AI automation is about more than cutting costs. It's about freeing up humans to do the things that grow revenue.

To work really well, many AI systems and AI agents will need some fine-tuning (i.e., training). Fine tuning requires data examples that show the system what "good" looks like. That is, getting AI to work well on narrow tasks will be a data problem.

A lot of companies are tinkering with AI right now. But the the best companies will be full of people who think like AI Engineers, rather than AI Hackers.

A lot of entrepreneurs are hacking together AI systems. ... but failing to think about failure modes, edge cases, and operational guardrails. If you want AI to really work in your business, you need to start thinking like an AI Engineer.

5 Ways to Use AI to Increase Revenue

RAG can turn your past plans, processes, policies, and playbooks into assets that power AI agents.

The best entrepreneurs of the next decade will build huge companies with a small number of employees. To do this, they will need to become more like AI Engineers.

Part of the problem with vibe coding is that as complexity increases, the data set on which the model was trained becomes thinner and thiner. As you try to do something special, unusual, or unique, the model has less and less experience and will make more and more mistakes.

Moreover, because of the way LLMs work (probabilistic next token generation), they will tell you to do incorrect things quite confidently (i.e., they hallucinate).

The deeper you get into a complex project, an LLM starts bringing you into bad or useless rabbit holes. If you lack experience, you'll dive right in and waste a ton of time/money before you ever realize that the AI system was mis-guiding you.

this is the same story with ai. the killer apps will be the ones that collapse latency, friction, & context switching into something instant.