Meta preps to alleviate your daily online routine with agentic tools; Harvard professors are getting AI clones to focus on more personalized interactions with students; Experian helps users browse credit card offerings with ChatGPT; while Anthropic experiments with AI tech controlling physical equipment. AI use cases scope is expanding daily and big industry names are involved, but safety concerns do not vanish, especially since details of OpenAI agents coordinated attack on Hugging Face were revealed.
Artificial intelligence is still answering a lot of human questions daily and generating tons of content guided by user prompts. Yet, these week’s advancements highlight how the AI technology developers prepare the infrastructure for taking actions on behalf of people, operating software and even controlling physical equipment. We witness these projects evolving across consumer services, education, financial products and scientific research, notwithstanding that recent AI incidents continue to raise questions about whether the security infrastructure around advanced AI is keeping pace with self-improving tech sophistication.
Inside Meta’s Project Hatch: Who Needs AI Agent from Social Media Magnate
Meta is reportedly testing a consumer AI agent internally known as Project Hatch, designed to handle everyday online tasks for users. The agent can reportedly book appointments, generate text and audio, manage calendars and emails, fill out forms, post on social media, shop online and perform research on a user’s behalf. It is now being tested by Meta employees as the company explores AI that can remain active in the background rather than waiting for individual prompts. Company’s internal memo quoted by Business Insider describes Hatch as “a personal agent that’s always working on your behalf to help achieve your goals and improve your life, your health, your relationships, your personal finances and more.”
This project is part of corporate vision earlier described by Meta CEO Mark Zuckerberg, which narrows down to “building personal superintelligence for everyone”. Given that large share of Meta’s revenue comes from advertising, besides daily personal agenda, the new consumer AI agent could be used by FB and Instagram influencers for SMM campaigns, potentially helping to tune in to these social media algorithms. Another potential use case is management of a Facebook marketplace account and sales. So far, no sold details are available publicly, though some media reports suggest the service will be worth up to $200 a month.
One potential concern about the tool is that Hatch also works in the background “even when the app is closed,” enhancing consumer conspiracy theories on whether Facebook and Instagram are listening to your conversations.
When Your Professor Is an AI Clone: Why Universities Step Away From Old-Fashioned Education Ways
Education is also becoming a testing ground for increasingly personalized AI. Harvard Business School has created AI avatars of seven professors, allowing entrepreneurs to practice investor pitches, sales calls and board meetings against digital versions of the faculty. The system is part of an eight-week, $699 HBS Foundry program and has already been used by hundreds of founders at more than 100 universities across the country.
Interestingly, the project team initially started with simple AI customer support chatbot capabilities, but student feedback prompted them to leverage more advanced AI tools to guide them through the school’s startup resources and help hone pitching skills in safer environment.
“It was just so clear from what the users were telling us and what the numbers were saying. It seems like people actually much prefer the AIs, and they actually trust more, if it is based on one specific person’s input.”
Katharina Rings, HBS Foundry project director
Meantimes, for the HBS professors, the AI avatars are also time-savers. Leveraging these tools, they can provide proper education to more potential startup founders, stepping up only where most needed, e.g. in live weekly sessions.
Meanwhile, Georgia Tech has also continued developing AI teaching assistants, while more than 100 universities have reportedly tested similar AI education tools.
The Institute pioneered AI teaching assistants with Jill Watson, a virtual assistant launched in 2016 by Professor Ashok Goel’s team for the Online Master of Science in Computer Science (OMSCS) program. Originally built on IBM’s Watson platform, the latest version of Jill Watson now integrates OpenAI’s ChatGPT and is allegedly outperforming it in real-world educational settings. Namely, Jill Watson’s accuracy on synthetic tests varies between 75% – 97%, vs OpenAI’s assistant’s 30% score.
In these settings, time-saving is one of major factors as well.
“I can have 20, 30 meetings a week, but beyond that, I literally can’t,” Solomon, a principal at the Georgia Institute of Technology, reportedly told the Times. “We can’t service the class, let alone the campus, in person constantly. So we need a way to scale.”
