Blog
The familiar case for an EU private cloud, in contrast to a US-based hyperscaler, is jurisdictional, and it is a very strong one: Under the CLOUD Act, a US-based provider can be legally compelled to deliver the data whatever its...
Just because something works when you put it live doesn’t mean it stays true. The world that the model learned has evolved and the only question is whether you spot it before your customers do. All deployed models are based...
The AI development industry is now so saturated with vendors that buyers no longer have time to vet everyone. The same capabilities are offered by all firms, and the lists ranking them are almost exclusively written by those same firms....
A big model takes dozens or hundreds of GPUs to train, and they have to remain perfectly synced as they work. At the end of every training iteration, they have to pass gradients and parameters between one another, meaning that...
Picking an enterprise AI agent platform isn’t a fresh dilemma, but the optimal choice in 2026 is now distinct from what it was a year back. The space has transitioned from a mess of clashing frameworks to a collection of...
Most AI projects do not fail on the model; rather, AI failures occur because of the journey to production, getting from a model that can work in an environment as simple as a notebook to a system that can run...
The argument for sovereign AI usually gets framed as a values question: do you trust European law more than American law to govern your data? That framing is comfortable and largely beside the point. Can you produce the documentation and...
A model registry that nobody updates is just a database. The vendors have already built the governance tooling; the hard part is making it produce a defensible answer when someone with authority asks a pointed question. AI model lifecycle management...
Fine-tuning has a reputation problem. Some see it as a magic wand, sure that feeding a model some company data will make it understand the domain. Others treat any change to the weights as a relic of the pre-prompting era. ...
Choosing the right GPU for model training is rarely as simple as picking the newest card. The decision shapes how fast a model trains and how quickly a team moves from experiments into a system it can run in production. ...
AI agents have moved from demos into everyday operations. Teams now expect software that can take an instruction, plan the work, pull from internal systems and finish the task without a person babysitting each step. A single capable agent is...
Generative AI is no longer sitting at the edges of business operations. Enterprises are deploying large language models and multimodal AI systems to handle decision-making and automation at scale, and as adoption moves from pilot to production, a consistent pattern...
