70% of production AI teams use open source models. 72.5% connect agents to databases, not chat interfaces. This is what 375 technical builders actually ship - & it looks nothing like Twitter AI.
70% of teams use open source models in some capacity. 48% describe their strategy as mostly open. 22% commit to only open. Just 11% stay purely proprietary.
Ten months ago, DeepSeek collapsed AI training costs by 90% using distillation - transferring knowledge from larger models to smaller ones at a fraction of the cost.
Distillation works like a tutor training a student : a large model teaches a smaller one.1 As we’ve shifted from knowledge retrieval to agentic systems, we wondered if there was a parallel technique for tool calling.2
Could a large model teach a smaller one to call the right tools?
GPT-5 launched yesterday. 94.6% on AIME 2025. 74.9% on SWE-bench.
As we approach the upper bounds of these benchmarks, they die.
What makes GPT-5 and the next generation of models revolutionary isn’t their knowledge. It’s knowing how to act. For GPT-5 this happens at two levels. First, deciding which model to use. But second, and more importantly, through tool calling.
We’ve been living in an era where LLMs mastered knowledge retrieval & reassembly. Consumer search & coding, the initial killer applications, are fundamentally knowledge retrieval challenges. Both organize existing information in new ways.
The most successful AI startups of 2024 shared an unlikely secret: they didn't rely on proprietary datasets. Instead, they leveraged synthetic data to outmaneuver competitors who were still chasing exclusive data partnerships and expensive labeling operations.
The numbers tell a compelling story. Synthesis AI grew 410.6% last year while Datagen raised $72M – the largest funding round in the synthetic data space. Meanwhile, companies burning millions on human data labeling watched their unit economics deteriorate as synthetic alternatives delivered 500-1000x cost reductions.