A newer way that I've thought about the phase of AI and LLMs that we're in: it's a race to turn probabilistic/stochastic ("random") processes into deterministic ("not random") ones.
- Easy example: adding two numbers together should use a calculator, not an LLM. Bad use case of AI.
- The company tracking suite I'm building out is built using the help of AI, but ultimately it's to build a strong database in the format I want, so that I can store companies, market maps, data, thoughts, etc. for the long-term. In my opinion, a good use of AI: turning a noisy, messy process (AI-driven coding) into something reliable (a deterministic database)
- LLM/agentic loops are all trying to build processes to reduce the error rate to close-enough-to-zero (e.g. double-checking/validation agents) or putting humans in the loop to bring it to "zero." The challenge is that you can reduce the probability of error close to zero, but can never quite hit it. I see this in the "document processing" realm -- we hope that the frontier models can read the PDF with 100% accuracy ... but for high stakes scenarios, would we trust it?
