WhyMinds aligns with WEF 2026’s shift from giant models to practical, responsible AI, using small specialized models, ethics-by-design, low-code deployment, and knowledge graphs to deliver scalable, trusted value.
This week at the World Economic Forum in Davos, a powerful consensus emerged: the AI revolution won’t be won by those building the largest models, but by those deploying the most practical, responsible, and economically viable solutions.
India’s IT Minister Ashwini Vaishnaw captured this shift brilliantly when he challenged the IMF’s classification, stating: “ROI doesn’t come from creating a very large model. 95% of the work can happen with models which are 20 billion or 50 billion parameters“. He emphasized that real economic value comes from the application layer—going to enterprises, understanding their business, and providing services using AI applications.
This isn’t just India’s story. It’s the global inflection point we’re witnessing at WEF 2026, where AI has moved from “super system” hype to operational necessity.
At WhyMinds, we’ve been building for exactly this moment. Our four strategic pillars mirror the core themes dominating Davos discussions:
While others chase trillion-parameter models, we’re training domain-expert intelligence in open-source tabletop models optimized for CPUs. Why? Because as WEF discussions confirm, specialized small models can reduce inference costs by 30x while delivering 98-99% of performance. Minister Vaishnaw reinforced this: “If you have a 30 billion parameter model…you don’t even require a GPU”. IBM reports similar findings—frugal AI enables on-device deployment, reduces cloud dependency, and keeps sensitive data local.
This isn’t compromise; it’s competitive advantage. As Gartner predicts, by 2027, enterprises will deploy 3x more specialized small models than generalist LLMs.
At Davos, trustworthy AI emerged as THE foundational requirement for scaling. Microsoft’s Satya Nadella stressed using AI for “useful outcomes that benefit communities,” while Google DeepMind’s Demis Hassabis advocated for international safety standards. The UN’s 2024 resolution on “safe, secure and trustworthy” AI underscores this global priority.
Our proprietary filter layer ensures data privacy and confidentiality at every stage—training, ingesting, and extracting. This “ethics-by-design” approach embeds fairness, privacy, and accountability from the start, precisely what WEF identifies as essential for responsible deployment.
The gap between AI pilots and scaled deployment emerged as a critical challenge at WEF 2026. With $1.5 trillion in AI investment, nearly 60% of companies aim to scale AI in 2025, yet most struggle with the “hard part”—moving beyond experimentation.
Our low-code approach, robust integration layer, and legacy co-existence design address exactly this. Minister Vaishnaw highlighted India’s public-private partnership enabling 38,000 GPUs as common compute—accessible to all students, researchers, and startups at one-third the cost of rich countries. This democratization through distribution is the playbook for real-world impact, not just in India but globally.
While less visible in headlines, knowledge graphs are quietly becoming the structural backbone guiding AI’s probabilistic nature. Facebook, Google, Netflix, and Siemens all rely on knowledge graph technology to organize complex, interrelated data. The relationships between data points are as valuable as the data itself (GraphRAG).
Our home-grown knowledge graphs with context-rich, cross-domain features provide the structural layer directing vector processing—turning AI from black-box predictions into explainable, traceable intelligence that enterprises can trust.
WEF 2026 revealed competing visions for AI, but one theme united them all: practical deployment with measurable ROI, responsible governance, and widespread diffusion. As BlackRock’s Larry Fink noted, AI’s potential impact on inequality is a defining future challenge. The answer isn’t in creating more powerful but inaccessible models—it’s in building systems that work at scale, responsibly, and economically.
India’s strategy—which Vaishnaw described as working across all five AI layers (application, model, chip, infrastructure, energy) with focus on diffusion—provides the blueprint. Saudi Arabia’s Minister of Investment Khalid Al-Falih echoed this: “AI has to be accessible…diffusion is not just within economies but globally”.
At WhyMinds, we’re not waiting for the future—we’re building it. Our strategy isn’t reactive; it’s prescient. While the world debates at Davos, we’re already deploying frugal, responsible, accessible, and knowledge-driven AI solutions that deliver real business value.
The fifth industrial revolution won’t be won by those with the most compute power. It will be won by those who understand that efficiency, responsibility, accessibility, and structural intelligence are the true competitive advantages.