.

    Software developers mostly agreed that competitive edge was in AI systems that can reason across codebases, call external services, and operate production infrastructure — not just autocomplete.

    Finally, in Higher Education and Research, Brown University’s Michael Littman, Boston University’s Mohammad Soltanieh-ha, Bentley University’s Noah Giansiracusa, and MIT’s Max Tegmark delivered major sessions. The academic-to-production pipeline has never been shorter.

    Many institutions are now building their own AI operating systems — course-specific agents, research assistants, administrative automation tools, and student support chatbots. Multiple sessions referenced institutions deploying self-hosted LLMs on their own cloud infrastructure to maintain FERPA compliance and student data privacy.

    One takeaway was that institutions that own their AI infrastructure, rather than subscribing to shared platforms, would emerge as the leaders.

     

  • Practitioners at the ODSC Event Examined Why AI Enterprise Projects Failed and Analyzed Tectonic Shifts

    Practitioners at the ODSC Event Examined Why AI Enterprise Projects Failed and Analyzed Tectonic Shifts

    Mikel Amigot, IBL News | Boston

    Around 3,500 AI practitioners (mostly data scientists, engineers, researchers, and business leaders) are attending the 11th ODSC (Open Data Science Conference) East conference this week in Boston, with a dominant theme: it’s time to execute Agentic AI across industries.

    With 300+ hours of expert-led content, 250+ speakers, and 15+ dedicated tracks, participants shared insights on the race to build the infrastructure that will define who controls AI in production.

    The conference has surfaced three tectonic shifts:

    1. Agents are replacing chatbots — not as a trend, but as a deployment pattern. Organizations that are still building Q&A bots are already behind.
    2. Governance is infrastructure, not paperwork — the organizations that build technical governance (audit trails, verification, guardrails) will move faster than those that skip it.
    3. The model is commoditizing; the stack is the moat — with 8 frontier models shipping in a single week and open-source catching up to proprietary, the competitive advantage has shifted from “which model” to “what infrastructure do you own.”

    ODSC dedicated an entire track to Agentic AI & Workflow Automation for the first time. Gartner’s prediction — 40% of enterprise apps will embed AI agents by the end of 2026 (up from 5% today) — was quoted in several keynotes.

    • MIT’s Max Tegmark challenged the room to move beyond “vibe coding” toward provably correct AI systems.
    • Pedro Domingos introduced “tensor logic” as a unifying language for AI.
    • Nouha Dziri (Cohere Labs) argued that hallucination mitigation requires architectural changes, not just better prompting.
    • Olivia Buzek from IBM ran a pre-conference workshop on building responsible AI agents with open-source tools.

    Rehgan Bleile (AlignAI) broke down why enterprise AI keeps failing to scale: Organizations invest in models and platforms but don’t invest in the organizational change management, incentive alignment, and cross-functional governance required to actually operationalize AI. Her prescription: treat AI deployment like an organizational redesign, not a technology upgrade.

    Adam Tauman Kalai (OpenAI) delivered a talk titled “Why Language Models Hallucinate,” notable because it came from inside OpenAI itself. Kalai explained the mathematical reasons behind hallucination as a phenomenon, positioning it not as a bug to be fixed but as an inherent property of probabilistic generation that needs to be managed through system design.

    Governance went mainstream. Shoshana Rosenberg (Women in AI Governance / Logical AI Governance) made the case that AI governance has moved from a compliance checkbox to a strategic competitive advantage. Her session on building future-ready governance frameworks was one of the most attended in the leadership track.

    Shoshana Rosenberg explained that organizations that build governance infrastructure now — not just policies, but actual technical controls, audit trails, and decision frameworks — will move faster in the long run than those who skip it to ship faster today.

  • American Universities Continue to Lead Globally by Academic Field, According the QS Rankings

    American Universities Continue to Lead Globally by Academic Field, According the QS Rankings

    IBL News | Washington, D.C.

    American universities continue to lead globally, with 228 ranked institutions — nearly double the next competitor — and the most number-one spots across academic disciplines worldwide, according to the QS World University Rankings by Subject 2026, released this week during the 2026 Global Skills Week in Washington, DC.

