AI isn’t “going to the cloud” the way the headlines promised. It’s going wherever the economics and the architecture force it to go, and that often means back on hardware you control. In this episode of #F5'sPop Goes the Stack, Lori MacVittie talks with longtime cloud strategist David Linthicum about why AI workloads are driving a very familiar shift: from breathless outsourcing narratives to a sober “where does this bring the most business value” decision. David argues that the right placement question is not ideological, it’s operational. Public cloud #LLMs bring ecosystem convenience, but GPU-as-a-service costs can be multiples higher than running inference on your own equipment, even after factoring in colocation, managed services, leasing, and support. That’s colliding with token shock: organizations build agentic prototypes expecting small bills and then get six-figure invoices because demand and context usage are hard to forecast. The discussion also highlights a second trap: lock-in. Using a simple API can make switching models easier, but agents often pull teams into full frameworks and ecosystems that are harder to unwind later. And the technology isn’t standing still; today’s transformer-era models aren’t the final generation, so tying your long-term processes to a single provider can turn into expensive technical debt. The practical message is blunt: stop overbuilding. Most successful AI applications in enterprises will be narrow, tactical, and “minimum viable” in their use of AI. Sometimes that’s a small model on-prem. Sometimes it’s classic ML. Sometimes it’s a frontier model in the cloud. The win is choosing the smallest effective solution, in the right location, at a cost your business can sustain. If you’re planning AI infrastructure, this episode is a reality check: best-of-breed, hybrid placement is back, and the companies that treat AI spend like a business decision will outlast the ones treating it like a hype contest. Chapters: 00:00 Welcome to Pop Goes the Stack 00:32 AI isn’t “cloud-only”: On‑prem GPUs are booming 01:20 David Linthicum’s take: “I predicted repatriation” 02:10 Decision framework: Place workloads where value is highest 03:23 The cost of AI: On-prem + maintenance or cloud expense + token shock 06:58 Hybrid reality: Optimizing with mix cloud, neocloud, on‑prem, edge 08:18 The trap: LLM pricing will rise (loss leaders won’t last) 09:41 Agents = deeper lock‑in than simple OpenAI‑style APIs 11:56 Minimum viable AI: Narrow use cases beat “boil the ocean” 13:08 Use the smallest tech that solves the problem (sometimes ML) 15:30 Shadow IT + on‑prem: How teams are adapting in practice 17:19 Hard truth: Big models add little value for most business tasks 19:55 Managing AI complexity and heterogeneity 21:21 Key takeaway: Right tool for the right job in the right place Learn how you can stay ahead of the curve and keep your stack whole with additional insights on app security, multicloud, #AI, and emerging tech: https://go.f5.net/ncohfez3 More about F5: https://go.f5.net/t8iaxbzn Read our blog: https://go.f5.net/zn40tz7f Follow us on LinkedIn: https://go.f5.net/tz3n1lr8

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