The Cost of Being Right Yesterday
Field Notes from The Build – No. 1 || Strategy from the drafting table. Lessons from the keyboard.
Nine months ago, when I began building Artwell in earnest, Deepgram was the right call for speech-to-text. Best quality at the time. Best pricing. Solid intelligence layer.
I also flagged, in the first week, that Artwell should not be permanently coupled to a single transcription provider. That was not some brilliant revelation. Every senior architect would have seen the same thing.
But there is a difference between seeing an architectural need and building it immediately. Early in the build, the more important job was learning the shape of the product: the workflow, the transcript model, the editing layer, the downstream story outputs, the places where quality actually mattered. Premature abstraction is its own kind of debt. So we kept moving.
This week I went back and re-tested the speech-to-text market against Artwell’s real needs – long-form audio, diarization, speaker handling, intelligence layers, downstream editing, cost at scale. The landscape had not moved by inches. It had moved by miles.
ElevenLabs’ latest Scribe v2 has overtaken on quality and pricing. AssemblyAI is right behind it, with a comparable intelligence layer – sentiment, topics, summaries, speaker identification inferred from transcript context. For the configuration Artwell needs, the incumbent provider now costs more than double what the leaders charge, with measurably higher word error rates (WER) and a thinner intelligence layer. This is no small thing. This changes margins, pricing flexibility, and what a product can afford to offer users.
With Artwell’s launch on the horizon, this became the moment to build the abstraction layer.
Artwell now has a transcription layer that can route across providers, support fallbacks, separate development from production, and absorb the next provider change without touching the product itself. The intelligence layer is next, same pattern – OpenAI and Anthropic today, whatever proves best tomorrow.
The lesson though is not “build abstraction layers.” The lesson is that AI infrastructure decisions have a shorter shelf life than most teams are conditioned for. The right provider today may not be the right provider one year from now. It might not even be the right provider one month from now. That’s simply the speed of the market we are all building in.
“Which provider is best” is the question every team asks. The better question to ask is what it will cost you to switch when that answer changes.
Architecture buys you the right to change your mind.
And in AI, that optionality may be the difference between building a moat and building a wrapper.
𝘛𝘩𝘢𝘵 𝘪𝘴 𝘸𝘩𝘢𝘵 𝘈𝘐 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘭𝘰𝘰𝘬𝘴 𝘭𝘪𝘬𝘦 𝘧𝘳𝘰𝘮 𝘛𝘩𝘦 𝘉𝘶𝘪𝘭𝘥.
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