Simor Consulting
Category: Thought Leadership
A data engineering team I worked with had adopted six AI-powered tools in twelve months. An automated code reviewer, a data quality scanner, a pipeline orchestrator with intelligent retry, a natural l
OpenAI shipped GPT-5. Anthropic shipped Claude 4. Google shipped Gemini Ultra 2. Within six weeks of each other, the three leading model providers released frontier models that are, by most benchmarks
A hiring manager at a large tech company told me they had four hundred engineers working on their AI platform and zero people with training in philosophy, ethics, or the social sciences. When I asked
The legal system has not caught up with the practice of training AI models on copyrighted data, and the people building AI systems are not waiting for it. Models trained on books, articles, code repos
The phrase "data-driven culture" has been emptied of meaning by overuse. It appears in every strategy deck, every job posting, every conference talk. Everyone claims to want it. Almost no one can desc
In 2009, deploying software to production was an event. It involved a change request, a maintenance window, a runbook, and a prayer. Developers wrote code, then threw it over the wall to operations, w
I was in a strategy session where a VP of Data told the room that generative AI would "eliminate the need for data analysts within two years." The room nodded. Budget was reallocated. Three analyst po
The CTO of a mid-size financial services firm told me they had spent $4 million on AI tooling in eighteen months. They had three large language model providers under contract, a vector database cluste
A technology company built an impressive AI platform. They had GPU clusters, fine-tuning pipelines, evaluation frameworks, and a growing model registry. They opened access to any team that wanted to u
A financial services firm spent eight months building an AI-powered document analysis system. When it came time to deploy, they discovered their retrieval system had no governance layer, their agent h
2025 was the year AI moved from experimentation to industrialization. While 2024 saw the explosion of generative AI capabilities, 2025 was about making those capabilities production-ready, cost-effect