There is no such thing as removing bias from generative AI completely. None. Zero. Anyone making that claim is lying, period, end of story. Here’s why. A lot of people – with good intentions, and with good effect – can REDUCE bias by giving today’s language models clear guidelines about what constitutes inclusive language versus…
One of the best things you can do in enterprise AI is set up testbeds to count tokens. Here’s why. Claude’s enterprise pricing is this: Opus 4.8: $5/million tokens in, $25/million tokens out Sonnet 4.6: $3/million tokens in, $15/million tokens out Microsoft’s pricing is based on Copilot Credits; 1 credit is one penny, and they…
PSA: Agents do stuff for you. In all the buzz about AI agents, very few people are using the term for what it means. Agents in computing are not new. We’ve had agents of some kind since 1963. Every time you take your phone and connect to a new wifi point, a slew of different…
Summary In today's episode, I explain why enterprise AI agents are failing observability audits and how to fix it by treating them as ordinary software. Here's what this means for you. You'll learn to reuse the monitoring and logging infrastructure you already own to build verifiable audit trails for regulated industries. You'll also learn these…
Use the right model for the right task to save token budget. Planning? Use the biggest, smartest models. Execution? Use a midrange model that can follow directions well. Reading and summarization? Use a light, fast, dumb model that does well when it’s given all the data. If you leave your favorite AI tool set on…
Summary In today's episode, I explain why hyperscalers are hoarding gigawatt-scale power capacity and what that power grab means for your enterprise AI costs. Here's what this means for you. You can escape rising token prices and cloud dependency by combining on-premise AI hardware with your own distributed green power generation. You'll also learn these…
Plan big, act small. With Anthropic’s latest model release, the fabulously expensive Fable 5, it’s a timely reminder that when you have a big, expensive model, you should use it to do planning, and then hand off the execution to a small, fast, cheap model that doesn’t have to think, it just has to follow…
