So… is ChatGPT actually coming for your job? In this episode, I walk you through the GPT-5 release, what’s new, what’s noise, and why I think most folks won’t even notice a difference. We’ll talk about how AI is being used at work, why the headlines can be misleading, where the tech still falls short, and why job-loss predictions are far from straightforward.

• Key GPT-5 updates for users
• OpenAI’s “phD-level intelligence” claim
• New email and calendar integration
• Benchmark testing and hallucination improvements
• AI hype vs. real job impact
• Media narratives around AI job loss
• Microsoft study on AI and job tasks
• Defining “agents”, “agentic”, an “AGI”
• Summary of ChatGPT’s potential and limits
• Advice for AI-proofing your job
• How I used ChatGPT to create the podcast intro music

• OpenAI’s GPT-5 Announcement (article)
• OpenAI’s GPT-5 Announcement (YouTube Video)
• Microsoft Article
• Suno AI Music Generator Affiliate Link
• Subscribe to the ChatGPT Curious Newsletter: The Curious Companion
• Submit a Question
• Visit the Website
• Feeling curious AND generous? Click here to support the podcast.

• (00:00:00) - ChatGPT Curious
• (00:00:38) - The Curious Companion
• (00:02:23) - Chat GPT 5: What You'll Notice
• (00:08:32) - Is AI Coming for Your Job?
• (00:10:38) - Does AI Threaten Your Job?
• (00:16:44) - AI Agents, AGI and ChatGPT
• (00:23:10) - GPT: Future Proofing Your Workforce
• (00:23:59) - How I Use ChatGPT to Generate Music For My Podcast
• (00:25:26) - Curious Planet

In this episode of ChatGPT Curious, I lay the groundwork for understanding what ChatGPT actually is, without getting too lost in the weeds. I break down key terms like LLM and parameters, explain how the model was trained (hint: lots of math), why it sometimes spits out wrong info, and what all of this means for how you use it. I also touch on the environmental cost, what the free vs. paid versions can actually do, and how to think critically about its outputs. If you've ever felt a little confused, a little curious, or both, this one's for you.

• Why it’s worth understanding the foundation of ChatGPT
• What is ChatGPT?
• Brief history of OpenAI and the development of GPT
• What GPT actually stands for and what changed with each version
• What “parameters” are and how they shape the model’s responses
• How language is turned into numbers via tokens
• What happens during training
• Human involvement in model training via RLHF (reinforcement learning with human feedback)
• Probabilistic vs deterministic systems and what that means for output accuracy
• The environmental cost of “compute” and an analogy for mindful use
• What the free version can do (and can’t), including search, uploads, and voice
• What the paid version offers
• What ChatGPT is not
• What to watch out for
• Real-life use case: Trying to fix a bike derailleur using ChatGPT

• Try the Tokenizer
• Try out the Free version of ChatGPT
• Subscribe to the ChatGPT Curious Newsletter
• Submit a Question
• Visit the Website
• Feeling curious AND generous? Click here to support the podcast.

• (00:00:00) - ChatGPT Curious
• (00:00:38) - What Is ChatGPT?
• (00:02:47) - ChatGPT
• (00:04:32) - parameters of OpenAI's LLMs
• (00:06:11) - Laplike 2.8
• (00:13:31) - ChatGPT and the Probability of Outputs
• (00:19:22) - ChatGPT: Algorithms That Predict the Next Word
• (00:21:39) - ChatGPT: Probabilistic Models (
• (00:28:28) - ChatGPT: What Can ChatGPT Do?
• (00:31:00) - What to Watch Out For With ChatGPT
• (00:36:13) - A Brief History of Chat GPT

In this episode I dig into the environmental impact of using ChatGPT: energy use, water consumption, and carbon emissions. I walk through the research, acknowledge the risk of whataboutism, and explain why comparisons to other systems matter when we're talking about true impact. Adopting a both/and approach, this episode is absolutely not about letting ChatGPT off the hook, it’s about putting things in perspective so we can take action on what will actually move the needle.

• Why this is episode 2 and why the topic matters
• Ramit Sethi’s $30K vs $3-question analogy
• The math behind ChatGPT’s energy use
• The origin (and problems) with the “3Wh per query” stat
• What .3Wh per query actually looks like in real-world use
• The difference between inference and training impact
• How ChatGPT kicked off the AI arms race
• Where most AI energy actually goes (spoiler, it's not ChatGPT)
• The hidden energy behind everything else we use
• How water usage is measured across three scopes
• ChatGPT’s water use vs typical U.S. electricity and food systems
• Why carbon emissions highlight the need for more transparency
• The repeating citations problem in media coverage
• What actually makes the biggest environmental difference
• Concerns about future energy use from agentic models
• Why transparency from companies matters
• This episode as a call for participatory awareness, not panic

• Check out the full environmental impact resources list on my website
• Subscribe to the ChatGPT Curious Newsletter
• Submit a Question
• Visit the Website
• Feeling curious AND generous? Click here to support the podcast.

• (00:00:00) - ChatGPT Curious: The Files in the Computer
• (00:00:55) - ChatGPT & the Environment
• (00:04:12) - Sam Altman's Cheat Code for ChatGPT
• (00:05:29) - How a Google Search Uses 10 Times More Energy than a Standard Search
• (00:14:05) - ChatGPT Uses 3 Watt Hours of Energy
• (00:20:57) - Netflix's 150 Million Daily Users
• (00:21:39) - The Impact of Inference on the Environment
• (00:26:25) - Energy and Water Footprint
• (00:30:21) - How Much Water Does ChatGPT Actually Use?
• (00:36:46) - How AI Could Use More Water Than Us!
• (00:42:50) - The Biggest Dial Mover
• (00:44:01) - Carbon and Energy
• (00:46:30) - Searching for Literature in the Dark
• (00:49:44) - Don't Waste Your Energy on ChatGPT
• (00:53:01) - A Short Episodes on the Environment
• (00:54:14) - How I Use ChatGPT
• (00:56:27) - ChatGPT's Impact on the Environment