    Another remarkable statistic is that just 9 countries account for roughly half of all ranked universities worldwide.

    The top 10 by number of ranked institutions are:

    1. 🇺🇸 United States — 228

    2. 🇨🇳 China — 158

    3. 🇬🇧 United Kingdom — 114

    4. 🇮🇳 India — 99

    5. 🇫🇷 France — 93

    6. 🇩🇪 Germany — 72

    7. 🇮🇹 Italy — 61

    8. 🇪🇸 Spain — 54

    9. 🇯🇵 Japan — 53

    10. 🇰🇷 South Korea — 47

    The most dramatic story in the data is China’s trajectory. In just four years (2022-2026), China went from 90 to 158 ranked institutions, a 75% increase. Other fast risers are:

    • 🇰🇷 South Korea: 30 → 47 (+57%)

    • 🇵🇰 Pakistan: 15 → 35 (+133%)

    • 🇹🇷 Turkey: 15 → 28 (+87%)

    • 🇮🇩 Indonesia: 12 → 26 (+117%)

    • 🇸🇦 Saudi Arabia: 10 → 23 (+130%)

    Notably, India has flatlined at 99 institutions since 2022, growing in number but not adding new ranked programs.

    China alone contributed 34 new subject entries in 2026, with particular focus on Medicine (8), Chemistry (6), Economics (6), Computer Science (4), and Physics (4). India added 20, concentrated in Chemistry (5), Medicine (4), and Computer Science (4).

    Both countries are making deliberate bets on STEM, and it’s showing in the rankings.

    Through its World Future Skills Index, which weights both quality and quantity, QS formalized that the top 10 countries for “Academic Readiness” were:

    1. 🇬🇧 United Kingdom — 100
    2. 🇩🇰 Denmark — 99.6
    3. 🇳🇱 Netherlands — 99.3
    4. 🇦🇺 Australia — 98.9
    5. 🇩🇪 Germany — 98.6
    6. 🇭🇰 Hong Kong — 98.2
    7. 🇺🇸 United States — 97.8
    8. 🇨🇦 Canada — 97.4
    9. 🇮🇹 Italy — 97.1
    10. 🇨🇭 Switzerland — 96.7

    Universities investing in AI programs have been climbing these rankings faster than traditional ones, signaling that the AI capability and institutional competitiveness game is now in the rankings.

    QS is increasingly weighting “employer reputation” and “research citations” — both metrics where AI-focused universities are surging.

    Per institutions, MIT dominates across 12 STEM subjects, including computer science, engineering, and data science. MIT has been ranked #1 overall for 14 consecutive years.

    • Oxford — two departments are top in their subjects globally.
    • FIU — hospitality ranked #3, politics top 15 among US public universities
    • CU Boulder — ranked top 100 nationally in 25+ subjects, including computer science, engineering, business, law, and medicine
    • University of Hawaii Mānoa — 14 programs recognized globally

    In the ranking, data science & AI is now a standalone ranked subject — for the first time, QS treats it as a full discipline, not a subcategory of CS. This is significant because universities investing in AI programs now receive direct rewards in rankings.

    The ranking and its conclusions were presented by Leigh Kamolins, Vice President, Evaluation and Insights at QS Quacquarelli Symonds, and Jacques de Champchesnel, Head of Consulting, QS Quacquarelli Symonds, during the 2026 Global Skills Conference [in the picture above].

    The session title was “How the Higher Education Sector is Rising to the Skills Challenge: The QS World University Rankings by Subject.” [Slides]

    They both highlighted the US paradox: With over 200 institutions but a median score of less than 50, America has extraordinary peaks — MIT, Stanford, Harvard — but a very long tail of weaker programs that drag the average down. The US ranks 34th in median quality.

    On AI readiness and who’s actually training the AI workforce, QS assessed countries on subjects that directly feed the AI and digital economy: Computer Science, Data Science, Engineering & Technology, Electrical Engineering, Mathematics, Statistics, Linguistics, Psychology, Business, Communication, Library Management, and Art & Design.

