{"id":7072947,"date":"2026-10-02T13:45:35","date_gmt":"2026-10-02T13:45:35","guid":{"rendered":"https:\/\/fivemor.com\/?p=7072947"},"modified":"2026-10-02T13:45:35","modified_gmt":"2026-10-02T13:45:35","slug":"the-full-enterprise-ai-stack","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=7072947","title":{"rendered":"The Full Enterprise AI Stack"},"content":{"rendered":"


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This is a resource that any CTO should read. Why? <\/p>\n

For almost four years, much of the AI conversation has centered on <\/span>how intelligence gets built<\/strong>. Pretraining gave models broad capabilities by learning from enormous datasets. Post-training shaped those capabilities into more useful behavior. Reinforcement learning from human feedback, or <\/span>RLHF<\/strong>, helped turn language models into assistants that could follow instructions and interact with people naturally. These were not separate technologies replacing one another, but successive layers of improvement around the model. <\/span><\/p>\n

We have now entered what I would call <\/span>the inference phase of the AI Supercycle<\/strong>. Not because training has stopped mattering, but because the economic question is expanding from <\/span>How do we build a capable model?<\/em> to <\/span>How do we operate that capability repeatedly, across millions of tasks, at a cost the customer can justify?<\/strong> Reasoning models make this transition particularly important: additional computation during inference can itself improve performance. The model\u2019s capabilities depend not only on how it was trained, but also on how much work the system performs when answering. <\/span><\/p>\n

My expectation is that <\/span>over the next three to five years, production inference could become a larger spending pool than pretraining alone<\/strong>, with enterprise deployment an important driver. Evidence already points in that direction within a defined market: Gartner forecasts inference to account for <\/span>55% of AI-optimized cloud infrastructure spending in 2026 and 59% in 2027<\/strong>, exceeding training spending in that segment. This isn’t an industry-wide accounting of every AI investment, but it shows why inference deserves to move from a technical consideration to the center of enterprise strategy (see <\/span>Gartner<\/a>). <\/span><\/p>\n

The mechanism is straightforward. Training costs recur as models are developed and improved. <\/span>Inference costs recur whenever those models are used.<\/strong> And an agentic workflow can require much more than a single answer. Imagine a system investigating a customer issue: it reads the account history, checks a contract, chooses a tool, processes the result, revises its plan, and prepares an action. Each model call adds work. Multiply that pattern across departments, customers, and continuous operations, and serving intelligence becomes a substantial production system. <\/span><\/p>\n

But <\/span>inference is not one uniform workload<\/strong>, and that is where the nuances become fundamental. In a typical autoregressive language model, two phases do different jobs:<\/span><\/p>\n