MBA Training AI

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MBA Training AI. The podcast for everyone learning to use AI well at work. Foundations of LLMs, prompting, AI agents, responsible AI, and getting the most out of ChatGPT, Claude and Gemini. New episodes every week, transcripts at mba-training.com.

  1. 5d ago

    Enterprise AI chat logs: how Dartmouth's provost was caught

    Dartmouth provost Santiago Schnell resigned after a faculty committee reviewed his enterprise Claude logs and emails and found undisclosed AI use in published work, including a Washington Post column and a ChemSystemsChem paper. The committee did not rely on Pangram or other AI detectors. The episode argues that the bigger story is the record your work AI account keeps, and debates whether patchy disclosure rules share the blame. You come away knowing that Copilot, ChatGPT Enterprise and Claude prompts sit on employer servers, why logs are stronger evidence than detectors, and why you should check each publisher's AI disclosure rules before you submit. 0:00 Provost asks Claude about AI signatures 1:17 Washington Post column flagged by Pangram 2:16 Enterprise Claude logs become the evidence 4:16 Murky AI disclosure rules across publishers 6:36 Chat logs versus AI detectors as proof 7:56 Publisher standards and employer log policies

  2. Oct 23

    PHP2Shell vulnerability: AI was fast, humans were the bottleneck

    Did AI really find the PHP flaws that took the French Interior Ministry, the Port of Rotterdam and Australia's cyber agency offline? The episode argues the bug had been spotted years earlier and parked because nobody could prove it was exploitable. Frontier models made that proof cheap, turning a memory bug into remote code execution. Then the humans slowed things down: a leaked embargo, a deleted Calif exploit video, and Chainguard publishing patches early. Listeners walk away able to question their own "probably fine" backlog, see why a public fix hands attackers a map, check for end-of-life versions that Censys found on over half of PHP servers, and re-rate risk register items marked "unlikely" only because proof was expensive. 0:00 PHP flaws take major websites offline 1:00 Bug spotted years ago, then parked 2:08 Chainguard finds the flaw with one prompt 2:51 How the PHP embargo leaked 5:32 End-of-life PHP servers left unpatched 6:42 Repricing parked risks on the register

  3. Oct 21

    Zillow iBuying loss: a 6.9% Zestimate error versus thin margins

    Why did Zillow Offers lose over $500M if the Zestimate was a decent model? The episode argues the AI did its job and the company changed the job. A 6.9% median error suits homeowner advice but exceeds an iBuyer margin of about six percent. The winner's curse meant Zillow bought the homes it overvalued, accuracy was measured on the wrong homes and timeframe, and Project Ketchup added overlays while telling pricing experts to stop questioning the algorithm. Listeners leave able to apply a napkin rule: error on deals you win plus price drift while holding must be smaller than your margin, or the model only advises. The episode compares Opendoor and Redfin, and covers the interagency AVM rule and spending caps on AI agents. 0:00 Zestimate as advice versus cash bid 1:09 6.9% error versus iBuyer margins 2:20 Winner's curse in algorithmic home buying 4:23 Project Ketchup and silenced pricing experts 6:06 Napkin rule: error versus margin 8:04 AVM rules and AI purchasing agents

  4. Oct 14

    DeepMind AI code review: write prompts with a one-sentence check

    AI writes roughly three quarters of Google DeepMind's production code, yet only about one change in eight is fully handed to the AI. What does that mean for how you prompt? The episode argues that reliability comes from scoping and review, not from the model or magic phrases, and that delegation works best on well-defined, verifiable tasks. You will be able to write a one-sentence check before writing a prompt, split vague tasks, and use Google's Gemini Argon tips such as grill-me questions and AGENTS file retrospectives. You also get Wharton findings on threats, tips and chain-of-thought, a 20-input test set method, few-shot prompting, prompt caching, and Asana's measurable-target example. 0:00 DeepMind's AI code and human review 1:32 Why verifiable tasks get delegated most 4:23 Write the check before the prompt 5:54 Google's Gemini Argon prompting tips 7:21 Prompt folklore: threats, tips, step by step 8:55 Checkable prompts for memos and forecasts

