Laguna S runs 118 billion total parameters with only 8 billion active, and went from the start of training to launch in eight weeks. Sparse activation plus a fast pipeline, rather than brute size.
Kant's claim is that model building is 90% engineering, and that roughly 95% of the wins reduce to two levers: better data, or better compute efficiency.
For AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → work, persistence, verification and backtracking may matter more than raw intelligence. A model that keeps checking and retrying beats a smarter one that commits to a wrong path.
He calls MCPThe Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.Full definition → and predefined tool lists a dead end. Rather than picking from dozens of declared tools, an agent should write a script and run it in a container, with the agent harnessThe scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.Full definition → kept minimal.
You rarely need a full run to judge a change: a new checkpoint is often evaluable within its first 30 minutes, which is what makes running experiments at high throughput affordable.
Terms in this piece · Glossary
streaming — Sending a model's response token by token as it is generated, so the reader sees text immediately instead of waiting for the whole answer.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
MCP — The Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
Why it matters
Explains how Poolside operationalizes frontier code-model training at scale — reproducible experimentation, data streamingSending a model's response token by token as it is generated, so the reader sees text immediately instead of waiting for the whole answer.Full definition → into training, and low-precision compute — giving engineers a concrete picture of what a modern model-training pipeline looks like end to end.
Key quotes
“I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five.”
Eiso Kant
“model building is ultimately 90% engineering.”
Eiso Kant
“I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.”
Eiso Kant
“You're improving data or you're improving compute efficiency.”
