Perspectives

Most enterprise engineering organizations are not deciding whether to adopt AI coding agents anymore. That decision is already behind them. What they are actually deciding, often without realizing it, is whether the velocity those agents produced in the first two quarters holds up once the codebase, the team count, and the audit scrutiny all grow at the same time. This piece walks through what has to be true for that velocity to hold, section by section, starting with where specification actually belongs in your delivery lifecycle.

An operating partner building an AI roadmap for private equity portfolio companies sits down with a fixed budget and five portfolio companies on the table. One is a healthcare services roll-up with three years of bootstrapped acquisitions and no unified reporting. One is a payments platform with a clean data stack and a product team already asking for a fraud-detection model. One just closed, and the new CTO is still mapping what exists. The mandate from the fund is the same for all five: build the AI story for the next board meeting.

Compare leading large language models to find the best fit for your business needs, from GPT-4 to Llama 2, and discover their unique strengths and use cases