  5. Oct 13

    Character.AI bankruptcy: what AI vendor failure means for users

    What happens to users, data and contracts when an AI vendor goes under? Character.AI's Chapter 11 filing in Delaware gives a live answer. Disney is the stalking horse bidder at $20 million in a Section 363 sale, two years after Google's $2.7 billion license-and-hire deal took the founders. The hosts debate whether lawsuits or the Google deal killed the company, and note that its 160 million users are not treated as creditors. You will learn why wrapper apps carry more risk than OpenAI, Anthropic or Google, how to find the change of control clause in an AI vendor contract, and why to export prompts, custom assistants and chat logs to storage you control this week. 0:00 Character.AI Chapter 11 and Disney's bid 1:55 Google's $2.7 billion license-and-hire deal 3:09 Lawsuits, regulators and who killed Character.AI 5:35 Why users are not creditors 6:22 AI vendor risk for business buyers 8:04 Disney's bid and change of control clauses

  6. Oct 9

    GPT-6 Intelligent UI: why ChatGPT calculators need auditing

    OpenAI rolled out GPT-6 to all ChatGPT tiers with Intelligent UI, which answers with charts, forms, buttons and small working calculators. Is it a real upgrade? Independent Artificial Analysis scores show GPT-6 Sol and Luna about level with GPT-5.6 at half the cost, with a lower hallucination rate but more non-answers. The episode argues the polished tools can look more trustworthy than they are. You will be able to read the 44% time to first token claim correctly, weigh vendor benchmarks against independent ones, compare Luna with Anthropic's Claude Haiku 5.5 pricing and its 100,000 token threshold, check Enterprise admin settings, and rebuild a ChatGPT calculator in your own spreadsheet to verify its formula and inputs. 0:00 GPT-6 Sol and Luna rollout by tier 1:08 Why interactive calculators are harder to audit 2:30 Time to first token speed claims 3:25 Artificial Analysis benchmarks and hallucination rate 5:59 Claude Haiku 5.5 pricing versus Luna 7:12 Interface hype and a spreadsheet audit

  7. Oct 8

    Fintech AI timelines: the evidence clock beats the build clock

    How long does it really take to put an AI agent live in regulated finance? Vendors such as Gradient Labs quote days, and Nous Research just raised $90 million for Hermes for Businesses. The episode argues the build was never the delay. The real timeline is the evidence clock: the time needed to observe enough rare worst-case outcomes, with SR 26-2 excluding agentic AI and EU AI Act credit scoring rules arriving December 2027. Listeners learn to separate value metrics from guardrail metrics, apply the rule of three to size a shadow mode run, assign guardrail ownership outside the project team, read the Klarna case, and cross out go-live dates that ignore base rates. 0:00 Nous Research funding and agent hype 2:06 SR 26-2 and EU AI Act deadlines 3:43 Guardrail metrics and Klarna's lesson 5:13 Rule of three shadow mode math 6:35 FCA testing and vendor go-live claims 8:25 Guardrail swaps and pilot traps

  8. Oct 6

    ChatGPT Ads: the measurement stack behind image ads

    Did OpenAI launch ads inside ChatGPT image generation, or announce a test? The episode separates the two, then argues the ad format is the smaller story. The measurement stack shipped alongside it, attribution partners like AppsFlyer, Triple Whale, Adjust, Branch and Northbeam, plus geo-based incrementality work with Haus, Measured and WorkMagic, is what turns a placement into an advertising business. You come away able to read partner-reported results with the right scepticism, including WorkMagic's Dose study and Triple Whale's Portland Leather figure, understand view-through conversions and why 52.7% landing inside an hour matters, and judge the DoubleVerify and Integral Ad Science pilots that exclude user conversations. Practical steps: check the partner list against your analytics stack and write your own evidence standard. 0:00 What OpenAI actually announced on October 5 0:46 Attribution and incrementality behind the ad unit 1:48 Partner-reported lift numbers and view-through conversions 3:17 Can anyone audit the ads-don't-influence-answers claim 4:45 The case for ads and the inventory shortage 6:46 What enterprise AI teams should do this week

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About

MBA Training AI. The podcast for everyone learning to use AI well at work. Foundations of LLMs, prompting, AI agents, responsible AI, and getting the most out of ChatGPT, Claude and Gemini. New episodes every week, transcripts at mba-training.com.

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  • Channel
    Leaders Insights
  • Creator
    MBA Training
  • Years Active
    2026
  • Episodes
    88
  • Rating
    Clean
  • Copyright
    © 2026 MBA Training, mba-training.com
  • Show Website
    MBA Training AI

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