Experian Credit Cards App is Now on ChatGPT
The financial sector is taking a more cautious step to agentic AI use, with less autonomy than in previous use cases but that’s implied by the sensitive matter at hand, which is credit applications. Experian launched its Credit Cards app on ChatGPT on August 27, allowing consumers to discover and compare credit card offers inside a conversational AI experience. Users can review annual fees, introductory bonuses, rewards rates and APR information before continuing to Experian platform for offers matched to their credit profile. The launch follows Experian’s earlier efforts to bring loan, insurance and credit-score experiences into ChatGPT.
“Our goal is to help consumers find credit card products that align with their financial goals and make those products easy to choose, get and use,” said Michael Coleman, Chief Marketing Officer at Credit One Bank. “Experian’s Credit Cards app on ChatGPT creates a new way for consumers to discover and compare card options through a trusted, conversational experience. We’re excited to be part of an innovation that helps consumers make more informed decisions before they apply.”
Perhaps, for third-party users, that experience wouldn’t differ greatly from simple comparisons of credit products with ChatGPT. However, Experian members benefit from the tool, as they can at once view any pre-approved offers they are qualified for and take notes on cards labeled No Ding Decline, which won’t affect their credit scores if application is declined. These details might be small but impactful enough to use the dedicated AI assistance rather than generic chatbots that have no access to firm’s AI data analysis.
Experian has been working with OpenAI for a while now. The partners have earlier enabled consumers to show credit-applicants-to-be how credit scores vary across relevant locations and age groups. This functionality was supposed to help ChatGPT users and potential borrowers in the UK understand the benchmark credit score ratings in simpler terms.
Anthropic Tests AI With Lab and Robotic Equipment
At the more advanced end of the spectrum, Anthropic is pushing AI into the physical world. The company has opened a research preview of the Model Hardware Standard (MHS), a shared specification intended to let AI agents safely operate programmable laboratory and manufacturing equipment.
Early applications include microscopes, liquid handlers and robotic arms, as well as tasks such as drug-discovery experiments and laser calibration for quantum computers. The challenge here is not simply to make AI control some of these tools but potentially orchestrate their functioning, i.e. getting multiple devices in a lab or on a factory floor to communicate with one another and modify their operations in real-time based on changing factors. The possibilities of complex AI systems with machinery involved are numerous, starting from AI healthcare diagnosis towards fully automated warehouses or factories.
Anthropic says the standard is being developed with research and industrial partners before a planned open-source release. The AI developer is experimenting with those use cases in collaboration with HHMI Janelia Research Campus.
Together, all these developments mentioned above illustrate how quickly the definition of an AI “use case” is evolving and penetrating all spheres of human life and business.
AI Technology Is Expanding So Quickly, Security Doesn’t Catch Up
That rapid expansion is happening against a backdrop of persistent safety concerns. We speak of AI ethics, liabilities, and its ability to work both ways when it comes to cybersecurity. Reuters reported this week that roughly 700 AI agents involved in an OpenAI-related test were found to have conducted coordinated attacks against systems at Hugging Face. They were supposed to work independently, but instead united to break the Hugging Face cyber protenctions. Meanwhile, those AI agents also compromised OpenAI infrastructure by exploiting vulnerabilities and stealing credentials.
The incident happened some time ago, but investigation results just came out, which in our humble opinion also reflects the sheer difference between AI action speed and human reactions on it. The latter takes time, often days, while AI operations are lighting-fast. The incidents again highlighted a particularly difficult problem: increasingly capable systems can use tools, pursue objectives and probe the environments around them with limited human intervention. That’s exactly what people want from them. But what happens when AI agents have their own agenda? How to tackle it, report the cases, amend their algorithms, and who is even responsible for AI agents going rogue? Some of that will presumably be addressed in new Nvidia-led alliance – AI cybersecurity initiative called SAFE.
The answer to those issues may not simply be to make models less capable. Bankwatch argues that the immediate security problem is often the gap between AI speed and the speed at which organizations patch vulnerabilities. Its analysis says advanced models have not been shown to reliably defeat genuinely hardened defenses, but can exploit weaknesses that already exist, including unpatched systems, flat networks and weak identity controls. Phishing-resistant multifactor authentication, strong isolation, tightly controlled access and faster patching can therefore be as important as model-level safeguards.
The challenge for businesses is multifold. AI agents are gaining more access because that access is what makes them useful. The more useful AI virtual assistants become, however, the more organizations must treat them not simply as software tools, but as powerful actors operating inside real systems. The race to deploy AI may therefore depend as much on improving the security around the model as on improving the AI software development model itself.
