Cut One AI Cost and Two More Grow Back

What does AI really cost once thousands of people inside a company start using it? Ioannis Zempekakis, Director of AI at Prosus, has been running agents in production since 2024. We put that question to him on a tandem bike ride from a favorite Amsterdam cafe to Vondelpark.

Ioannis explains the decision to stop relying purely on cloud APIs, and why that approach breaks down at the scale of a large company. He covers why agent harnesses and sandboxes are as greedy as software gets, and why agents scheduled to run every five minutes repeat a lesson the data world already learned about freshness. He also explains how the habit of throwing away a codebase and prompting again quietly adds up. His bigger point is that even if model progress froze tomorrow, there would still be about two years of value left to squeeze out. The cheapest wins come from controlling context and running a basic model router, while ten new features a month keep sprouting like Hydra heads.

From there we get into when fine-tuning small models pays off for repetitive work like data analytics, and the eval suites that come as a byproduct of building in-house. Ioannis explains how owning the stack let Prosus grow a GPT-3 Slack bot into a full agent harness. He also explains why committing to a single vendor or open source project is a bigger bet than it looks, and how to balance top-down priorities with bottom-up building across the company. We close on hiring. Ioannis looks for system design over syntax, a nose for the over-engineered code AI models love to produce, and enough agent intuition to know when to build something yourself and when to let the agent decide.

Demetrios Brinkmann: https://www.linkedin.com/in/dpbrinkm

Ioannis Zempekakis: https://www.linkedin.com/in/zempekakis

Timestamps:

[0:00] AI total cost of ownership on a bike

[0:48] The million dollar decision

[4:26] When everyone can build with AI

[6:51] Which use cases are worth the tokens

[10:46] Benchmarking every model on every task

[12:18] Total cost of ownership and AI ROI

[15:12] Context control and model routing

[16:39] Training and fine-tuning your own models

[19:41] Is the juice worth the squeeze

[25:26] Optionality and vendor lock-in

[27:30] From Slack bot to agent harness

[30:12] Top-down vs bottom-up AI adoption

[32:19] What AI engineer even means

[35:13] What knowing AI really means

[37:47] The hardest part of hiring