    “The pattern is unmistakable: Hong Kong, the Netherlands, Denmark, and Australia consistently outperform the US in AI-related academic quality. The US ranks near the bottom of the top 10 in every single AI/digital category — 43 in Computer Science, 49 in Engineering, 47 in Electrical Engineering,” said the authors to IBL News.

    The top 10 countries were:

    Computer Science:
    Hong Kong 70 → Netherlands 60 → Denmark 58 → Australia 54 → Switzerland 53 → Canada 51 → UK 50 → Italy 44 → US 43 → Germany 42

    Data Science:
    Hong Kong 70 → Denmark 59 → Switzerland 57 → Netherlands 56 → Australia 56 → Belgium 56 → UK 52 → Germany 42 → US 43

    Engineering & Technology:
    Hong Kong 74 → Denmark 66 → Netherlands 66 → Belgium 63 → Australia 59 → Canada 59 → UK 56 → Italy 54 → Switzerland 52 → Germany 50 → US 49

    Electrical Engineering:
    Hong Kong 73 → Netherlands 69 → Denmark 67 → Switzerland 67 → Australia 62 → Belgium 57 → Canada 55 → UK 54 → Italy 50 → Germany 48 → US 47

    Leigh Kamolins and Jacques de Champchesnel highlighted the AI research explosion, as the number of AI publications in Computer Science grew from 100,000 in 2013 to 242,740 in 2023.

    “Universities that aren’t investing in AI research infrastructure are falling behind in real-time.”

    As key takeaways, these two QS researchers insisted on seven points:

    1. Quantity ≠ Quality. The US has the most ranked universities in the world, but ranks 34th in median quality. As mentioned, having 228 programs means nothing if most of them underperform.

    2. The real AI education leaders aren’t who you’d expect. Hong Kong, the Netherlands, and Denmark consistently outrank the U.S. in AI and digital subjects. Small, focused systems beat sprawling ones.

    3. China is the fastest-growing force in global education. From 90 to 158 ranked institutions in 4 years, with a strategic focus on STEM. India has stalled.

    4. Research impact is the differentiator. Countries that score highest in AI subjects aren’t just publishing more — they’re being cited more. Quality of research, not volume, is what separates the elite.

    5. The UK is the world’s most balanced education system. #1 on the Future Skills Index for Academic Readiness — combining 114 institutions with consistently high quality across subjects.

    6. AI publications have grown 143% in a decade, with 2023 seeing the biggest single-year jump (+20.8%) — driven by the generative AI boom.

    7. Higher education is an economic policy. QS’s framing is explicit: countries that invest in AI-, digital-, and sustainability-focused academic programs are positioning themselves for long-term economic competitiveness. Universities are no longer optional infrastructure — they’re strategic national assets.

    “How the Higher Education Sector Is Rising to the Skills Challenge (PDF Presentation Slides)

     

  • Human Skills-Centered Liberal Arts Education Can Help Institutions In Decline, Says ‘Deloitte 2026 Trends’ Report

    Human Skills-Centered Liberal Arts Education Can Help Institutions In Decline, Says ‘Deloitte 2026 Trends’ Report

    IBL News | Washington, D.C.

    The U.S. higher education system faces intense financial pressure from all sides as international and graduate enrollment declines, funding is cut, student loans are capped, AI advances, public confidence weakens, and policymakers and new regulations question the sector’s business model and ROI.

    “Institutions can play a critical role in preparing the next generation with the skills needed for a rapidly changing world,” said Cole Clark, Managing Director at the Higher Education sector in Deloitte Services, when presenting the 2026 Higher Education Trends report during the ACE Experience conference hosted last week in Washington, DC. “Consider a future with fewer but stronger US colleges as more institutions choose to merge or form strategic partnerships,” he added.

    The analysis portended the renewed importance of building adaptable, human-centered capabilities within liberal arts education, highlighting the need for higher education to re-establish itself as an engine of upward mobility. In this regard, AI will underscore the enduring importance of fundamentally human skills—communication, judgment, and teamwork, said Deloitte.

