A video interview on how enterprises scaling AI deployments are straining traditional cloud infrastructure, and why an "AI Grid" model — distributing AI inference across edge locations rather than concentrating it in a handful of facilities — changes the economics of running AI in production for UK and international organisations.
#inference
11 items
A feature on how generative AI is redrawing data centre design — from storage-led optimisation to power-hungry GPU compute and high-bandwidth interconnect — and the energy, permitting, and supply-chain constraints that come with it. John Bradshaw is quoted on why retrieval-augmented generation carries most enterprise workloads: cheaper, faster to deploy, no scarce ML engineering talent required, and proprietary data never touches a training pipeline.
Short session examining how organisations are reconsidering AI deployment locations as the technology accelerates, exploring how edge-based AI is transforming cloud design, application performance, and data governance.
John Bradshaw discusses whether AI's competitive advantage in 2026 lies in model size or infrastructure placement, and how financial scrutiny is refining rather than slowing adoption, pushing intelligence toward edge infrastructure for improved performance and cost efficiency.
Video companion to the Tech Talks Daily podcast episode. John Bradshaw examines where AI inference should live in 2026, exploring the shift from centralised cloud training to distributed edge inference.
On-demand session with Computer Weekly's Bryan Glick examining the shift from AI experimentation to practical deployment in 2026, covering four key predictions shaping cloud and AI's future, balancing innovation with measurable business value.
Featured in the inaugural issue of SDxCentral Magazine, covering AI inferencing, edge computing, and the evolving cloud landscape in 2026.
DCD's Edge Computing broadcast channel is renamed to reflect the convergence of edge computing and AI inference, signalling a broader industry shift toward distributed intelligence at the network edge.
John Bradshaw, Field CTO Cloud EMEA at Akamai, explains why 2026 is shaping up to be the breakout year for AI inferencing — the process of putting trained models to work — and why the market remains wide open for challengers to hyperscaler dominance.
John Bradshaw argues the economics of hyperscale AI models don't stack up for most enterprise use cases, and makes the case for domain-specific and small language models running closer to users — trading raw parameter count for cost efficiency, latency, and fit-for-purpose outcomes.
On-demand session moderated by Computer Weekly's Bryan Glick examining how Akamai Inference Cloud redefines AI infrastructure, covering AI adoption trends, the emergence of AI agents, and practical strategies for accelerating AI transformation while addressing skills gaps.