When does a company actually decide to hire an ML engineer instead of just using

<!-- SC_OFF --><div class="md"><p>I’m curious how companies make the tradeoff between building their own AI models vs just using APIs like OpenAI or Anthropic. </p> <p>Is it

<!-- SC_OFF --><div class="md"><p>Just went through a Stanford paper that tracked 51 actual AI deployments - not surveys or sentiment polls, real production systems across 41

<!-- SC_OFF --><div class="md"><p>Been noticing that once you use LLMs for real dev work, the cost conversation gets messy fast. It is not just raw API spend. It is retries, long

<!-- SC_OFF --><div class="md"><p>I'll be upfront about my endgame: I work at a large tech company, I have a niche picked out, and I'm making the move to build and sell AI agents

<!-- SC_OFF --><div class="md"><p>I've been using AI extensively for ML research, and I've noticed a bottleneck: brainstorming research directions and generating smoke run

<!-- SC_OFF --><div class="md"><p>Agents are amazing. Harnesses are cool. But the fundamental role of a data scientist is not to use a generalist model in an existing workflow;

<!-- SC_OFF --><div class="md"><p>Everyone keeps saying the future is using high-capacity frontier models to systematically train and distill more efficient, low-cost models. And

<!-- SC_OFF --><div class="md"><p><a href="https://youtu.be/vczBo0AvbTI?si=pglMPmTjsq-TNJa9&amp;t=375">https://youtu.be/vczBo0AvbTI?si=pglMPmTjsq-TNJa9&amp;t=375</a></p>