Keynote: Welcome
summary
Conference opens by mapping where open source actually matters: infrastructure and models. Claims include $900B invested in 2026 and $1.4T capex in 2027 for hardware and data centers, PyTorch 85% adoption for AI models, Linux runs 90% of cloud workloads and virtually all AI training, and 20,000 MCP integrations registered for agent connectors. Projects named: React joins the Linux Foundation, Overture Maps pools Microsoft Meta TomTom Amazon geodata, an Agentic AI Foundation and a security effort called Acces aim to retire decades of technical debt in 12–18 months.
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chapters (14)
- 0:00Open-source and AI journey
- 6:48AI investment surge
- 12:54Open-weight AI models rise
- 14:432027 AI workloads
- 15:44Measuring AI economic value
- 16:52Agents and open-source AI
- 19:23Open data challenges in AI
- 24:03Future of open-source devs
- 28:20Government restrictions on models
- 32:10Importance of AI harness tech
- 35:21AI cybersecurity vulnerabilities
- 39:57Securing open-source with Accretis
- 43:34Open Secure AI standards
- 44:41Open-source future in AI
