Generative AI 101

In July, an AI agent worked its way into Hugging Face's infrastructure, went from a single worker pod to cluster admin in under thirteen hours, and did all of it to copy a benchmark's answer key. Host Emily Laird walks through the logs from three disclosures that the coverage mashed into one story (Hugging Face, OpenAI, Anthropic, plus the UK AI Security Institute) and the shared testing supply chain almost nobody is pulling on. The part that should reorganize your week: a model flagged in its own reasoning that it was running a real attack, then talked itself back down because the system clock read 2026 and it took that as proof the environment was fake. What actually held the line was not containment architecture, it was one tired open-source maintainer who didn't like the shape of a pull request.

 

 

🎯 JOIN THE AI WEEKLY MEETUPS

https://www.uwstout.edu/ai-weekly-meetup

 

📩 EMAIL REMINDERS FOR THE MEETUPS

https://app.e2ma.net/app2/audience/signup/2101263/1779703/

 

💬 CONNECT WITH EMILY LAIRD ON LINKEDIN

http://www.linkedin.com/in/meet-emily-laird

Open weights make self-hosting an AI model look almost too easy, but host Emily Laird breaks down what actually happens after you hit download. This episode walks through the infrastructure, staffing, security and compliance costs that separate a slick demo from a real institutional service, including GPU power draws, KV cache limits and FERPA obligations. It's a reality check on when owning your own model actually saves money, and when it just means insourcing a cloud provider without the cloud provider's scale. If you've ever heard someone ask "why are we paying Microsoft," this episode answers it.

 

🎯 JOIN THE AI WEEKLY MEETUPS

https://www.uwstout.edu/ai-weekly-meetup

 

📩 EMAIL REMINDERS FOR THE MEETUPS

https://app.e2ma.net/app2/audience/signup/2101263/1779703/

 

💬 CONNECT WITH EMILY LAIRD ON LINKEDIN

http://www.linkedin.com/in/meet-emily-laird

An analyst went through sixty-eight AI models and found that exactly zero of the downloadable ones qualify as open source. In this episode, host Emily Laird explains what open weights actually gets you (the house, not the blueprints) and why the training data you never see is the only part that matters. She also walks through Jensen Huang's first post on X, the distillation argument buried inside it, and the EU AI Act exemption that vanishes right when a model gets capable enough to be worth using. If you have told your board you are running open source AI, consider this a correction.

 

🎯 JOIN THE AI WEEKLY MEETUPS

https://www.uwstout.edu/ai-weekly-meetup

 

📩 EMAIL REMINDERS FOR THE MEETUPS

https://app.e2ma.net/app2/audience/signup/2101263/1779703/

 

💬 CONNECT WITH EMILY LAIRD ON LINKEDIN

http://www.linkedin.com/in/meet-emily-laird

Elon Musk said under oath that xAI partly distills OpenAI's models, and the courtroom gasped. Host Emily Laird takes apart what model distillation actually is, why hiding chain of thought was never a real defense (fabricated reasoning traces deliver roughly 96.7 percent of the value of genuine internal access), and what 24,000 fraudulent accounts look like when no vulnerability was exploited and the product worked exactly as designed. The uncomfortable part is structural: every dollar spent making a model cleaner and safer makes it a better teacher for whoever is copying it. Capability transfers through distillation, safety does not, and nobody has ever un-released 2.8 trillion parameters.

 

🎯 JOIN THE AI WEEKLY MEETUPS

https://www.uwstout.edu/ai-weekly-meetup

 

📩 EMAIL REMINDERS FOR THE MEETUPS

https://app.e2ma.net/app2/audience/signup/2101263/1779703/

 

💬 CONNECT WITH EMILY LAIRD ON LINKEDIN

http://www.linkedin.com/in/meet-emily-laird

Ethan Mollick’s Summer 2026 AI guide makes one thing clear: the biggest shift is no longer model intelligence, it is what AI agents can do once you give them access to your computer, inbox, and files. Host Emily Laird breaks down Mollick’s recommendations for ChatGPT, Claude, Gemini, and Copilot, including the moment ChatGPT sent an email he expected it to draft. The real issue is prompt injection, forgotten permissions, and the uncomfortable fact that an AI can behave exactly as authorized while still doing something you did not expect. As agents become more reliable, the risk is moving from hallucination to control.

 

🎯 JOIN THE AI WEEKLY MEETUPS

https://www.uwstout.edu/ai-weekly-meetup

 

📩 EMAIL REMINDERS FOR THE MEETUPS

https://app.e2ma.net/app2/audience/signup/2101263/1779703/

 

💬 CONNECT WITH EMILY LAIRD ON LINKEDIN

http://www.linkedin.com/in/meet-emily-laird