Over the past year, discussions about AI progress have centred largely on model scaling – parameter counts, training compute, and benchmark performance. But organisations deploying AI at scale must urgently wake up to a new set of priorities.
This was made especially clear at Crusoe’s Next Gen AI Tech Talk in London this week. The observations on AI in deployment reinforced what Stelia has long been saying. Building secure, scalable and future-proofed architecture, with governance embedded from the ground up, must be at the forefront of AI system design as organisations move beyond pilot into full production. And this is starting to manifest more clearly within businesses as AI adoption advances.
Founders working across distinct domains – robotics, training automation, and agent orchestration – described market challenges that had less to do with model capability and more to do with the infrastructure required to make models reliable, safe and economically viable.
And while executives on the panel from Reimagine Robotics, Inephany, and StackOne are evaluating distinct challenges in the market, the technical bottlenecks they raised pointed toward a common theme: AI capabilities can only deliver genuine value when the infrastructure layers beneath them are robust, and responsibly governed at scale.
The pattern manifested across three distinct technical domains:
Robotics cannot scale without governed action layers, safe interfaces, and real-world telemetry. The data required to train embodied AI systems largely does not exist in publicly available datasets. It resides within operational environments: warehouses, manufacturing facilities, and industrial workflows. Accessing this data depends on deployment rather than acquisition. Deployment at scale, in turn, requires interface layers that allow non-expert operators to configure and control robotic systems safely. Without this foundational infrastructure, robotics deployment remains constrained to controlled, specialist environments.
Agent systems fail without orchestration, permissions, provenance, and policy-aware routing. The technical capability to build autonomous systems that interact with multiple APIs and data sources has matured considerably. What separates successful deployments from failed proofs-of-concept is the orchestration infrastructure required to make those interactions reliable and auditable in production settings. Agents represent a capability layer, but production viability requires underlying infrastructure that handles permissions, tracks provenance, and enforces policy constraints. In enterprise contexts where compliance and security are non-negotiable, this substrate cannot be an afterthought.
Training automation breaks without runtime observability, stable learning dynamics, and substrate-level optimisation. Traditional approaches to model optimisation involve running extensive hyperparameter searches, hundreds or thousands of discrete training runs to identify optimal configurations. This method does not scale effectively for modern large-scale models. More scalable approaches require systems capable of dynamic optimisation during training: monitoring runtime behaviour, adjusting parameters at granular levels, and maintaining learning stability without continuous manual intervention. Rather than model capacity, the restraint becomes the absence of adaptive training infrastructure.
What production demands
The challenges across these three domains exemplify a broader reality: successful AI adoption requires not only capable models but sophisticated systems that take into consideration the entire modern AI stack, and are designed to scale responsibly.
The characteristics of these systems increasingly align with architectural principles that treat governance, observability, and policy enforcement as foundational. Systems that demonstrate how compute translates into auditable, verifiable results, that enforce policy at runtime rather than relying on retrospective controls. They should optimise for cost, carbon footprint, and jurisdictional requirements together as architectural considerations. The goal lies in enabling scalable AI capabilities that enterprise can actually deploy to deliver genuine business outcomes.
The infrastructure constraints surfaced at Crusoe’s event reflect what founders building at the frontier are encountering in practice. And the gap between model capability and production deployment readiness is crystallising as architectural.
What defines the next decade
The next phase of AI development will be defined less by advances in model performance and more by advances in the systems that make those capabilities governable, observable, and economically sustainable at scale. While model capabilities will undoubtedly continue to improve, the organisations that embed these principles as architectural foundations will be better positioned to bridge the gap between technical potential and realised value.
