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A journalist screams, “I’m wasting four hours a day transcribing,” and a product is born. We sit down with Lasse Finderup, CEO of Good Tape to unpack how a newsroom pain point turned into a privacy-first platform used by millions—and why saying no to feature bloat matters more than chasing every shiny AI trick.
We trace the spin-out origin story and the “instant product-market fit” that came from building for colleagues who needed reliable, fast transcripts yesterday. Lasse explains the decision to never train on user data and to host models in-house, trading flashy add-ons for deep security, ISO-grade compliance, and trust. We explore global AI adoption gaps—from Denmark’s “I’ll just ChatGPT this” culture to regions where automated speech-to-text still feels like magic—and why context matters when you’re designing tools for journalists, podcasters, and creators handling sensitive sources.
From a tech perspective, we dive into an open-source stack centered on Whisper V3 Large and the heavy lifting around the model: optimization, infrastructure, and the real costs of self-hosting LLMs. Lasse lays out a sharp distinction between “record-everything meetings” tools and workflows where the transcript is the output itself. That sets the stage for Good Tape’s next big leap: an “artificial memory” that surfaces relevant past notes at the right moment, with user-controlled reminders that feel helpful, not invasive. We also touch on multilingual transcription’s surge across contact centers and newsrooms, market consolidation on the horizon, and founder advice: build for real needs, not just because AI makes it possible.
If you care about accuracy, confidentiality, and simple tools that get out of your way, this conversation will sharpen how you evaluate transcription tech and where the industry is heading. Subscribe, share with a teammate who fights transcripts, and leave a quick review to help more builders and storytellers find the show.

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