The Sci-Fi Moment of OpenClaw and Open Source AI Agents

This month, when we saw the creation of Moltbook, a Reddit clone where AI agents using OpenClaw could communicate with one another, some thought that computers had begun to organize against humans. “What’s currently going on at [Moltbook] is genuinely the most incredible sci-fi takeoff-adjacent thing I have seen recently,” Andrej Karpathy, a founding member of OpenAI and previous AI director at Tesla, wrote on X at the time. However, due to Moltbook’s security vulnerabilities and flaws, anyone could create an account, impersonate robots in an interesting way, and even upvote posts without any guardrails or rate limits, making the technology unusable. Moreover, on a social network for agents, someone could try to perform mass prompt injection and send Bitcoin to a specific crypto wallet address. For sure, Moltbook made for a fascinating moment in internet culture and went viral. The open-source AI agent OpenClaw has amassed over 200,000 stars on GitHub, making it one of the most popular code repositories ever posted on the platform. AI agents are not novel, but OpenClaw made them easier to use and to communicate with, via customizable agents that can be used in natural language via WhatsApp, Discord, iMessage, Slack, and most other popular messaging apps. OpenClaw users can leverage any underlying AI model they have access to, whether via Claude, ChatGPT, Gemini, Grok, or another service. Users can download “skills” from a marketplace called ClawHub, which can automate most of what one can do on a computer, from managing an email inbox to trading stocks. The skill associated with Moltbook, for example, enabled AI agents to post, comment, and browse the website. Some experts say that, at the end of the day, OpenClaw is nothing novel and does not break new scientific ground. It is an iterative improvement on what people are already doing, hitting a new capability threshold by just organizing and combining these existing capabilities to do tasks autonomously. It's not just a wrapper to ChatGPT, or Claude, or any other AI model. Currently, developers are snatching up Mac Minis to power extensive OpenClaw setups that might be able to accomplish far more than a human could on their own. In this context, OpenAI CEO Sam Altman predicted that AI agents will allow a solo entrepreneur to turn a startup into a unicorn, seem plausible.

The Demystifying Book "The Teaching with AI" Releases Its Second Edition

Voice AI Tool to Reduce Teachers' Burnout, Socrait Awarded at the FETC 2026 Conference

Organizations Are Moving From Endless Pilots to Real Business Value Project, Says Deloitte

Enterprises Move From Experimentation to Measurable ROI On AI, Says Wharton School

Enterprises are rapidly transitioning from experimentation to proving measurable ROI, said a research study conducted by The Wharton School of the University of Pennsylvania. "Gen AI is becoming deeply integrated into modern work." The report examines how generative AI technology is being adopted in mainstream enterprises, highlighting the ROI. The shift is moving from exploration to pilots to more disciplined, enterprise-level adoption, according to the study, which concludes that "the next phase is not about adoption; it is about advantage." • 82% of enterprises used Gen AI at least weekly, and 46% used it daily, with 89% agreeing that Gen AI enhances employees’ skills. • A total of 72% of organizations formally measured the ROI of Gen AI, focusing on productivity gains and incremental profit. • Three out of four leaders see positive returns on Gen AI investments. The Wharton School predicted that approximately one-third of Gen AI technology budgets will be allocated to internal R&D, indicating that many enterprises are building custom capabilities for the future. "Training, hiring, and rollout approaches are key human capital aspects that need to be addressed to increase chances of success." • Download the Executive Summary • Download the 2025 Report

OpenAI Announced 1M Business Customers and 800M Users Weekly

OpenAI announced yesterday that it has 1 million business customers worldwide using ChatGPT for Work, either directly or through its developer platform. Organizations related to areas such as financial services, healthcare, and retail are among the most active. Consumer adoption is also at high rates, with 800 million users weekly. The San Francisco-based lab is set to generate $13 billion in revenue this year as it continues to expand sales. To support the enterprise acceleration, OpenAI launched a new wave of tools and integrations, such as: Company knowledge consolidates all the context from connected apps (Slack, SharePoint, Google Drive, GitHub, Canva, Figma, Zillow, Spotify, among others) into ChatGPT. Codex model for code generation, refactoring, and workflow automation. AgentKit allows users to create and build enterprise agents. Multimodal models, such as the Image Generation API, Sora 2, gpt-realtime, and Realtime API to build production voice agents. Databricks has made OpenAI models available natively on its stack. The Agentic Commerce Protocol (ACP) enables the creation of conversational commerce experiences in ChatGPT. Shopify, Etsy, Walmart, PayPal, and Salesforce are among the companies utilizing this protocol. OpenAI quoted a recent Wharton study to highlight that 75% of enterprises report a positive ROI, and fewer than 5% report a negative return. "When AI is deployed with the right use case and infrastructure, teams see real results," said the firm. This week, too, OpenAI agreed to pay Amazon.com $38 billion for computing power in a multiyear deal. That marks the first partnership between the startup and the cloud company.