    “Institutions have an opportunity to chart a more sustainable path forward by reconciling two realities: Students overwhelmingly seek degrees that lead to meaningful employment, and employers need graduates who not only have immediate skills but also the agility to adapt as work continues to evolve—especially under the influence of AI.”

    Deloitte’s Center for Higher Education Excellence convened college and university presidents in November 2025 at Deloitte University in Westlake, Texas. After institutional leaders shared successes and lessons learned to drive change, the consultancy company described and prioritized the 2026 trends.

     

    • Trend 1: Erosion of the revenue model for higher education

    In some cases, the reductions have been substantial: The University of Southern California laid off more than 900 employees; Stanford University cut 363; and Northwestern University laid off 424, amounting to about 5% of its workforce.

    The Institute of International Education reported a 17% drop in new international student graduate enrollments this past fall.

    In 2024, more than half of private universities rated by S&P Global posted operating deficits, up from the year before, and early 2025 results look even weaker. A recent analysis of 44 midsize universities with enrollments of between 1,000 and 8,000 students found a weak financial outlook, with many at risk of becoming insolvent in five to 10 years if enrollments fall by 1% to 3% per year over that period. The nation is projected to see a 13% decline in college enrollment from 2025 through 2041.

    “While more uncertainty and challenges may come, we are optimistic that creativity and openness to new models will enable us to meet the current moment and our future,” Boston University’s president, Melissa Gilliam, and provost, Gloria Waters, said in a letter to the community announcing a round of layoffs.

     

    Trend 2: Shifting the conversation from the ‘cost of college’ to the ‘value of a credential.’

    Data have long shown that people with college degrees earn a substantial earnings boost compared to those without one. The latest data from the Bureau of Labor Statistics show that workers age 25 and older earn 80% more per week than those with only a high school degree. While that’s true on average, the situation for individuals varies widely depending on a person’s major and other factors.

    Colleges now offer more credentials than ever; nearly 1.1 million credentials are offered in the United States. However, the vast majority of nondegree credentials don’t lead to higher paychecks, with only 12% of credentials delivering significant wage gains, according to the Burning Glass Institute.

    The July 2025 passage of H.R.1 may further encourage students to pursue non-degree credentials through a provision known as Workforce Pell, which stipulates that low-income students can use Pell grants to pay for credential programs as short as eight weeks. While details must be worked out before the program takes effect in July 2026, the change is expected to increase interest in nondegree credentials.

    A study released in November 2025 by the Massachusetts Institute of Technology found that nearly 12% of the U.S. workforce could be replaced by AI tools.

    Expanding internships and apprenticeship programs has also proven helpful in bridging the gap between college and work. Recent research by the Strada Education Foundation found that 73% of graduates who completed a paid internship landed a first job that required a degree, compared to 44% of those without an internship.

    The rise of AI may lead to a resurgence of interest in the humanities. Proponents of the humanities say that as AI tools reshape jobs, critical thinking, ethics, and judgment will become more highly valued, while the number of jobs in areas such as coding will shrink.


    • Trend 3: A reset for sponsored research
    .

    2025 was marked by an unprecedented change and a reduction of federal research dollars after decades of steady growth, including amendments to previously awarded grants, workforce reductions, and incentivized early retirements of thousands of workers at federal agencies that produced research and proposals to reduce future federal research funding.

    With funding reductions, many of the top research institutions have trimmed research budgets, frozen hiring, pulled back on PhD admissions, and reduced their workforce, actions that will likely have ripple effects.

    In 2026, philanthropic groups, especially big tech and pharmaceutical companies, have emerged as significant funders.

    However, federal research support is 10 times that of philanthropy, with US$50 billion from the federal government compared to US$5 billion from philanthropy as of 2021.

    Philanthropists such as Roy and Diana Vagelos made a historic donation to Columbia University in 2024 of US$400 million for basic biomedical research, and the Howard Hughes Medical Institute, which has a longstanding pledge to support college research in the biomedical sciences, has given out more than US$7 billion to researchers since 2004.

