How generative AI works as a “probability machine” Artificial intelligence, and particularly generative AI (GenAI), has captured the imagination of leaders everywhere. As we look for ways to embrace GenAI, it’s important to understand the fundamentals; the underlying technology and software principles powering generative AI don’t operate like traditional software. What do I mean by … Continued
As engineering consultants here at Axian, clients often ask us to parachute into mature products with years of development (and technical debt). Navigating these codebases is difficult and time-consuming. It is critical that we work with existing staff to help guide us through the process. However, as codebases mature and staff are asked to deliver … Continued
From undocumented AI usage to sanctioned innovation It’s no longer a question if, but how. You’ve likely seen it already: developers using various LLMs to debug complex code, accelerate feature development, and even draft technical specs. This is a testament to your team’s drive for innovation! But, you need to manage its usage to … Continued
Who gets your data when OpenAI goes bankrupt? As more of our clients look to leverage AI, I’m often asked: What happens to my data if an AI provider (say, OpenAI) goes out of business? Let’s unpack that beyond headlines and gossip. AI companies feed on personal and protected data We’re seeing “AI-as-a-Service” firms up ramp … Continued
In the age of AI, disagreement is a feature One reason I love my job is that my co-workers have no problem disagreeing with me. That may sound odd coming from a CTO. After all, shouldn’t leadership have authority when choosing direction, making decisive calls, and defining vision? Sure, but the best visions are forged … Continued
Like many of you, our engineering teams are navigating a challenging squeeze: tighter budgets, 10×-level performance mandates, and increasing expectations of AI-enhanced productivity. We’re exploring every possibility; augmenting workflows with GitHub Copilot, test generation tools, and using (limited) automated code reviews in efforts to make good on the acceleration hype. AI isn’t magic We are … Continued
Enterprise Model-as-a-Service (MaaS) AI companies such as Anthropic and OpenAI are facing rapid consolidations and disruption during this unprecedented high tech gold rush. Powered by debt, providers are sprinting to evolve model capabilities while also anticipating capacity. Finally, they have to figure out a fee structure that covers the costs. Flat fee subscription pricing (as … Continued
Local AI benefits are already extensive Local AI refers to open-source AI models that you can host on your own systems—enabling your creative and production teams without relying on large hosted models from OpenAI or Anthropic. Performance for open-source models like Llama, Qwen, and DeepSeek is often equivalent to publicly hosted AI services like ChatGPT … Continued
Premise Generative AI is a fantastic accelerant for software development velocity and product delivery. But it’s not magic. VPs and Engineering Directors are under pressure to deliver the same roadmap with dramatically less headcount. GenAI helps, but only when deployed alongside strong governance, automation, and experienced oversight. Without these, GenAI becomes less of a force … Continued
In the evolving landscape of web applications, the traditional request-response model—where a system processes a request immediately and replies with a result—can feel increasingly outdated. While intuitive, this synchronous approach ties up resources, creates bottlenecks, and limits scalability in distributed environments. An alternative approach embraces asynchronous workflows, where systems shift from immediate action to orchestrated … Continued
