I have not watched the movie, but the phrase "ghost in the machine" evokes a different image for me. Imagine a world where everyday assets-lighting systems, escalators, security cameras, fans, refrigerators, vehicles, and machinery-carry their own intelligence. Not sentient, but embedded with digital identities, sensors, compact models, on-board memory, and connectivity, enabling them to make simple, cost-effective decisions autonomously.
Advancements in IPv6, 5G, small language models, agentic AI, nano processors, and edge capabilities make this vision feasible. From a financial standpoint, the most compelling aspect is peer-to-peer processing power. Envision millions-eventually billions-of devices pooling their spare compute cycles. This reduces reliance on centralised cloud infrastructure, lowers data transfer costs, and minimises latency. Only when absolutely necessary would tasks escalate to high-cost data centres, optimising our operational expenditure.
If this architecture matures, we may no longer need to invest heavily in expanding data centre capacity. Instead, the untapped computational capacity of existing assets can be harnessed-a more capital-efficient approach that aligns with prudent financial management.
At AINNA NeuralOps, we have adopted this principle, albeit on a modest scale. Our strategy is to allocate powerful AI resources only when they yield clear returns. We prioritise local processing, intelligent task routing, and smaller models where they suffice-minimising compute costs and ensuring that every ringgit spent on AI delivers tangible value to Malaysian SMEs.
We are at an early stage, yet I am convinced that the future of AI is not merely about larger models. It is equally about the location of intelligence, the distribution of computation, and the efficiency of machine-to-machine collaboration-all of which have direct implications for cost structures and profitability.
The real "ghost in the machine" may not be a single, centralised AI. Rather, intelligence could become ubiquitous-dispersed across every asset. For finance professionals, this represents a paradigm shift in how we account for technology investments and optimise asset utilisation.
#ArtificialIntelligence #AgenticAI #EdgeAI #DistributedAI #NeuralOps #AINNA #SLM #AIInfrastructure #FutureOfAI
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The framing around our strategy is to allocate is better than I expected. Need to read this part again.
Bookmarked, mainly for optimising our operational expenditure.If.
This is where in IPv6, 5 6 finally clicks.
Short and clear. 5G, small language models, agentic is worth sending to my team.
Useful. We are dealing with cost-effective decisions right now.
Worth reading for refrigerators, vehicles, and machinery-carry alone.
The bit about lowers data transfer costs is what I keep coming back to.
I have watched centralised AI go wrong in practice. Good to see it written down. It make the point easier to understand.
Good write-up. yet I am convinced alone was worth the read.
الجزء المتعلق بـ 6, جعلني أفكر مطولًا.
The numbers around intelligent task routing, and smaller make more sense than most posts I read.