AI House Davos, a multi-day forum held alongside the World Economic Forum in Switzerland, convened startup operators, enterprise executives, researchers, and policymakers to explore how artificial intelligence (AI) is evolving from pilot projects into core business infrastructure. Rather than spotlighting product launches or funding announcements, the agenda focused on the methods of building, deploying, and governing AI across various sectors, including manufacturing, healthcare, sustainability, and governance. This framing set the stage for panels that positioned AI not as a mere tool but as a system-level capability transforming productivity and competitiveness.
Laura Modiano, the Startups EMEA lead at OpenAI, shared insights on LinkedIn following her participation at AI House Davos. She highlighted five key takeaways from a discussion centered on cultivating startups amid the AI era. OpenAI is known for its large-scale AI models and developer platforms that empower entities to create AI-driven products. Modiano joined industry leaders such as Nicole Büttner, Founder and CEO of Merantix Momentum; Alex Ilic, a technology executive; Andrew Ng, Founder of DeepLearning.AI; and Andy Hock, a senior technology executive focused on AI.
Reflecting on the session, Modiano noted that the use of AI tools is expanding beyond technical teams. She stated, “AI tool literacy is essential. The need to use AI tools like Codex and the OpenAI API platform is spreading across every role, not just engineers: product, sales, BD, and ops increasingly need to understand and work with AI directly.” She also posited that merely accelerating development cycles no longer ensures competitive differentiation. “Speed is now table stakes. AI collapsed time-to-value, and defensibility no longer comes from moving faster than humans but from workflow redesign that focuses on outcomes,” she added.
Several of Modiano’s insights underlined the limitations of applying AI to isolated tasks, a theme prevalent throughout the Davos discussions. She asserted, “Transformation ≠ automation. Replacing a single step with AI delivers efficiency, but real impact comes from redesigning entire products and workflows to change outcomes, not just cut costs.” Modiano argued that as AI reduces the cost of development and iteration, the primary constraint shifts to product judgment. “When building becomes cheap, deciding what to build becomes the constraint: taste, user understanding, and point of view matter more than ever,” she explained.
Modiano also framed infrastructure improvements as catalysts for structural change. She remarked, “Faster infrastructure enables new categories. Lower latency and agentic systems don’t just improve existing products; they unlock entirely new experiences and business models.” This perspective highlights the transformative potential of AI beyond mere efficiency enhancements.
In a separate LinkedIn post, Büttner reflected on her panel, “The Great Rewiring: AI, Industry, and the Architecture of Global Resilience,” also at AI House Davos. She noted how the discourse illustrated that AI adoption has progressed beyond mere experimentation. “AI has moved far beyond experimentation. It is no longer an add-on or efficiency tool at the margins. AI is becoming foundational infrastructure, reshaping productivity, competitiveness, and resilience across industries and economies,” Büttner wrote.
The panel included Mallik Rao, Chief Technology and Enterprise Officer at Telefónica Germany, and Pravina Ladva, Group Chief Digital and Technology Officer at Swiss Re. Büttner explained that the conversation emphasized how economic value is increasingly shifting toward organizations that redesign their core processes around AI, rather than merely optimizing existing workflows. “Value is increasingly created by organizations that combine data, models, and infrastructure to fundamentally redesign their core processes rather than simply optimize existing ones,” she stated.
As AI systems integrate into mission-critical environments, Büttner argued that governance and human oversight are becoming essential for scalability. “As AI scales into mission-critical systems, governance, accountability, and human judgment are not constraints on innovation, but essential enablers of responsible scale,” she concluded. This underscores the urgency for businesses to adapt not only their technologies but also their governance frameworks in the era of AI.
See also
Andrew Ng Advocates for Coding Skills Amid AI Evolution in Tech
AI’s Growing Influence in Higher Education: Balancing Innovation and Critical Thinking
AI in English Language Education: 6 Principles for Ethical Use and Human-Centered Solutions
Ghana’s Ministry of Education Launches AI Curriculum, Training 68,000 Teachers by 2025
57% of Special Educators Use AI for IEPs, Raising Legal and Ethical Concerns