The George Washington University Launches its GW Engineering AI Academy

The Trump Administration Dismisses the Risks of AI as It Pursues Faster Economic Growth

Institutions, In a Hurry Up to Comply with the Disabilities Act of 2024

Educause Releases the Top 10 Report Predicting Where Higher Ed Will Be in 2026

Educause yesterday released its Top 10 list of where higher education is headed in 2026 during the second day of its annual conference, which gathered around 7,600 educational professionals (15% over expectation) on October 28 -30 in Nashville, Tennessee. This ranking was primarily focused on cultivating a data-centric culture and building collective will around technology initiatives. “Tensions around free speech and ideological differences in the classroom are leaving leaders and faculty uncertain about their future in academia. And the financial stability of many institutions, and of Minority Serving Institutions (MSIs) in particular, is increasingly strained by reductions in federal support,” said Educause. “We must find ways to cultivate human connection, both within ourselves and with one another.” Traditionally, the Top 10 report, which highlights how technology leaders are thinking about and planning, serves as a guide for institutions to move forward. This year, the Top 10 was presented by Dr. Crista Copp, Vice President of Research at Educause, and Dr. Mark McCormack, Senior Director of Analytics & Research. #1. Collaborative Cybersecurity: Building a cybersecurity culture of shared responsibility, end-user awareness and training, and improved access to security services and support #2. The Human Edge of AI: Empowering students, faculty, and staff to engage with artificial intelligence tools critically, creatively, and safely #3. Data Analytics for Operational and Financial Insights: Leveraging data analytics to provide insights into spending patterns, enrollment trends, and areas for cost savings and operational efficiencies #4. Building a Data-Centric Culture Across the Institution: Expanding and improving data access and unlocking the full potential of data as a strategic asset #5. Knowledge Management for Safer AI: Mitigating the risks of artificial intelligence by integrating knowledge management into data governance, privacy, and ethics programs #6. Measured Approaches to New Technologies: Making better technology investment decisions (or choosing not to invest) through clear cost, ROI, and legacy systems assessments #7. Technology Literacy for the Future Workforce: Supporting discipline-specific technology training and education to enhance student success with in-demand technology skills #8. From Reactive to Proactive: Using data for scenario modeling, forecasting, and prediction to strengthen institutional agility and planning #9. AI-Enabled Efficiencies and Growth: Using artificial intelligence, robotic process automation, and other analytics capabilities to reduce operational costs, streamline processes, and improve strategic and business decision-making #10. Decision-Maker Data Skills and Literacy: Enhancing the value of institutional data by training and equipping decision-makers to use and interpret it properly • 2026 Top 10 infographic

Amazon Releases "Quick Suite", a Collection of AI Agentic Tools

This month, Amazon issued its new agentic platform, Quick Suite, a collection of AI tools that analyze and gather data from multiple applications, turning natural language queries into enterprise actions, processes, and workflows. As a unified digital workspace for day-to-day work, it connects internal documents, emails, and databases with external third-party apps, sources, and services, including Amazon S3, Snowflake, Google Drive, and Microsoft SharePoint. It also integrates with third-party services like Salesforce for customer data, Zendesk for support tickets, and Slack for team collaboration. These enterprise processes often require consultation with specialized teams to analyze advanced datasets, Amazon Quick Suite includes productivity capabilities such as research, interactive visualizations (in competition with Tableau and Microsoft Power BI), and business intelligence and automation tools and agents. Amazon made Quick Suite available in two tiers: a Professional plan starting at $20 per user per month, and an Enterprise plan at $40 per user per month with advanced features. AWS marketing chief Julia White said to Bloomberg that existing customers of the Amazon Q Business AI software, which launched 18 months ago, will be encouraged to migrate to the new platform. The company said Quick Suite has already been deployed to tens of thousands of Amazon employees, reporting that “the tool has reduced complex data analysis tasks from months to minutes, for example, based on internal use.” In addition, it has rolled out the platform to hundreds of corporate beta customers, citing examples of cost savings and efficiency improvements. However, since Amazon does not own a native productivity suite like Microsoft 365 or Google Workspace, it has to convince users to adopt its platform as an overlay on top of those tools. This month, Google announced a unified Gemini Enterprise AI agent subscription, consolidating its business-focused AI tools.