    As universities pursue grants from philanthropic and corporate sponsors, research may shift toward applied work rather than basic science.

    Meanwhile, other global powers are ramping up their efforts: The Chinese government increased research support by 10% in 2024. The European Union is debating plans to double the funding for its flagship research program, Horizon Europe, to more than US$200 billion between 2028 and 2034. If US colleges fail to find new models for research support, some experts worry about a brain drain of top science talent to other countries.

    Some leaders are betting on emerging AI tools to meet reporting requirements on grants more efficiently.

    Models in which principal investigators (PIs) are employed by both the university and industry are increasing in popularity—enabling PIs to draw a larger salary from the portion of their work conducting research for industry while continuing to support the mission of their institution for a lower pay rate.

    University leaders are treading carefully to preserve the integrity of scientific discovery, avoiding politicizing the selection of research topics, while also preserving the United States’ ability to lead in scientific exploration and innovation.


    • Trend 4: More colleges explore mergers and partnerships to preserve core missions amid demographic and financial pressures.

    Mergers, once seen as taboo, akin to admitting failure, but today college leaders are shifting, and higher education is entering a “consolidation era.” Merger success stories are starting to bubble up.

    Roughly 80 nonprofit colleges and universities have shuttered or merged in the past five years—not only reflecting an increase in activity, but also seeing a shift from for-profit closures to nonprofit closures, as well as the first instances of a public institution shuttering (not merging).

    Nearly 20% of college presidents said it was somewhat or very likely that their institution would merge or be acquired in the next five years.

    Antioch University and Otterbein University cofounded the Coalition for the Common Good in 2023, while maintaining distinct undergraduate brands and collaborating on graduate programs and shared services.

    Gannon University in Erie, Pennsylvania, is in the process of merging with Ursuline College near Cleveland. Even though the institutions are only about 100 miles apart, the fact that they are in different states is key, as state policies provide financial incentives for students to stay in-state when seeking financial aid. Both colleges say they are financially healthy for now, but see strengths and greater potential for enrollment growth by combining.

    Pomona College, a private liberal arts college with around 1,700 students, is reportedly in talks to acquire Claremont Graduate University, which has around 2,200 students.

     

    • Trend 5: A changing global higher education landscape necessitates strategic shifts by American universities.

    Many leaders from around the world have earned their degrees from US universities and colleges. The U.S. has long been the most desirable destination for higher education, one of America’s top exports, bringing in more revenue than natural gas and coal combined.

    Many American colleges and universities have come to depend on international students as a key revenue source. Students from abroad now make up about 6% of total enrollment at US colleges, or nearly 1.2 million students. At elite institutions, in particular, these students typically pay the full posted tuition rates, which often works out to two to three times what an average domestic student pays.

    New international enrollments have recently faltered, however, in part due to new restrictions and heightened scrutiny of student visas.

    The number of international students enrolling in American colleges fell by 17% in fall 2025, the first time in 10 years. This decline has been estimated to cost the US economy US$1.1 billion, according to an analysis by the National Association of Foreign Student Affairs and the Association of International Educators. The vast majority of colleges seeing a decline cite concerns about obtaining student visas as a key factor, with two-thirds of those colleges pointing to travel restrictions as a reason.

    Across all science and engineering fields, 47% of graduate students and 58% of postdocs are international. Many of these students intend to remain in the country after graduation to work in science and tech fields.

    Nearly a third of international students at American campuses come from India, which, in 2020, adopted a policy paving the way for more foreign campuses.

    Meanwhile, universities in Asia and Europe recently reported increases in new international student enrollment. If this trend continues, it could lead to a shift in higher ed enrollments from west to east.

    Online options may also expand, helping attract more international students. A recent survey of international student recruiters found a jump in interest from international students in seeking entirely online degrees from US colleges.

    More U.S. colleges may also choose to bring their educational offerings to other parts of the world by, for instance, adding branch campuses abroad. Currently, American colleges already have more branch campuses abroad than any other country, with 97 satellite campuses in 40 countries.